The Federal Highway Administration (FHWA) established the Highway Safety Improvement Program (HSIP) to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.
The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses on results.
Under the enabling transportation legislation enacted in December 2015, Fixing America's Surface Transportation (FAST), New Jersey receives approximately $57 million annually for safety improvements.”
The HSIP requires a statewide strategic highway safety plan to set goals and prioritize safety investments. The HSIP also requires annual progress reporting to the FHWA.
Data source and extract/transform script - https://github.com/dvrpc/planning-data-management/tree/main/gis-hsip
The Federal Highway Administration (FHWA) established the Highway Safety Improvement Program (HSIP) to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.
The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses on results.
Under the enabling transportation legislation enacted in December 2015, Fixing America's Surface Transportation (FAST), New Jersey receives approximately $57 million annually for safety improvements.”
The HSIP requires a statewide strategic highway safety plan to set goals and prioritize safety investments. The HSIP also requires annual progress reporting to the FHWA.
Data source and extract/transform script - https://github.com/dvrpc/planning-data-management/tree/main/gis-hsip
The Federal Highway Administration (FHWA) established the Highway Safety Improvement Program (HSIP) to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.
The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses on results.
Under the enabling transportation legislation enacted in December 2015, Fixing America's Surface Transportation (FAST), New Jersey receives approximately $57 million annually for safety improvements.”
The HSIP requires a statewide strategic highway safety plan to set goals and prioritize safety investments. The HSIP also requires annual progress reporting to the FHWA.
Data source and extract/transform script - https://github.com/dvrpc/planning-data-management/tree/main/gis-hsip
The Regional High Injury Network (RHIN) is the result of a data-driven process designed to show where killed and severe injury (KSI) crashes occurred, and where bicyclists and pedestrian crashes occurred, both using reportable crash data from 2017-2021 (NJ) and 2018-2022 (PA). The RHIN has two layers: the red layer includes segments where 2 or more KSI crashes took place within a half mile, and the blue layer includes segments where 2 or more bicyclist and/or pedestrian crashes of any injury severity occurred, also within a half mile.
The RHIN does not include previously developed high injury networks (e.g.: Philadelphia, Trenton) but the Regional Vision Zero effort recognizes those existing networks. The RHIN excludes limited-access roadways (e.g.: ramp-access highways) because road design changes and investment strategies on such major facilities are beyond the scope of this effort.
The RHIN is a planning tool designed for use in tandem with other data sets to inform roadway safety investigations and for reference in plans and projects. We are publishing the RHIN in advance of the Regional Vision Zero Safety Action Plan release to support local safety planning. If you would like additional background on our methodology, data sources, or other information, please reach out to RVZ@DVRPC.ORG.
For specifics about methodology please visit https://github.com/dvrpc/high-injury-network
https://catalog.dvrpc.org/dvrpc_data_license.html
Focus Limited Access Roadway Bottlenecks contain a road segment on a limited access roadway or approach to a limited access roadway with a high peak hour TTI greater than 1.50 or PTI segment greater than 3.00, and high peak hour vehicle and volume delays. Any trailing adjacent segment with a TTI of 1.40 or more is also included as part of the bottleneck. For each bottleneck, peak travel time vehicle and volume delays are summarized for the immediate bottleneck segment and remaining upstream segments with a TTI of 1.40 or more, or until another bottleneck is encountered.
There are 145 Focus Limited Access Roadway Bottlenecks identified: 102 in the Pennsylvania subregion and 43 in the New Jersey subregion. These bottlenecks are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time vehicle and volume delays.
CMP Focus Limited Access Roadway Bottleneck Database Fields
MAPID – Unique map bottleneck identifier by state
MAPID2 – Unique map bottleneck identifier by DVRPC region; NJ bottlenecks start at identifier 200
NAME – Names of the intersecting expressways and ramps
MUNICIPAL – Municipal where intersection exists; for Philadelphia it is the planning district
COUNTY – County where the intersection exists
AMPKVEDEL – AM Peak Vehicle Delay
PMPKVEDEL – PM Peak Vehicle Delay
HIPKVEDEL – Highest AM or PM Peak Vehicle Delay
TDPKVEDEL – AM or PM Time of Day of Highest Vehicle Delay
PKVEDELRK – Peak Vehicle Delay Rank with lowest rank number the most delay
PKVODELRK – Peak Volume Delay Rank with lowest rank number the most delay
AMPKVODEL – AM Peak Volume Delay in HH:MM:SS
PMPKVODEL – PM Peak Volume Delay in HH:MM:SS
HIPKVODEL – Highest of AM or PM Peak Volume Delay in HH:MM:SS
HIPKVOLDEV – Highest of AM or PM Peak Volume Delay in number format
TDPKVODEL – AM or PM Time of Day of Highest Volume Delay
KEYIDX – Unique identifier link to non-spatial data
KEYID – Unique identifier link to non-spatial data (number format)
STATE – AM or PM Time of Day of Highest Volume Delay
COMMENT – Description of the potential cause of the bottleneck as applicable
LOTTRMAXMI – Miles of travel time unreliable for the measure (1.50 or more)
TTTRMAXMI – Miles of truck travel time unreliability for the measure (2.00 or more)
PHEDVAPMI – Total Peak Hours of Excessive Delay weighted by road miles
MAXVCMI – Miles of Travel Demand Model forecasted congestion V/C greater than or equal to 0.85 in 2050
CRINDEXMI – Miles of high crash rate for the measure
CRINDEXMI – Miles of high crash severity for the measure
IMRMAXPMI – CMP Objective Measure score to increase mobility and reliability and meet PM3 targets weighted by road miles, where the maximum score is 4.0
IMRMAXPR – CMP Objective Measure rank of IMRMAXPMI where lower values represent higher scores
IMIAMAXPMI – CMP Objective Measure score to integrate modes and provide transit where it is most needed weighted by road miles, where the maximum score is 2.0
IMIAMAXPR – CMP Objective Measure rank of IMIAMAXPMI where lower values represent higher scores
MRMAXPMI – CMP Objective Measure score to modernize and maintain the existing transportation network, where the maximum score is 1.5
MRMAXPR – CMP Objective Measure rank of MRMAXPMI where lower values represent higher scores
SVRMAXPMI – CMP Objective Measure score to achieve Vision Zero, where the maximum score is 2.0
SVRMAXPR – CMP Objective Measure rank of SVRMAXPMI where lower values represent higher scores
GCMAXPMI – CMP Objective Measure score to maintain the movement of goods by truck and meet PM3 targets, where the maximum score is 1.5
GCMAXPR – CMP Objective Measure rank of GCMAXPMI where lower values represent higher scores
SPMAXPMI – CMP Objective Measure score to maintain and enhance transportation security and prepare for major events, where the maximum score is 1.0
SPMAXPR – CMP Objective Measure rank of SPMAXPMI where lower values represent higher scores
LRPMAXPMI – CMP Objective Measure score to support LRP centers, infill, redevelopment and emerging growth areas, environmental sensitive areas, and Environmental Justice and Equity populations, where the maximum score is 3.0
LRPMAXPR – CMP Objective Measure rank of LRPMAXPMI where lower values represent higher scores
CMPMAXPMI – Total of of the CMP Objective Measure scores, where the maximum score is 15.0
CMPMAXPR – Total CMP Objective Measure rank of CMPMAXPMI where lower values represent higher scores
https://catalog.dvrpc.org/dvrpc_data_license.html
The CMP Objective Measures GIS layer includes scoring by road segment where congested road segment locations that meet CMP Objective Measure criteria than others contain higher score totals and are given stronger consideration for managing congestion. The final mapping is a composite of all the scores where a maximum score of 15 can be attained, and road segment locations with scores greater than nine are shown in brown.
The CMP Objective Measures include: 1) increase mobility and reliability; 2) integrate modes and increase accessibility; 3) modernize infrastructure; 4) achieve Vision Zero; 5) make global connections; 6) strengthen security and enhance emergency preparedness; and 7) support Long-Range Plan principles.
CMP Objective Measure Database Fields
SegID – INRIX Segment ID
FRC – INRIX facility ID as applicable
Miles – INRIX segment miles
RoadNumber – INRIX Road Highway number
RoadName – INRIX Road name
State - INRIX State
County – INRIX County
Bearing – INRIXD Bearing
SEGID2X – INRIX Segment ID (string)
TTIWKD0708 – INRIX Travel Time Index 7-8 am
TTIWKD0809 – INRIX Travel Time Index 8-9 am
TTIWKD1617 – INRIX Travel Time Index 4-5 pm
TTIWKD1718 – INRIX Travel Time Index 5-6 pm
PTIWKD0708 – INRIX Planning Time Index 7-8 am
PTIWKD0809 – INRIX Planning Time Index 8-9 am
PTIWKD1617 – INRIX Planning Time Index 4-5 pm
PTIWKD1718 – INRIX Planning Time Index 5-6 pm
REFSPDMEAN – Reference or Freeflow speed
FFTIME – Freeflow Time
MAJTMC – Primary INRIX TMC associated with INRIX XD
NHS – Roadway segment on NHS based on NPMRDS database
Valid values:
1 – On NHS
2 – Not on NHS
LOTTRMAX – Roadway segment LOTTR value
TTTRMAX – Roadway segment TTTRMAX value
PHEDVAL – Roadway segment PHEDVAL value from conflated TMC
PHEDVALPMI – Roadway segment PHEDVAL value Per Mile of Roadway from conflated TMC
FROMTONODE – Travel Demand Model From To Node Link
VC7to8am15 – Travel Demand Model V/C 7 am to 8 am 2015
VC8to9am15 – Travel Demand Model V/C 8 am to 9 am 2015
VC4to5pm15 – Travel Demand Model V/C 4 am to 5 pm 2015
VC5to6pm15 – Travel Demand Model V/C 5 am to 6 pm 2015
VCMaxPH15 – Highest of 2015 am and pm V/Cs
VC7to8am50 – Travel Demand Model V/C 7 am to 8 am 2050
VC8to9am50 – Travel Demand Model V/C 8 am to 9 am 2050
VC4to5pm50 – Travel Demand Model V/C 4 am to 5 pm 2050
VC5to6pm50 – Travel Demand Model V/C 5 am to 6 pm 2050
VCMaxPH50 – Highest of 2050 am and pm V/Cs
PCPH7to8AM – Percent Change in V/C from 2015 to 2050 during 7-8am
PCPH8to9AM – Percent Change in V/C from 2015 to 2050 during 8-9am
PCPH4to5PM – Percent Change in V/C from 2015 to 2050 during 4-5pm
PCPH5to6PM – Percent Change in V/C from 2015 to 2050 during 5-6pm
PCMaxPH – Highest of Percent Change V/C
TRANSIT – Travel Demand Model road segments that include surface transit (bus or trolley)
Valid values:
0 – No road segments with transit
1 – Road segments with any transit
2 – Road segments with substantial transit (2 or more in urban; 3 or more suburban)
rmshash – PennDOT road segments county, route and segment identifiers from PennDOTs RMSSEG database
newaadtv2 – Average Annual Daily Traffic (AADT)
newadttv2 – Average Annual Daily Truck Traffic (AADTT)
DVRPCCR_RT – Average crash rate analyzed separately for the PA and NJ portions of DVRPC region based on similar roadway characteristics
SEG_CR_RT – Actual crash rate by roadway segment
CR_INDEX - Crash Rate index comparing actual crash rate with average rate (analyzed separately for the PA and NJ portions of DVRPC region)
FATALRATE – Fatal crash rate based on fatalities per 100,000,000 million vehicle miles traveled
CR_INDEX_C – Crash Rate
Valid values:
0 – Crash rate less than 4 times the state rate
1 – Crash rate 4 or more times the state rate
CRASH_CT – Number of crashes by roadway segment
FATAL_CT – Number of fatal crashes by roadway segment
TOT_INJ_CT – Number of injury crashes by roadway segment
MAJ_INJ_CT – Number of major injury crashes by roadway segment
MOD_INJ_CT – Number of moderate injuries by roadway segment
MIN_INJ_CT – Number of minor injuries by roadway segment
UNK_INJ_CT - Unknown injuries by roadway segment
CRASHPERMI – Crashes per Mile
FATMAJINJC – Fatal and major injuries by roadway segment
FATMIPMILE – Fatalities and Major Injuries Per Mile
FATMAPMICR – Fatalities and Major Injuries Per Mile Crash Index
Valid values:
0 – Less Than 5 or more fatal and major injuries per roadway segment mile
1 – 5 or more fatal and major injuries per roadway segment mile
TTTIMAXVAL – Highest Truck Travel Time Index value for 7-8am, 8-9am, 4-5pm and 5-6pm
FACIDNEW – Focus Roadway Corridor Facility identifier
FACIDDIR – Focus Roadway Corridor Facility direction, which is used to calculate AADT
TTICR - Roadway segment congestion threshold – TTICRP
Valid values:
0 – TTI value Less Than 1.20
1 – TTI value >= 1.20 and <= 1.50 (moderately congested)
2 – TTI > 1.50 (highly congested)
TTIWKDMAX - Highest of INRIX Travel Time Index 7-8am, 8-9am, 4-5pm, 5-6pm
PTIWKDMAX - Highest of INRIX Planning Time Index 7-8am, 8-9am, 4-5pm, 5-6pm
PTICR - Roadway segment reliability threshold - PTICRP
Valid values:
0 – PTI value Less Than 2.00
1 – PTI value >= 2.00 and =< 3.00 (moderately unreliable)
2 – PTI > 3.00 and <= 3.50 (highly unreliable)
3 – PTI > 3.50 (very highly unreliable)
LOTTRCR – Roadway segment reliable threshold
Valid values:
0 – LOTTR < 0 1.50; reliable
1 – LOTTR 1.50 to 2.00; moderately unreliable
2 – LOTTR > 2.00; highly unreliable
TTTRCR – Roadway segment reliable threshold (trucks on interstates only)
Valid values:
0 – TTTR value is 0
1 – TTTRMAX > 0 and Less Than 1.50; reliable
2 – TTTRMAX 1.50 to 2.49; moderately unreliable
3 – TTTRMAX >= 2.50; highly unreliable
PHEDCR – Roadway segment that experiences excessive delay above the regional average (24,355 PHED/Mile) (based on NPMRDS database, not on conflated INRIX database).
Valid values:
0 – Not above regional average
1 – At or above regional average
VC8550PCR – Travel Demand Model Segments with >=15% change from 2015 to 2050
Valid values:
0 – Segments < 15% Change in V/C
1 – Segments >= 15% Change in V/C
VC8550CR – Travel Demand Model Road segments in 2050 with 0.85 or more V/C
Valid values:
0 – Segments < 0.85 V/C
1 – Segments >= 0.85 V/C
TTTICR – Truck Travel Time Index Roadway segment truck congestion threshold
Valid values:
0 – Less Than 2.00
1 – 2.00 to 3.00 (moderately congested)
2 - > 3.00 (highly congested)
SETRANSTCR – Transit Score Priority
Valid values:
0 – Not Priority
1 – Priority
CRSSRIID – NJ Crash Identifier SRI
popdenmnx2 – Road segments that intersect CBGs with population more than 2x the regional average
Valid values:
0 –No
1 – Yes
empdenmnx2 – Road segments that intersect CBGs with employment more than 2x the regional average
Valid values:
0 –No
1 – Yes
railsta1mi – Road segments that intersect 1 mile buffer of rail stations
Valid values:
0 –No
1 – Yes
trnstqtrmi – Road segments that intersect 1/4 mile buffer of bus transit routes
Valid values:
0 –No
1 – Yes
raillin1mi – Road segments that intersect 1 mile buffer of passenger rail
Valid values:
0 –No
1 – Yes
frtrail1mi – Road segments that intersect 1 mile buffer of freight rail
Valid values:
0 –No
1 – Yes
frtcntr1mi - Road segments that intersect freight centers
Valid values:
0 –No
1 – Yes
trnstscor - Road segments that intersect CBG with very and high composite (pop, emp and 0 veh households)
Valid values:
0 –No
1 – Yes
TPTIMAXVAL – Highest Truck Planning Time Index value for 7-8am, 8-9am, 4-5pm and 5-6pm.
TPTICR – Truck Planning Time Index Roadway segment truck reliability threshold
Valid values:
0 – Less Than 2.00
1 – 5.50 to 6.50 (moderately unreliable)
2 - > 6.50 (highly unreliable)
HVTRST1mi – Heavily used transit stations: 3 in Philadelphia and 1 in each of the other PA counties; road segments that intersect within 1-mile buffer
Valid values:
0 –No
1 – Yes
NUCPLT1MI – Road segments within 10 mile buffer of Limerick Nuclear Power Plant
Valid values:
0 –No
1 – Yes
RDBRGR1MI – Road segments within 1 mile buffer of major road bridges carrying > 100,000 AADT
Valid values:
0 –No
1 – Yes
FRBRGR1MI – Road segments within 1 mile buffer of major freight bridges
Valid values:
0 –No
1 – Yes
RLBRGR1MI – Road segments within 1 mile buffer of major passenger rail bridges
Valid values:
0 –No
1 – Yes
MIL1MI – Road segments within 1 mile buffer of major military sites
Valid values:
0 –No
1 – Yes
STDWTF1MI – Road segments within 1 mile buffer of major military sites
Valid values:
0 –No
1 – Yes
lucntr – Road segments that intersect 2050 land use centers
Valid values:
0 –No
1 – Yes
IREGAreas – Road segments that intersect Infill, Redevelopment and Emergency Growth Areas
Valid values:
0 –No
1 – Yes
ENSCAreas – Road segments where 90% or more of segment score low in the Environmental Screening Tool (or not environmental sensitive areas)
Valid values:
0 –No
1 – Yes
FLD100500 – Road segments that intersect 100-year and 500-year floodplain
Valid values:
0 –No
1 – Yes
IPDEJLI – Road segments that intersect IPD CBG’s with well above average or above average low income populations
Valid values:
0 –No
1 – Yes
IPDEJRM – Road segments that intersect IPD CBG’s with well above average or above average racial minority populations
Valid values:
0 –No
1 – Yes
TTICRP – Travel Time Index criteria Points
PTICRP – Planning Time Index criteria Points
PHEDCRP – PHED criteria Points
VC8550CRP – high anticipated V/C criteria Points
VC8550PCRP – high anticipated growth in V/C criteria Points
LOTTRCRP – LOTTR criteria Points
IMRMAXP – increase mobility and reliability maximum Points
trnstscorP – transit score criteria Points
railsta1mP – near passenger rail stations (1 mile) criteria Points
trnstqtrmP - bus transit quarter mile criteria Points
IMIAMXP – Integrate modes and increase accessibility maximum Points
TRANSITP - substantial bus transit from travel demand model criteria Points
raillin1mP – near transit passenger rail (1 mile) criteria Points
frtcntr1miP – near freight centers (1 mile) criteria Points
frtrail1miP – near freight rail facilities (1 mile) criteria Points
NHSP – national highway system criteria Points
MRMAXP – Modernize Infrastructure maximum Points
CRINDEXCRP – crash rates criteria Points
FATMAPMICP - fatal and major injury criteria Points
SVRMAXP – safety maximum Points
TTTICRP – Truck travel time index criteria Points
TPTICRP – Truck planning time index criteria Points
TTTRCRP – PM3 truck reliability criteria Points
GCMAXP – Global Connections maximum Points
popdenmnxP - population density criteria Points
empdenmnxP - employment density criteria Points
HVTRST1milP – heavily used transit stations (1 mile) criteria Points
NUCPLT1MIP – Limerick nuclear power plant evacuation zone (3 miles) criteria Points
RDBRGR1MIP – Major roadway bridges (1 mile) criteria Points
FRBGR1MIP – Major freight rail bridges (1 mile) criteria Points
RLBRGR1MIP - Major passenger rail bridges (1 mile) criteria Points
MIL1MIP – Military locations (1 mile) criteria Points
STDWTF1MIP – stadium and waterfront attraction locations (1 mile) criteria Points
SPMAXP – Strengthen cybersecurity maximum Points
lucntrP – land use center points
IREGAreasP – Infill and redevelopment areas, and emerging growth areas criteria Points
ENSCAreasP – Environmental Screening Tool criteria Points
FLD100500P – 100- and 500-year floodplains criteria Points
IPDEJLIP – IPD low income criteria Points
IPDEJRMP – IPD racial minority criteria Points
LRPMAXP – Long Range Plan maximum Points
CMPMAXP – Maximum CMP Objective Score points (add all maximums together)
MAXCRCNT – Count of CMP Objectives met
CMPMAXPMI - Maximum CMP Objective Score points (CMPMAXP above) divided by segment length
PHEDVAPMI2 – Roadway segment PHEDVAL (from INRIX TMC) value multiplied by roadway miles (from INRIX XD)
NEXTSEGID – Next directional INRIX Segment ID (SegID)
PREVSEGID – Previous directional INRIX Segment ID (SegID)
https://catalog.dvrpc.org/dvrpc_data_license.html
Focus Intersection Bottlenecks occur along arterials and other non-controlled access roadway facilities, typically at signalized intersections. Focus intersection bottlenecks are identified in the region as ones that have at least one roadway segment approach to an intersection with a peak hour TTI greater than 1.50 or a PTI greater than 3.00 and high peak hour vehicle and volume delays. Intersections with more than one segment approach with high peak hour delays were given added weight to be included as a focus intersection bottleneck.For each bottleneck, peak travel time vehicle and volume delays are summarized for all approach segments that touch the intersection and any other trailing adjacent segments with a TTI of 1.40 or more, or until another bottleneck is encountered.
A total of 299 Focus Intersection Bottlenecks were identified in the DVRPC region: 181 in Pennsylvania and 118 in New Jersey.
These bottlenecks are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time vehicle and volume delays.
CMP Focus Intersection Bottleneck Database Fields
MAPID – Unique map bottleneck identifier by state
MAPID2 – Unique map bottleneck identifier by DVRPC region; NJ bottlenecks start at identifier 300
NAME – Names of the intersecting streets
MUNICIPAL – Municipal where intersection exists; for Philadelphia it is the planning district
COUNTY – County where the intersection exists
AMPKVEDEL – AM Peak Vehicle Delay
PMPKVEDEL – PM Peak Vehicle Delay
HIPKVEDEL – Highest AM or PM Peak Vehicle Delay
TDPKVEDEL – AM or PM Time of Day of Highest Vehicle Delay
PKVEDELRK – Peak Vehicle Delay Rank with lowest rank number the most delay
PKVODELRK – Peak Volume Delay Rank with lowest rank number the most delay
AMPKVODEL – AM Peak Volume Delay in HH:MM:SS
PMPKVODEL – PM Peak Volume Delay in HH:MM:SS
HIPKVODEL – Highest of AM or PM Peak Volume Delay in HH:MM:SS
HIPKVOLDEV – Highest of AM or PM Peak Volume Delay in number format
TDPKVODEL – AM or PM Time of Day of Highest Volume Delay
RELATEID – Unique identifier link to non-spatial data
RELATEIDN – Unique identifier link to non-spatial data (number format)
STATE – AM or PM Time of Day of Highest Volume Delay
LOTTRMAXMI – Miles of travel time unreliable for the measure (1.50 or more)
PHEDVAPMI – Total Peak Hours of Excessive Delay weighted by road miles
MAXVCMI – Miles of Travel Demand Model forecasted congestion V/C greater than or equal to 0.85 in 2050
CRINDEXMI – Miles of high crash rate for the measure
FATMIMI – Miles of high crash severity (fatalities and major injuries) for the measure
IMRMAXPMI – CMP Objective Measure score to increase mobility and reliability and meet PM3 targets weighted by road miles, where the maximum score is 4.0
IMRMAXPR – CMP Objective Measure rank of IMRMAXPMI where lower values represent higher scores
IMIAMAXPMI – CMP Objective Measure score to integrate modes and provide transit where it is most needed weighted by road miles, where the maximum score is 2.0
IMIAMAXPR – CMP Objective Measure rank of IMIAMAXPMI where lower values represent higher scores
MRMAXPMI – CMP Objective Measure score to modernize and maintain the existing transportation network, where the maximum score is 1.5
MRMAXPR – CMP Objective Measure rank of MRMAXPMI where lower values represent higher scores
SVRMAXPMI – CMP Objective Measure score to achieve Vision Zero, where the maximum score is 2.0
SVRMAXPR – CMP Objective Measure rank of SVRMAXPMI where lower values represent higher scores
GCMAXPMI – CMP Objective Measure score to maintain the movement of goods by truck and meet PM3 targets, where the maximum score is 1.5
GCMAXPR – CMP Objective Measure rank of GCMAXPMI where lower values represent higher scores
SPMAXPMI – CMP Objective Measure score to maintain and enhance transportation security and prepare for major events, where the maximum score is 1.0
SPMAXPR – CMP Objective Measure rank of SPMAXPMI where lower values represent higher scores
LRPMAXPMI – CMP Objective Measure score to support LRP centers, infill, redevelopment and emerging growth areas, environmental sensitive areas, and Environmental Justice and Equity populations, where the maximum score is 3.0
LRPMAXPR – CMP Objective Measure rank of LRPMAXPMI where lower values represent higher scores
CMPMAXPMI – Total of of the CMP Objective Measure scores, where the maximum score is 15.0
CMPMAXPR – Total CMP Objective Measure rank of CMPMAXPMI where lower values represent higher scores
https://catalog.dvrpc.org/dvrpc_data_license.html
This dataset documents DVRPC’s analysis of freight truck movements and major freight centers to define a hierarchy of roads that make up the region’s freight-intensive truck network. The identified network highlights key roadways (Limited Access, Intermodal Connectors, and Primary Routes) that are regularly used for goods movement and serves as a planning tool to guide outreach, stakeholder discussions, and decision-making related to freight mobility, regional collaboration, and freight education. The network was finalized in 2025 and covers the entire 9-County DVRPC region.
Methodology: https://www.dvrpc.org/products/25152
https://catalog.dvrpc.org/dvrpc_data_license.html
The National Highway Freight Network is composed of the Primary Highway Freight System (PHFS), other Interstate portions, Critical Rural Freight Corridors (CRFCs), and Critical Urban Freight Corridors (CUFCs). The PHFS was set by the FAST Act. CRFCs and CUFCs have yet to be designated.
Public domain
Includes highway and rail river crossing inventory for the DVRPC region.
The DVRPC Highway River Crossing data layer was developed to identify key bridges in the region that span major rivers and can be considered crucial to interstate commerce. These bridge were mapped as point features identifying the center point location of the facility. Additional inputs were on the facilities was based on Department of Transportation, Turnpike Commission and Bridge Commission data.
The DVRPC Railroad River Crossing data layer was developed to identify key bridges in the region that span major rivers and can be considered crucial to interstate commerce. These bridge were mapped as point features identifying the center point location of the facility. Additional inputs were on the facilities was based on Department of Transportation and rail carrier data.
https://catalog.dvrpc.org/dvrpc_data_license.html
Based on the NDC waterway link model. Limited to DVRPC facilities and modified to reflect local conditions.
Public domain
Truck Parking facilities are essential to the freight system to accommodate overnight parking to support long haul truck movement in, out and through the region. In the DVRPC region there three types of truck parking facilities: private truck stops, service plazas, and rest areas. This data layer was assembled by DVRPC utilizing directory listing for these facilities and related property boundaries. Additional data was acquired through on site surveys of facilities.
https://catalog.dvrpc.org/dvrpc_data_license.html
Centerline of navigable channel. Based on NOAA navigation charts.
Public domain
The DVRPC port terminals data set represents the landside and pier data for active commercial marine terminals in the Delaware Valley. The data shows piers consolidated by operator along with the landside terminal facilities. Data varies by bulk and general cargo facility. Data was compiled by DVRPC using port terminal marketing materials and outreach with terminal operators.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC Freight Rail Yards data layer was developed to provide information about key interchange, sorting, and storage yards in the region. The yard data was created utilizing a combination of outreach to rail operators and DVRPC mapping.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC navigable river layer is a polygon layer representing the extents of the Delaware and Schulykill rivers that are designated for commercial traffic. Each waterway is segmented by the appropriate channel and reaches division. NOAA data was utilized to populate the segments with related navigation data.
Public domain
The pipelines shapefile was digitized from Ai graphic files, where individual lines were traced from PDFs of individual county maps provided by the National Pipeline Mapping System(NPMS). Due to the sensitive nature of this data, the maps that were made available through NPMS are not precisely accurate and are intended for use at a county or regional scale.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC Class I Grade Crossing data set is an extraction of the U.S. DOT National Highway-Rail Crossing Inventory with details related to the regional freight system. This data set includes only those grade crossings that exist on Class I mainlines in the Delaware Valley.
https://catalog.dvrpc.org/dvrpc_data_license.html
National Highway System (NHS) connectors are roads, which may be either state or locally owned, which connect intermodal facilities to major highways on the National Highway System. The NHS Connectors in this data layer are officially designated NHS freight connectors that serve any of three types of facilities: intermodal rail facility, port, and/or airport. These routes were digitized based on DVRPC's street centerline data set to correspond with the officially designated NHS Connector routes.
https://catalog.dvrpc.org/dvrpc_data_license.html
Maintained navigation channels for the DVRPC region. Based on NOAA navigation charts data.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC Freight Highways data layer was based on DVRPC's major routes dataset representing major highways in the region. The dataset uses the centerlines of undivided highway segments and ramps to depict each of the major freight serving highways as identified by DVRPC staff. The highways are limited to regional serving routes including interstates and limited-access highways. This dataset is mapped by route name number and is segmented at key interchanges and by several characteristics: road profile, V/C ratio and performance indicators.
https://catalog.dvrpc.org/dvrpc_data_license.html
Intermodal rail terminals provide key connections between the nation freight rail network and local shippers and receivers. The DVRPC intermodal rail terminals data set represents four types of intermodal terminals: trans-flow, transload, auto-intermodal and intermodal container.
https://catalog.dvrpc.org/dvrpc_data_license.html
Heliports in the DVRPC Region
https://catalog.dvrpc.org/dvrpc_data_license.html
Freight as a good neighbor, is an initiative to highlight cooperation across private, public and non-profit interests to reduce negative externalities related to freight activity. These features respresent examples from the DVRPC region.
https://catalog.dvrpc.org/dvrpc_data_license.html
A freight center is an area identified by DVRPC as having a concentration of facilities that generate significant amounts of freight as well as employment in related sectors. Each center is assigned one of five descriptive typologies based on their physical form and the types of businesses located there. The typologies are: Distribution and Logistics, Heavy Industrial, High Tech Manufacturing, International Gateway, and Local Manufacturing and Distribution. The most recent update was completed in 2025.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC Freight Rail Line data layer was developed to identify key ownership and operation details for freight rail lines in the Delaware Valley. This data set was developed internally using various rail mapping and track chart sources. The polyline network represents rights-of-way rather than track and is segmented based on subdivision or track name as well as key profile details. The line set has been developed with input from various Class I and short-line railroads in the region.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC airports data set includes the property areas of all commercial and reliever airports in the Delaware Valley. Annual operations are extracted from FAA records or based upon the DVRPC counting program.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC anchorages data set is a polygon data file showing navigation anchorages in the DVRPC region. Geography from NOAA Navigation Charts.
Public domain
Urban areas within Pennsylvania based on U.S Census Bureau 2020 urban area boundaries. This layer was developed in conjunction with the state's Regional Planning Organizations/Municipal Planning Organizations for Functional Class Highway determination.
The user shall indemnify, save harmless, and, if requested, defend the COMMONWEALTH, their officers, agents, and employees from and against any suits, claims, or actions for injury, death, or property damage arising out of the use of or any defect in the FILES or any accompanying documentation. The COMMONWEALTH excludes any and all implied warranties, including warranties or merchantability and fitness for a particular purpose. The COMMONWEALTH makes no warranty or representation, either express or implied, with respect to the FILES or accompanying documentation, including its quality, performance, merchantability, or fitness for a particular purpose. The FILES and documentation are provided \"as is\" and the USER assumes the entire risk as to its quality and performance. The COMMONWEALTH will not be liable for any direct, indirect, special, incidental, or consequential damages arising out of the use or inability to use the FILES or any accompanying documentation. The USER is granted permission to translate and add value to the FILES for the use of the FILES on its computer hardware; provided, however, that the USER annually notify the UNIVERSITY / COMMONWEALTH of any customizing or value-adding work done. Any customized or value added versions of the files will contain the following disclaimer: THIS IS NOT A PENNSYLVANIA DEPARTMENT OF TRANSPORTATION APPROVED FILE. THE DEPARTMENT OF TRANSPORTATION RETAINS THE MASTER FILES. THE USER AGREES AND UNDERSTANDS THAT IT MAY NOT FURTHER DISTRIBUTE THE FILES TO A THIRD PARTY. If you have any questions or problems, contact the ORGANIZATION where you acquired the data.
NJDOT has revised the New Jersey urban area based upon the 2020 U.S. Census urban area boundaries. The U.S. Census defines an Urbanized Area as any area with a population >= 5,000. Under the 2020 Urban Area definition, Urban Clusters are no longer a classification. FHWA, however, has slightly different criteria for what defines an urban area. Under FHWA, an Urban Area is >= 5,000, with Small Urban Areas 5,000-49,999 and Urbanized Areas >= 50,000. NJDOT followed the FHWA urban area definitions for this urban area update. To perform this update, NJDOT used the 2020 US Census urban areas greater than 5,000 in population. Since census urban area boundaries are based upon census block boundaries, which can be irregular, NJDOT extended outward the urban area (\"smoothed\") to the nearest road, stream, political boundary, or manmade feature. When a roadway is used as the adjusted boundary, the following buffers will be applied to include the right of way of the roadway: 50’ from undivided roadway centerlines (single centerline) and 80’ from divided roadway centerlines (dual centerline). Where there was no obvious boundary to smooth to, the census boundary was retained. NJDOT also expanded the urban area to include any densely developed areas not included in the 2020 census urban areas. The urban area update underwent a thorough public review and comment period. Representatives from NJDOT and the 3 metropolitan planning organizations (NJTPA, SJTPO, and DVRPC) met during various phases of the project to review the updated urban area. All comments were logged into an Urban Area Comment Tracking Form, and an official NJDOT response was provided for each comment. Further revisions were made to the urban area based upon comments from FHWA. These revisions were limited in scope and consisted of the following: 1) Smoothed the urban boundary outward at water boundaries: 1000’ from corporate boundary / shoreline for coastal areas and 500’ from corporate boundary / shoreline for bay areas. 2) Utilize Census State Boundary for the state boundary except for coastal boundaries.
Contact: Eric Oberle, Bureau of Transportation Data and Support, New Jersey Department of Transportation, P.O. Box 600, Trenton, NJ 08625, 609-963-1881, eric.oberle@dot.nj.gov
State boundaries for Pennsylvania, New Jersey, Delaware, Maryland, New York, Connecticut, and the District of Columbia. Original Sources: PA - PennDOT NJ - NJOIT DE - DE Geological Survey MD, NY, CT, DC - ESRI Linework has been adjusted using the following methodology. NJ's boundaries overrode PA, since NJ's were highly accurate. PA overrode MD since PennDOT's were more accurate than ESRI's, and DE overrode PA & MD, since the \"arc\" of northernmost DE was a bit more accurate than PennDOT's or our original county boundaries. State boundaries in the DVRPC region have been adjusted to align with parcel data (where appropriate).
https://catalog.dvrpc.org/dvrpc_data_license.html
*** Downloaded from Pennsylvania Spatial Data Access (PASDA) on 9/26/2022 ***
State senate boundaries within Pennsylvania attributed with names of legislators and party affiliations.
The user shall indemnify, save harmless, and, if requested, defend the COMMONWEALTH, their officers, agents, and employees from and against any suits, claims, or actions for injury, death, or property damage arising out of the use of or any defect in the FILES or any accompanying documentation. The COMMONWEALTH excludes any and all implied warranties, including warranties or merchantability and fitness for a particular purpose. The COMMONWEALTH makes no warranty or representation, either express or implied, with respect to the FILES or accompanying documentation, including its quality, performance, merchantability, or fitness for a particular purpose. The FILES and documentation are provided \"as is\" and the USER assumes the entire risk as to its quality and performance. The COMMONWEALTH will not be liable for any direct, indirect, special, incidental, or consequential damages arising out of the use or inability to use the FILES or any accompanying documentation. The USER is granted permission to translate and add value to the FILES for the use of the FILES on its computer hardware; provided, however, that the USER annually notify the UNIVERSITY / COMMONWEALTH of any customizing or value-adding work done. Any customized or value added versions of the files will contain the following disclaimer: THIS IS NOT A PENNSYLVANIA DEPARTMENT OF TRANSPORTATION APPROVED FILE. THE DEPARTMENT OF TRANSPORTATION RETAINS THE MASTER FILES. THE USER AGREES AND UNDERSTANDS THAT IT MAY NOT FURTHER DISTRIBUTE THE FILES TO A THIRD PARTY. If you have any questions or problems, contact the ORGANIZATION where you acquired the data.
State boundaries for Pennsylvania, New Jersey, Delaware, Maryland, New York, Connecticut, and Washington DC. This dataset was compiled from multiple sources. Original Sources: PA - PennDOT NJ - NJOIT DE - DE Geological Survey MD, NY, CT, DC - ESRI Linework has been adjusted using the following methodology. NJ's boundaries overrode PA, since NJ's were highly accurate. PA overrode MD since PennDOT's were more accurate than ESRI's, and DE overrode PA & MD, since the \"arc\" of northernmost DE was a bit more accurate than PennDOT's or our original county boundaries. Boundaries in the DVRPC region have been adjusted to align with parcel data (where appropriate). This file is the result of a dissolve performed on the county boundary file.
https://catalog.dvrpc.org/dvrpc_data_license.html
*** Downloaded from Pennsylvania Spatial Data Access (PASDA) on 9/26/2022 ***
State House boundaries within Pennsylvania attributed with names of legislators and party affiliations.
The user shall indemnify, save harmless, and, if requested, defend the COMMONWEALTH, their officers, agents, and employees from and against any suits, claims, or actions for injury, death, or property damage arising out of the use of or any defect in the FILES or any accompanying documentation. The COMMONWEALTH excludes any and all implied warranties, including warranties or merchantability and fitness for a particular purpose. The COMMONWEALTH makes no warranty or representation, either express or implied, with respect to the FILES or accompanying documentation, including its quality, performance, merchantability, or fitness for a particular purpose. The FILES and documentation are provided \"as is\" and the USER assumes the entire risk as to its quality and performance. The COMMONWEALTH will not be liable for any direct, indirect, special, incidental, or consequential damages arising out of the use or inability to use the FILES or any accompanying documentation. The USER is granted permission to translate and add value to the FILES for the use of the FILES on its computer hardware; provided, however, that the USER annually notify the UNIVERSITY / COMMONWEALTH of any customizing or value-adding work done. Any customized or value added versions of the files will contain the following disclaimer: THIS IS NOT A PENNSYLVANIA DEPARTMENT OF TRANSPORTATION APPROVED FILE. THE DEPARTMENT OF TRANSPORTATION RETAINS THE MASTER FILES. THE USER AGREES AND UNDERSTANDS THAT IT MAY NOT FURTHER DISTRIBUTE THE FILES TO A THIRD PARTY. If you have any questions or problems, contact the ORGANIZATION where you acquired the data.
*** Downloaded from the Pennsylvania Spatial Data Access (PASDA) on 5/16/2024 ***
United States Congressional legislative boundaries within Pennsylvania as set forth in Act 34 of 2002. This layer was digitized by PADOT from maps generated by the Reapportionment Commission for the Bureau of Commissions, Elections and Legislation Pennsylvania Dept. of State. These boundaries were used as the legislative district boundaries for the November 2002 general election and are currently being contested in the judicial system. The current layers are these same district boundary linework attributed with the current legislator's name, party affiliation and home county.
The user shall indemnify, save harmless, and, if requested, defend the COMMONWEALTH, their officers, agents, and employees from and against any suits, claims, or actions for injury, death, or property damage arising out of the use of or any defect in the FILES or any accompanying documentation. The COMMONWEALTH excludes any and all implied warranties, including warranties or merchantability and fitness for a particular purpose. The COMMONWEALTH makes no warranty or representation, either express or implied, with respect to the FILES or accompanying documentation, including its quality, performance, merchantability, or fitness for a particular purpose. The FILES and documentation are provided \"as is\" and the USER assumes the entire risk as to its quality and performance. The COMMONWEALTH will not be liable for any direct, indirect, special, incidental, or consequential damages arising out of the use or inability to use the FILES or any accompanying documentation. The USER is granted permission to translate and add value to the FILES for the use of the FILES on its computer hardware; provided, however, that the USER annually notify the UNIVERSITY / COMMONWEALTH of any customizing or value-adding work done. Any customized or value added versions of the files will contain the following disclaimer: THIS IS NOT A PENNSYLVANIA DEPARTMENT OF TRANSPORTATION APPROVED FILE. THE DEPARTMENT OF TRANSPORTATION RETAINS THE MASTER FILES. THE USER AGREES AND UNDERSTANDS THAT IT MAY NOT FURTHER DISTRIBUTE THE FILES TO A THIRD PARTY. If you have any questions or problems, contact the ORGANIZATION where you acquired the data.
*** Downloaded from the New Jersey Geographic Information Network (NJGIN) Open Data Portal on 5/16/2024 ***
This polygon layer delineates the US Congressional District boundaries in New Jersey, 2022 - 2032. For this redistricting, the apportionment is the same as for the previous, 2012 redistricting, resulting in the same number of Congressional Districts; however the boundaries have changed significantly.
This version of the layer uses high-quality base map spatial data published by the State of New Jersey, instead of U.S. Census Bureau geographic data (TIGER.) Where no higher-quality data were available, the TIGER lines were used.
This layer, which overlays correctly the highest quality municipal boundary data for New Jersey is an unofficial version; by law, the official version is based on U.S. Census data (geographic and demographic.)
The data layer is not survey data and must not be used as survey data. See the balance of this metadata record for conditions that limit appropriate uses. Users are urged to look at this documentation to assess the fitness of the data for any planned use.
Acknowledgement of the provider, NJ Office of Information Technology, Office of GIS (NJOGIS), is requested for maps, data or other products derived from these data.
The State of New Jersey makes great effort to provide secure, accurate, and complete data and metadata. However, portions of the data and metadata may be incorrect or not current. Any errors or omissions should be reported for investigation.
The State of New Jersey, its officers, employees or agents shall not be liable for damages or losses of any kind arising out of or in connection with the use or performance of data and metadata, including but not limited to, damages or losses caused by reliance upon the accuracy or timeliness of any such data and metadata, or damages incurred from the viewing, distributing, or copying of those materials.
The data and metadata are provided \"as is.\" No warranty of any kind, implied, expressed, or statutory, including but not limited to the warranties of non-infringement of third party rights, title, merchantability, fitness for a particular purpose, and freedom from computer virus, is given with respect to the data and metadata, or its hyperlinks to other Internet resources.
The State disclaims any duty or obligation either to maintain availability of or to update the data and metadata.
All web site data distribution by NJOGIS is, in addition, subject to the Conditions of Use for State of New Jersey executive branch web sites, listed on the State’s Legal Statement & Disclaimers page https://www.nj.gov/nj/legal.html .
*** Downloaded from the New Jersey Geographic Information Network (NJGIN) Open Data Portal on 9/26/2022 ***
The Legislative District polygon spatial layer delineates the legislative districts for New Jersey, beginning with the elections of 2023; the individuals elected will take office in January, 2024. The timing of this New Jersey redistricting was delayed due to the COVID19 pandemic. The spatial data are based mainly on the NJ Municipal Boundaries layer Govt_admin_municipal_bnd (fka nj_munis), rather than Census Bureau geographic data (TIGER.) Information on officeholders is derived from the legislative roster provided by the NJ Office of Legislative Services. It is updated infrequently and should be checked from an independent source before use. ***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.
The data layer is not survey data and must not be used as survey data. See the balance of this metadata record for conditions that limit appropriate uses. Users are urged to look at this documentation to assess the fitness of the data for any planned use. Acknowledgement of the provider, NJ Office of Information Technology, Office of GIS (NJOGIS), is requested for maps, data or other products derived from these data. The State of New Jersey makes great effort to provide secure, accurate, and complete data and metadata. However, portions of the data and metadata may be incorrect or not current. Any errors or omissions should be reported for investigation. The State of New Jersey, its officers, employees or agents shall not be liable for damages or losses of any kind arising out of or in connection with the use or performance of data and metadata, including but not limited to, damages or losses caused by reliance upon the accuracy or timeliness of any such data and metadata, or damages incurred from the viewing, distributing, or copying of those materials. The data and metadata are provided \"as is.\" No warranty of any kind, implied, expressed, or statutory, including but not limited to the warranties of non-infringement of third party rights, title, merchantability, fitness for a particular purpose, and freedom from computer virus, is given with respect to the data and metadata, or its hyperlinks to other Internet resources. The State disclaims any duty or obligation either to maintain availability of or to update the data and metadata. All web site data distribution by NJOGIS is, in addition, subject to the Conditions of Use for State of New Jersey executive branch web sites, listed on the State’s Legal Statement & Disclaimers page https://www.nj.gov/nj/legal.html .
Municipal boundaries for Pennsylvania, New Jersey, Maryland, Delaware, and the District of Columbia with ortho-rectified municipal boundaries for the DVRPC Region. Municipal Boundaries have been edited by DVRPC to create a more accurate, seemless regional dataset for the DVRPC region and its surrounding area. Original data sources (in order of drawing rank): DVRPC - for Mercer, Burlington, Camden, Gloucester counties in NJ (an adaptation of NJOIT's Mun Bounds) and Bucks, Montgomery, Philadelphia, Chester, and Delaware counties in PA. In most cases, boundaries have been adjusted to align with parcel data. NJOIT - remaining NJ counties (2008) PennDOT - remaining PA counties (2009) DE State Office of Mgmt & Budget - Delaware municipalities (2002) MD State Hwy Administration - Maryland municipalities (2003) **NOTE: NJ's coast has been clipped or unioned to ESRI's data CD detailed state boundary**
https://catalog.dvrpc.org/dvrpc_data_license.html
Municipal boundaries for DVRPC Region with Philadelphia's County Planning Areas. Municipal Boundaries have been edited by DVRPC to create a more accurate, seemless regional dataset for the DVRPC region and its surrounding area. This dataset was compiled from multiple sources. Original data sources (in order of drawing rank): DVRPC - for Mercer, Burlington, Camden, Gloucester counties in NJ (an adaptation of NJOIT's Mun Bounds) and Bucks, Montgomery, Philadelphia, Chester, and Delaware counties in PA. In most cases, boundaries have been adjusted to align with parcel data. ***Due to the name change from Washington Twp to Robbinsville Twp in Mercer County, the unique ID (GEOID) for this municipality has changed from 3402177210 to 3402163850 . However, if you download 2000 census data from the Bureau's website, the unique ID (GEOID) for Robbinsville Township will still be 3402177210 in the census data…you will have to manually change its GEOID to 3402163850 in order to get the 2000 data to join to the MCD file correctly.*** Edited in April 2021 to match PCPC's planning district boundaries and numbers dated 2013
https://catalog.dvrpc.org/dvrpc_data_license.html
Municipal boundaries for Pennsylvania, New Jersey, Maryland, Delaware, and the District of Columbia with ortho-rectified municipal boundaries for the DVRPC Region. Municipal Boundaries have been edited by DVRPC to create a more accurate, seemless regional dataset for the DVRPC region and its surrounding area. This dataset was compiled from multiple sources. Original data sources (in order of drawing rank): DVRPC - for Mercer, Burlington, Camden, Gloucester counties in NJ (an adaptation of NJOIT's Mun Bounds) and Bucks, Montgomery, Philadelphia, Chester, and Delaware counties in PA. In most cases, boundaries have been adjusted to align with parcel data. NJOIT - remaining NJ counties (2008) PennDOT - remaining PA counties (2009) DE State Office of Mgmt & Budget - Delaware municipalities (2002) MD State Hwy Administration - Maryland municipalities (2003) Washington DC - ESRI **NOTE: NJ's coast has been clipped or unioned to ESRI's data CD detailed state boundary**
Due to a consolidation, Princeton Twp and Princeton Boro in Mercer County, NJ in 2013, the new municipality (named \"Princeton\"), uses Princeton Boro's unique ID 3402160900. Princeton Township's unique ID (3402160915)no longer exists.
Due to the name change from Washington Twp to Robbinsville Twp in Mercer County NJ in 2008, the unique ID (GEOID) for this municipality has changed from 3402177210 to 3402163850 . However, if you download 2000 census data from the Bureau's website, the unique ID (GEOID) for Robbinsville Township will still be 3402177210 in the census data…you will have to manually change its GEOID to 3402163850 in order to get the 2000 data to join to the MCD file correctly.
Due to name change from Birmingham Twp to Chadds Ford Twp in Delaware County, PA in 1997, the unique ID (GEOID) for this municipality has changed from 4204506552 to 4204512442.
https://catalog.dvrpc.org/dvrpc_data_license.html
County boundaries (linear features) for Pennsylvania, New Jersey, Delaware, Maryland, New York, Connecticut, and the District of Columbia. Original Sources: PA - PennDOT NJ - NJOIT DE - DE Geological Survey MD, NY, CT, DC - ESRI Linework has been adjusted using the following methodology. NJ's boundaries overrode PA, since NJ's were highly accurate. PA overrode MD since PennDOT's were more accurate than ESRI's, and DE overrode PA & MD, since the \"arc\" of northernmost DE was a bit more accurate than PennDOT's or our original county boundaries. County boundaries in the DVRPC region have been adjusted to align with parcel data (where appropriate). NOTE: Use the following definition query if only need one county in DVRPC Region \"Co_Name\" LIKE '%Bucks%'
https://catalog.dvrpc.org/dvrpc_data_license.html
Municipal boundaries for DVRPC Region with Philadelphia's County Planning Areas. Municipal Boundaries have been edited by DVRPC to create a more accurate, seemless regional dataset for the DVRPC region and its surrounding area. This dataset was compiled from multiple sources. Original data sources (in order of drawing rank): DVRPC - for Mercer, Burlington, Camden, Gloucester counties in NJ (an adaptation of NJOIT's Mun Bounds) and Bucks, Montgomery, Philadelphia, Chester, and Delaware counties in PA. In most cases, boundaries have been adjusted to align with parcel data. ***Due to the name change from Washington Twp to Robbinsville Twp in Mercer County, the unique ID (GEOID) for this municipality has changed from 3402177210 to 3402163850 . However, if you download 2000 census data from the Bureau's website, the unique ID (GEOID) for Robbinsville Township will still be 3402177210 in the census data…you will have to manually change its GEOID to 3402163850 in order to get the 2000 data to join to the MCD file correctly.*** Edited in April 2021 to match PCPC's planning district boundaries and numbers dated 2013
https://catalog.dvrpc.org/dvrpc_data_license.html
County boundaries (polygon features) for Pennsylvania, New Jersey, Delaware, Maryland, New York, Connecticut, and the District of Columbia. Original Sources: PA - PennDOT NJ - NJOIT DE - DE Geological Survey MD, NY, CT, DC - ESRI Linework has been adjusted using the following methodology. NJ's boundaries overrode PA, since NJ's were highly accurate. PA overrode MD since PennDOT's were more accurate than ESRI's, and DE overrode PA & MD, since the \"arc\" of northernmost DE was a bit more accurate than PennDOT's or our original county boundaries. County boundaries in the DVRPC region have been adjusted to align with parcel data (where appropriate). NOTE: Use the following definition query if only need one county in DVRPC Region \"Co_Name\" LIKE '%Bucks%'
https://catalog.dvrpc.org/dvrpc_data_license.html
Data pertaining to business parks were obtained from counties in the DVRPC region (if available). These datasets were used in conjunction with DVRPC's latest land use layer and web searches to compile a list of locations with street addresses.
For the purposes of this data gathering exercise, the following assumptions were made to help determine if a location was a business park:
Location is in an area classified as commercial in DVRPC’s land use layer
Location often has a single landlord listed
Office parks are considered business parks
Medical offices, in certain areas, may qualify as a business park
Businesses adjacent or in close proximity to a business park are not always part of that business park
A single building containing multiple businesses can qualify as a business park
Please note, although efforts were made to ensure its completeness and accuracy, this layer is not a definitive inventory of the region's business parks and should not be considered as such.
https://catalog.dvrpc.org/dvrpc_data_license.html
This index represents the coverage of the scans of DVRPC's non-orthorectified, mylar, aerial photo enlargements for the year 1959.
https://catalog.dvrpc.org/dvrpc_data_license.html
This index represents the coverage of the scans of DVRPC's non-orthorectified, mylar, aerial photo enlargements for the years 1965, 1970, 1975, 1980, 1985, 1990, and 1995.
https://catalog.dvrpc.org/dvrpc_data_license.html
This index represents the coverage of DVRPC's UTM Zone18N orthoimagery for the years 2000, 2005, and 2010. This orthoimagery covers the entire 9-county DVRPC region.
https://catalog.dvrpc.org/dvrpc_data_license.html
This index represents the coverage of DVRPC's New Jersey State Plane orthoimagery for the years 2000, 2005, and 2010. This orthoimagery covers the 4-county, New Jersey portion of the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
This index represents the coverage of DVRPC's Pennsylvania State Plane (South Zone) orthoimagery for the years 2000, 2005, and 2010. This orthoimagery covers the 5-county, Pennsylvania portion of the DVRPC region.
https://catalog.dvrpc.org/dvrpc_data_license.html
The DVRPC region falls entirely within the Philadelphia-Wilmington-Atlantic City, NJ 8-hour Ozone Nonattainment Area. The U.S. Environmental Protection Agency (EPA) set national air quality standards (NAAQS) for ground-level ozone, with a design value level of 0.070 parts per million - at time of publication, this area does not meet that standard.
Public domain
The PM-2.5 maintenance areas for the DVRPC region represent areas that meet federal air quality standards for particulate matter pollution. The DVRPC region covers the following three maintenance areas: the Philadelphia-Wilmington, PA-NJ-DE area; the New York-Northern New Jersey-Long Island, NY-NJ-CT area; and the Delaware County, PA area.
Public domain
DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [ "" ], "issued": "2024-08-07T20:57:03.000Z", "modified": "2024-08-07T21:09:47.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::montgomery-county-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=6d8ef2bc652f4d26882e86d574db4a8e", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::bucks-county-land-use-2023", "title": "Bucks County Land Use 2023", "description": "DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [], "issued": "2024-08-07T20:46:56.000Z", "modified": "2024-08-07T21:09:33.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::bucks-county-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=9c25d51bbaf44406a1cbe967cfd00725", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::burlington-county-land-use-2023", "title": "Burlington County Land Use 2023", "description": "DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [], "issued": "2024-08-07T20:48:46.000Z", "modified": "2024-08-07T21:09:25.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::burlington-county-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=f909dd4aa4b74b72aade016b136ceb64", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::camden-county-land-use-2023", "title": "Camden County Land Use 2023", "description": "DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [], "issued": "2024-08-07T20:49:48.000Z", "modified": "2024-08-07T21:09:16.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::camden-county-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=a959ffff95744742aca3bc22b8d2505b", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::chester-county-land-use-2023", "title": "Chester County Land Use 2023", "description": "DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [], "issued": "2024-08-07T20:50:46.000Z", "modified": "2024-08-07T21:09:09.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::chester-county-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=07a69b904c08403ebc5db6ccbba20a9d", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::gloucester-county-land-use-2023", "title": "Gloucester County Land Use 2023", "description": "DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [], "issued": "2024-08-07T20:53:00.000Z", "modified": "2024-08-07T21:09:00.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::gloucester-county-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=905095238254496ca5ffdadba23fa5ab", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::mercer-county-land-use-2023", "title": "Mercer County Land Use 2023", "description": "DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [], "issued": "2024-08-07T20:55:36.000Z", "modified": "2024-08-07T21:08:52.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::mercer-county-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=798b16d9500748bd8dd202bb10044ce4", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::philadelphia-land-use-2023", "title": "Philadelphia Land Use 2023", "description": "DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.
Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.
lu23cat: Land use main category two-digit code.
lu23catn: Land use main category name.
lu23cat | lu23catn |
1 | Residential |
3 | Industrial |
4 | Transportation |
5 | Utility |
6 | Commercial |
7 | Institutional |
8 | Military |
9 | Recreation |
10 | Agriculture |
11 | Mining |
12 | Wooded |
13 | Water |
14 | Undeveloped |
lu23sub: Land use subcategory five-digit code.
(refer to this data dictionary for code description)
lu23subn: Land use subcategory name.
lu23dev: Development status.
mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).
acres: Area of feature, in US acres.
geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).
state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.
", "keyword": [], "issued": "2024-08-07T20:58:12.000Z", "modified": "2024-08-07T21:08:17.000Z", "publisher": { "name": "DVRPC-GIS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "DVRPCGIS", "hasEmail": "mailto:slawrence@dvrpc.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::philadelphia-land-use-2023" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=ccd01e6167904144941d04ec456259b5&sublayer=0", "landingPage": "https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dvrpcgis::dvrpc-transit-parking-facilities", "title": "DVRPC Transit Parking Facilities", "description": "This layer represents footprints of transit parking facilities in the DVRPC region. Additionally, parking facilities at stations on SEPTA's Wilmington/Newark Line in New Castle County, Delaware (and several park & ride lots in that county) are included. The features in this layer were generated by digitizing all discernable parking areas that are associated with transit facilities using the best available aerial imagery.
Included in this layer’s attribute table is a field that contains a link to the facilities’ webpage (if available). These pages are maintained by the transit provider who operates the facility.
Please note, although efforts were made to ensure its completeness and accuracy, this layer is not a definitive inventory of the region's transit parking facilities and should not be considered as such.
https://catalog.dvrpc.org/dvrpc_data_license.html
DVRPC is counting a region in motion. As leaders in travel monitoring, we provide access to thousands of traffic counts throughout the region. Travel Monitoring counts are used to develop policies, regulations, and design decisions about transportation infrastructure throughout the region.
Vehicle class counts can be joined to this dataset using RECORDNUM
For more information, contact our Office of Travel Monitoring: Joshua Rocks, Manager Phone: 215.238.2854 | Email: jrocks@dvrpc.org
https://catalog.dvrpc.org/dvrpc_data_license.html
Monthly TIP Action updates in the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
Monthly TIP action updates in the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
The Sidewalk Priority Score (SPS) is a screening tool for identifying needed sidewalk connections in the Greater Philadelphia region. It assigns a priority score (from 1 to 10, with 10 being the highest priority) to every street segment in the DVRPC region that is missing complete sidewalks on both sides of the road. The score is based on transportation conditions and needs and nearby destinations. Clicking on a segment and looking at the components of the priority score will provide the best understanding of the segment's values.
Use this data to explore your community and investigate where building a missing sidewalk connection would have the greatest impact. This data could be used to support a grant application, aid in local pedestrian plans, improve project selection, or provide a starting point for considering where sidewalks are most needed.
Learn more about the SPS Methodology
https://catalog.dvrpc.org/dvrpc_data_license.html
The Reliability Score layer shows the results of combining TTI, OTP, and scheduled speed to calculate the overall measure of reliability. A high reliability score (reliscore) is indicative of segments that may benefit from targeted improvements to improve transit operations. The Reliability Score was weighted by ridership (riderrelis) to highlight segments that impact high ridership surface transit service and allow for prioritization of improvements.
https://catalog.dvrpc.org/dvrpc_data_license.html
DVRPC's Regional Trails Program provides planning assistance and financial support to trail developers, counties, municipalities and nonprofit organizations to complete the Circuit, Greater Philadelphia's 800-plus-mile network of multi-use trails. With financial support from the William Penn Foundation, the Regional Trails Program has provided $22 million in funding to over 120 trail planning, design, and construction projects to date.
https://catalog.dvrpc.org/dvrpc_data_license.html
This point feature class contains representations of pedestrian curb ramps found throughout the DVRPC region.
https://catalog.dvrpc.org/dvrpc_data_license.html
This polyline feature class contains detailed digital representations of pedestrian network features found throughout the DVRPC region. The feature class leverages three (3) FGDB Subtypes.
Sidewalk (1) - Paved pathway running primarily parallel to named roadway.
Crosswalk (2) - Discrete path connecting discrete pedestrian features across a named roadway (ramps, sidewalks, etc.)
Trail (3) - Paved or unpaved pathway serving sporting or aesthetic purposes.
https://catalog.dvrpc.org/dvrpc_data_license.html
Sidewalk coverage percentages along road segments by combining road network data from Overture Maps (2025) with pedestrian infrastructure (2020)
The sw_ratio field provides a normalized metric for sidewalk coverage:
Calculation: Average of both side coverage ratios: (side1_length/seg_length + side2_length/seg_length) / 2
Range: 0.0 (no sidewalk) to 1.0 (complete coverage on both sides)
Capped: Values are capped at 1.0 even if measured sidewalk lengths exceed segment length due to measurement overlaps
See https://github.com/dvrpc/gis-sw-coverage for detailed methodology
https://catalog.dvrpc.org/dvrpc_data_license.html
DVRPC counts bicyclists and pedestrians because count data helps us understand and plan for the role bicyclists and pedestrians play in our transportation network. Similar to how planners use vehicular traffic counts to analyze roadway facilities, our bicycle and pedestrian counting program allows planners to measure existing levels of bicycling and walking, monitor travel trends, plan for new or improved facilities and measure outcomes of bicycle and pedestrian related projects. Our counting program consists of four types of counts: Project Counts, Cyclical Counts, Screenline Counts and Permanent Counts.
https://catalog.dvrpc.org/dvrpc_data_license.html
Passenger rail stations in the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
Passenger rail in the DVRPC Region
https://catalog.dvrpc.org/dvrpc_data_license.html
This layer consists of points representing park and ride origin survey locations.
Only train stations and park and rides that have publicly accessible parking and have been surveyed by DVRPC. Each station’s data is based on fieldwork completed in one day, typically in fall or spring between the hours of 9 AM and 4 PM.
A publicly accessible web mapping application is available for use. To ensure privacy of the surveyed vehicles, individual points are aggregated to a standard geography in order to visualize trends of how people are using public transportation as part of their commute. In this case, the standard geography is 1/2 square mile hexagons that are large enough to anonymize the points within them, but also small enough to effectively display the trends in the data:
DVRPC Park & Ride Passenger Origins
https://catalog.dvrpc.org/dvrpc_data_license.html
Working park and ride lots in the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
The Transportation Improvement Program (TIP) is the regionally agreed-upon list of priority transportation projects, as required by federal law (ISTEA, TEA-21, SAFETEA LU, MAP-21, the FAST Act, and the Infrastructure Investment and Jobs Act/Bipartisan Infrastructure law (IIJA/BIL)).
The TIP document must list all projects that intend to use federal funds, along with all non-federally funded projects that are regionally significant. It also includes all other State funded capital projects. The projects are multi-modal; that is, they include bicycle, pedestrian, ITS, and freight related projects, as well as the more traditional highway and public transit projects.
The DVRPC FFY2026 TIP for New Jersey (FFY26-FFY29) was adopted by the DVRPC Board on September 25, 2025, and became effective on December 15, 2025. This important document represents the latest program of transportation projects for the NJ portion of the region. This TIP will remain in effect until at least September 30, 2027.
Please note that not all TIP projects are able to be mapped.
https://catalog.dvrpc.org/dvrpc_data_license.html
The Transportation Improvement Program (TIP) is the regionally agreed-upon list of priority transportation projects, as required by federal law (ISTEA, TEA-21, SAFETEA LU, MAP-21, the FAST Act, and the Infrastructure Investment and Jobs Act/Bipartisan Infrastructure law (IIJA/BIL)).
The TIP document must list all projects that intend to use federal funds, along with all non-federally funded projects that are regionally significant. It also includes all other State funded capital projects. The projects are multi-modal; that is, they include bicycle, pedestrian, ITS, and freight related projects, as well as the more traditional highway and public transit projects.
The DVRPC FFY2026 TIP for New Jersey (FFY26-FFY29) was adopted by the DVRPC Board on September 25, 2025, and became effective on December 15, 2025. This important document represents the latest program of transportation projects for the NJ portion of the region. This TIP will remain in effect until at least September 30, 2027.
Please note that not all TIP projects are able to be mapped.
https://catalog.dvrpc.org/dvrpc_data_license.html
National Highway System Routes in the DVRPC 9-County Region (Last updated January 2018)
https://catalog.dvrpc.org/dvrpc_data_license.html
LTS is a road classification scheme based on the estimated comfort of bicyclists in the traffic stream.
The table below summarizes LTS in terms of the cyclist type that would be comfortable on certain levels and the characteristics of those levels. Generally, the higher the LTS, the more dangerous or the greater the perceived danger, and the more confident the cyclist needs to feel on their bike to consider riding on that type of road.
LTS 1: Most People Lowest stress, comfortable for most ages and abilities
LTS 2: Interested, but Concerned Suitable for most adults; presents little traffic stress
LTS 3: Enthused and Confident Moderate traffic stress; comfortable for those already biking in American cities
LTS 4: Strong and Fearless High traffic stress; multilane, fast-moving traffic
DVRPC’s LTS assignment is based on the number of lanes, vehicle speed (PennDOT posted speed limits, NJDOT posted speed limits, and modeled effective vehicle speeds) and the presence and type of bicycle facility along the road segment.
Please visit https://github.com/dvrpc/gis-lts-calc for methodology and https://dvrpc.github.io/link-docs/how-it-works/lts/ for it's use in the LINK web application.
https://catalog.dvrpc.org/dvrpc_data_license.html
DVRPC, in partnership with PennDOT District 6, has developed the web-based IDRuM application that organizes all existing PennDOT Emergency Detour Routes within the DVRPC region. IDRuM compiles the PDF detour maps into a simple \"point-and-click\" online application which allows the PDF detour maps to be downloaded to be viewed, saved, and/or printed. All detour routes are PennDOT District 6 approved for both passenger vehicles and heavy trucks. The detour route PDFs can be used by first responders, planners, and/or anyone who is in need of DOT approved detour route plans.
https://catalog.dvrpc.org/dvrpc_data_license.html
DVRPC Connectivity Score by TAZ (2005). Data set was originally created for DVRPC's CI2 project. It reflects the connectivity of a place’s street network; commonly used as a proxy for walkability. Source: Density of 3+ leg non-freeway intersections from TIM 2.0 network; intersections <100 ft. apart were combined Intersection Density: Number of intersections per square mile Intersection Count: Number of intersections
https://catalog.dvrpc.org/dvrpc_data_license.html
The Regional High Injury Network (RHIN) is the result of a data-driven process designed to show where killed and severe injury (KSI) crashes occurred, and where bicyclists and pedestrian crashes occurred, both using reportable crash data from 2017-2021 (NJ) and 2018-2022 (PA). The RHIN has two layers: the red layer includes segments where 2 or more KSI crashes took place within a half mile, and the blue layer includes segments where 2 or more bicyclist and/or pedestrian crashes of any injury severity occurred, also within a half mile.
The RHIN does not include previously developed high injury networks (e.g.: Philadelphia, Trenton) but the Regional Vision Zero effort recognizes those existing networks. The RHIN excludes limited-access roadways (e.g.: ramp-access highways) because road design changes and investment strategies on such major facilities are beyond the scope of this effort.
The RHIN is a planning tool designed for use in tandem with other data sets to inform roadway safety investigations and for reference in plans and projects. We are publishing the RHIN in advance of the Regional Vision Zero Safety Action Plan release to support local safety planning. If you would like additional background on our methodology, data sources, or other information, please reach out to RVZ@DVRPC.ORG.
For specifics about methodology please visit https://github.com/dvrpc/high-injury-network
https://catalog.dvrpc.org/dvrpc_data_license.html
Analyzing congestion at the roadway corridor facility level, rather than by roadway segment, can give a better understanding of why some roadway corridors are performing better than others, and enables congestion to be tracked over time. There are 336 Focus Roadway Corridor Facilities in the DVRPC region – 205 in Pennsylvania and 131 in New Jersey.
Focus Roadway Corridor Facilities are used to prioritize congested locations and develop a set of focused strategies to manage congestion. Facility limits are delineated based on where there are breaks between Congested Corridor and Subcorridor Areas, and between major interchanges and arterial roadways. Ramps are not included due to lack of traffic volume data, but freeway to freeway interchanges (e.g. I-76 to I-676) with traffic volume data are included.
These facilities are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time and planning time (95th percentile) vehicle and volume delays.
Analyzing congestion at the roadway corridor facility level, rather than by roadway segment, can give a better understanding of why some roadway corridors are performing better than others, and enables congestion to be tracked over time. There are 336 Focus Roadway Corridor Facilities in the DVRPC region – 205 in Pennsylvania and 131 in New Jersey.
Focus Roadway Corridor Facilities are used to prioritize congested locations and develop a set of focused strategies to manage congestion. Facility limits are delineated based on where there are breaks between Congested Corridor and Subcorridor Areas, and between major interchanges and arterial roadways. Ramps are not included due to lack of traffic volume data, but freeway to freeway interchanges (e.g. I-76 to I-676) with traffic volume data are included.
These facilities are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time and planning time (95th percentile) vehicle and volume delays.
CMP Focus Roadway Corridor Facility Database Fields
MAPID – Unique identifier for the focus roadway corridor facility; NJ identifier starts at 300
ROADWAY – Roadway name of the facility
FROMLIMIT – From limit roadway of the facility
TOLIMIT – To limit roadway of the facility
MILES – Length of the facility in miles
LIMITACCES – Facility access type
Valid values:
Yes – Limited access
No – Not limited access
MUNICIPAL – Municipalities the facility is within; if too many then indicate “various”
COUNTY – County the facility is within
COUNTY_ONE – One County with the majority of the focus roadway corridor
AADT – Annual Average Daily Traffic for both directions of travel
TTAMVEHDEL – Highest AM Peak Vehicle Travel Time Delay
TTPMVEHDEL – Highest PM Peak Travel Time Vehicle Delay
TTHIVEHDEL – Highest of AM and PM Peak Travel Time Vehicle Delay
TTTDVEHDEL – Time of Day of Highest Peak Travel Time Vehicle Delay
Valid values:
AM – AM Peak
PM – PM Peak
TTRKVEHDEL – Highest Peak Travel Time Vehicle Delay Rank with lower numbers indicating the most delay
TTRKVOLDEL – Highest Peak Travel Time Volume Delay Rank with lower numbers indicating the most delay
TTAMVOLDEL – Highest Peak Travel Time AM Volume Delay
TTPMVOLDEL – Highest Peak Travel Time PM Volume Delay
TTHIVOLDEL – Highest of AM and PM Peak Travel Time Volume Delay
TTTDVOLDEL – Time of Day of Highest Peak Travel Time Volume Delay
Valid values:
AM – AM Peak
PM – PM Peak
TTAMHHMMSS – Highest of AM Peak Travel Time Volume Delay in HH:MM:SS
TTPMHHMMSS – Highest of PM Peak Travel Time Volume Delay in HH:MM:SS
PTAMVEHDEL – Highest AM Peak Planning Time Vehicle Delay
PTPMVEHDEL – Highest PM Peak Planning Time Vehicle Delay
PTHIVEHDEL – Highest of AM and PM Peak Planning Time Vehicle Delay
PTTDVEHDEL – Time of Day of Highest Peak Planning Time Vehicle Delay
Valid values:
AM – AM Peak
PM – PM Peak
PTRKVEHDEL – Highest Peak Planning Time Vehicle Delay Rank with lower numbers indicating the most delay
PTRKVOLDEL – Highest Peak Planning Time Volume Delay Rank with lower numbers indicating the most delay
PTAMVOLDEL – Highest Peak Planning Time AM Volume Delay
PTPMVOLDEL – Highest Peak Planning Time PM Volume Delay
PTHIVOLDEL – Highest of AM and PM Peak Planning Time Volume Delay
PTTDVOLDEL – Time of Day of Highest Peak Planning Time Volume Delay
Valid values:
AM – AM Peak
PM – PM Peak
PTAMHHMMSS – Highest of AM Peak Planning Time Volume Delay in HH:MM:SS
PTPMHHMMSS – Highest of PM Peak Planning Time Volume Delay in HH:MM:SS
TKAADT – Truck Annual Average Daily Traffic for both directions of travel
TKRKVOLDEL – Highest Truck Peak Planning Time Volume Delay Rank with lower numbers indicating the most delay
TKAMVOLDEL – Highest Truck Peak Planning Time AM Volume Delay
TKPMVOLDEL – Highest Truck Peak Planning Time PM Volume Delay
TKHIVOLDEL – Highest of AM and PM Truck Peak Planning Time Volume Delay
TKTDVOLDEL – Time of Day of Highest Peak Truck Planning Time Volume Delay
Valid values:
AM – AM Peak
PM – PM Peak
TKAMHHMMSS – Highest of AM Peak Truck Planning Time Volume Delay expressed in HH:MM:SS
TKPMHHMMSS – Highest of PM Peak Truck Planning Time Volume Delay expressed in HH:MM:SS
STATE – State the facility is within
LOTTRMAXMI – Miles of travel time unreliability for the measure (1.50 or more)
LOTTRMAXPM – Miles of travel time unreliability (LOTTRMAXMI) per road mile
TTTRMAXMI – Miles of truck travel time unreliability for the measure (2.00 or more)
TTTRMAXPM – Miles of truck travel time unreliability (TTTRMAXMI) per road mile
PHEDVAPMI – Total Peak Hours of Excessive Delay weighted by road miles
MAXVCMI – Miles of Travel Demand Model forecasted congestion V/C greater than or equal to 0.85 in 2050
MAXVCPMI – Miles of Travel Demand Model forecasted congestion (MAXVCMI) per road mile
CRINDEXMI – Miles of high crash rate for the measure
CRINDEXPMI – Miles of high crash rate (CRINDEXMI) per road mile
CRINDEXMI – Miles of high crash severity for the measure
CRINDEXPMI – Miles of high crash severity (CRINDEXMI) per road mile
IMRMAXPMI – CMP Objective Measure score to increase mobility and reliability and meet PM3 targets weighted by road miles, where the maximum score is 4.0
IMRMAXPR – CMP Objective Measure rank of IMRMAXPMI where lower values represent higher scores
IMIAMAXPMI – CMP Objective Measure score to integrate modes and provide transit where it is most needed weighted by road miles, where the maximum score is 2.0
IMIAMAXPR – CMP Objective Measure rank of IMIAMAXPMI where lower values represent higher scores
MRMAXPMI – CMP Objective Measure score to modernize and maintain the existing transportation network, where the maximum score is 1.5
MRMAXPR – CMP Objective Measure rank of MRMAXPMI where lower values represent higher scores
SVRMAXPMI – CMP Objective Measure score to achieve Vision Zero, where the maximum score is 2.0
SVRMAXPR – CMP Objective Measure rank of SVRMAXPMI where lower values represent higher scores
GCMAXPMI – CMP Objective Measure score to maintain the movement of goods by truck and meet PM3 targets, where the maximum score is 1.5
GCMAXPR – CMP Objective Measure rank of GCMAXPMI where lower values represent higher scores
SPMAXPMI – CMP Objective Measure score to maintain and enhance transportation security and prepare for major events, where the maximum score is 1.0
SPMAXPR – CMP Objective Measure rank of SPMAXPMI where lower values represent higher scores
LRPMAXPMI – CMP Objective Measure score to support LRP centers, infill, redevelopment and emerging growth areas, environmental sensitive areas, and Environmental Justice and Equity populations, where the maximum score is 3.0
LRPMAXPR – CMP Objective Measure rank of LRPMAXPMI where lower values represent higher scores
CMPMAXPMI – Total of of the CMP Objective Measure scores, where the maximum score is 15.0
CMPMAXPR – Total CMP Objective Measure rank of CMPMAXPMI where lower values represent higher scores
VOLDEL2017 – Volume Delay 5-6 PM in 2017
VOLDEL2021 – Volume Delay 5-6 PM in 2021
VOLDEL2022 – Volume Delay 5-6 PM in 2022
Below are the new fields as of February 14, 2025
TTD070817 – 7-8 AM Travel Time Vehicle Delay in 2017
TTD171817 – 5-6 PM Travel Time Vehicle Delay in 2017
TTTD070817 – 7-8 AM Travel Time Volume Delay in 2017
TTTD171817 – 5-6 PM Travel Time Volume Delay in 2017
PTD070817 – 7-8 AM Planning Time Vehicle Delay in 2017
PTD171817 – 5-6 PM Planning Time Vehicle Delay in 2017
PTTD070817 – 7-8 AM Planning Time Volume Delay in 2017
PTTD171817 – 5-6 PM Planning Time Volume Delay in 2017
TTD070821 – 7-8 AM Travel Time Vehicle Delay in 2021
TTD080921 – 8-9 AM Travel Time Vehicle Delay in 2021
TTD161721 – 4-5 PM Travel Time Vehicle Delay in 2021
TTD171821 – 5-6 PM Travel Time Vehicle Delay in 2021
TTTD070821 – 7-8 AM Travel Time Volume Delay in 2021
TTTD080921 – 8-9 AM Travel Time Volume Delay in 2021
TTTD161721 – 4-5 PM Travel Time Volume Delay in 2021
TTTD171821 – 5-6 PM Travel Time Volume Delay in 2021
PTD070821 – 7-8 AM Planning Time Vehicle Delay in 2021
PTD080921 – 8-9 AM Planning Time Vehicle Delay in 2021
PTD161721 – 4-5 PM Planning Time Vehicle Delay in 2021
PTD171821 – 5-6 PM Planning Time Vehicle Delay in 2021
PTTD070821 – 7-8 AM Planning Time Volume Delay in 2021
PTTD080921 – 8-9 AM Planning Time Volume Delay in 2021
PTTD161721 – 4-5 PM Planning Time Volume Delay in 2021
PTTD171821 – 5-6 PM Planning Time Volume Delay in 2021
TTI060721 – 6-7 AM Travel Time Index in 2021
TTI070821 – 7-8 AM Travel Time Index in 2021
TTI080921 – 8-9 AM Travel Time Index in 2021
TTI091021 – 9-10 AM Travel Time Index in 2021
TTI101121 – 10-11 AM Travel Time Index in 2021
TTI111221 – 11-12 AM Travel Time Index in 2021
TTI121321 – 12-1 PM Travel Time Index in 2021
TTI131421 – 1-2 PM Travel Time Index in 2021
TTI141521 – 2-3 PM Travel Time Index in 2021
TTI151621 – 3-4 PM Travel Time Index in 2021
TTI161721 – 4-5 PM Travel Time Index in 2021
TTI171821 – 5-6 PM Travel Time Index in 2021
TTI181921 – 6-7 PM Travel Time Index in 2021
TTI192021 – 7-8 PM Travel Time Index in 2021
TTI202121 – 8-9 PM Travel Time Index in 2021
Below are the new Speed fields as of March 25, 2025
SPD060721 – 6-7 AM Speeds in 2021
SPD070821 – 7-8 AM Speeds in 2021
SPD080921 – 8-9 AM Speeds in 2021
SPD091021 – 9-10 AM Speeds in 2021
SPD101121 – 10-11 AM Speeds in 2021
SPD111221 – 11-12 AM Speeds in 2021
SPD121321 – 12-1 PM Speeds in 2021
SPD131421 – 1-2 PM Speeds in 2021
SPD141521 – 2-3 PM Speeds in 2021
SPD151621 – 3-4 PM Speeds in 2021
SPD161721 – 4-5 PM Speeds in 2021
SPD171821 – 5-6 PM Speeds in 2021
SPD181921 – 6-7 PM Speeds in 2021
SPD192021 – 7-8 PM Speeds in 2021
SPD202121 – 8-9 PM Speeds in 2021
TTD070822 – 7-8 AM Travel Time Vehicle Delay in 2022
TTD080922 – 8-9 AM Travel Time Vehicle Delay in 2022
TTD161722 – 4-5 PM Travel Time Vehicle Delay in 2022
TTD171822 – 5-6 PM Travel Time Vehicle Delay in 2022
TTTD070822 – 7-8 AM Travel Time Volume Delay in 2022
TTTD080922 – 8-9 AM Travel Time Volume Delay in 2022
TTTD161722 – 4-5 PM Travel Time Volume Delay in 2022
TTTD171822 – 5-6 PM Travel Time Volume Delay in 2022
PTD070822 – 7-8 AM Planning Time Vehicle Delay in 2022
PTD080922 – 8-9 AM Planning Time Vehicle Delay in 2022
PTD161722 – 4-5 PM Planning Time Vehicle Delay in 2022
PTD171822 – 5-6 PM Planning Time Vehicle Delay in 2022
PTTD070822 – 7-8 AM Planning Time Volume Delay in 2022
PTTD080922 – 8-9 AM Planning Time Volume Delay in 2022
PTTD161722 – 4-5 PM Planning Time Volume Delay in 2022
PTTD171822 – 5-6 PM Planning Time Volume Delay in 2022
TTI060722 – 6-7 AM Travel Time Index in 2022
TTI070822 – 7-8 AM Travel Time Index in 2022
TTI080922 – 8-9 AM Travel Time Index in 2022
TTI091022 – 9-10 AM Travel Time Index in 2022
TTI101122 – 10-11 AM Travel Time Index in 2022
TTI111222 – 11-12 AM Travel Time Index in 2022
TTI121322 – 12-1 PM Travel Time Index in 2022
TTI131422 – 1-2 PM Travel Time Index in 2022
TTI141522 – 2-3 PM Travel Time Index in 2022
TTI151622 – 3-4 PM Travel Time Index in 2022
TTI161722 – 4-5 PM Travel Time Index in 2022
TTI171822 – 5-6 PM Travel Time Index in 2022
TTI181922 – 6-7 PM Travel Time Index in 2022
TTI192022 – 7-8 PM Travel Time Index in 2022
TTI202122 – 8-9 PM Travel Time Index in 2022
Below are the new Speed fields as of March 25, 2025
SPD060722 – 6-7 AM Speeds in 2022
SPD070822 – 7-8 AM Speeds in 2022
SPD080922 – 8-9 AM Speeds in 2022
SPD091022 – 9-10 AM Speeds in 2022
SPD101122 – 10-11 AM Speeds in 2022
SPD111222 – 11-12 AM Speeds in 2022
SPD121322 – 12-1 PM Speeds in 2022
SPD131422 – 1-2 PM Speeds in 2022
SPD141522 – 2-3 PM Speeds in 2022
SPD151622 – 3-4 PM Speeds in 2022
SPD161722 – 4-5 PM Speeds in 2022
SPD171822 – 5-6 PM Speeds in 2022
SPD181922 – 6-7 PM Speeds in 2022
SPD192022 – 7-8 PM Speeds in 2022
SPD202122 – 8-9 PM Speeds in 2022
TTD070823 – 7-8 AM Travel Time Vehicle Delay in 2023
TTD080923 – 8-9 AM Travel Time Vehicle Delay in 2023
TTD161723 – 4-5 PM Travel Time Vehicle Delay in 2023
TTD171823 – 5-6 PM Travel Time Vehicle Delay in 2023
TTTD070823 – 7-8 AM Travel Time Volume Delay in 2023
TTTD080923 – 8-9 AM Travel Time Volume Delay in 2023
TTTD161723 – 4-5 PM Travel Time Volume Delay in 2023
TTTD171823 – 5-6 PM Travel Time Volume Delay in 2023
PTD070823 – 7-8 AM Planning Time Vehicle Delay in 2023
PTD080923 – 8-9 AM Planning Time Vehicle Delay in 2023
PTD161723 – 4-5 PM Planning Time Vehicle Delay in 2023
PTD171823 – 5-6 PM Planning Time Vehicle Delay in 2023
PTTD070823 – 7-8 AM Planning Time Volume Delay in 2023
PTTD080923 – 8-9 AM Planning Time Volume Delay in 2023
PTTD161723 – 4-5 PM Planning Time Volume Delay in 2023
PTTD171823 – 5-6 PM Planning Time Volume Delay in 2023
TTI060723 – 6-7 AM Travel Time Index in 2023
TTI070823 – 7-8 AM Travel Time Index in 2023
TTI080923 – 8-9 AM Travel Time Index in 2023
TTI091023 – 9-10 AM Travel Time Index in 2023
TTI101123 – 10-11 AM Travel Time Index in 2023
TTI111223 – 11-12 AM Travel Time Index in 2023
TTI121323 – 12-1 PM Travel Time Index in 2023
TTI131423 – 1-2 PM Travel Time Index in 2023
TTI141523 – 2-3 PM Travel Time Index in 2023
TTI151623 – 3-4 PM Travel Time Index in 2023
TTI161723 – 4-5 PM Travel Time Index in 2023
TTI171823 – 5-6 PM Travel Time Index in 2023
TTI181923 – 6-7 PM Travel Time Index in 2023
TTI192023 – 7-8 PM Travel Time Index in 2023
TTI202123 – 8-9 PM Travel Time Index in 2023
Below are the new Speed fields as of March 25, 2025
SPD060723 – 6-7 AM Speeds in 2023
SPD070823 – 7-8 AM Speeds in 2023
SPD080923 – 8-9 AM Speeds in 2023
SPD091023 – 9-10 AM Speeds in 2023
SPD101123 – 10-11 AM Speeds in 2023
SPD111223 – 11-12 AM Speeds in 2023
SPD121323 – 12-1 PM Speeds in 2023
SPD131423 – 1-2 PM Speeds in 2023
SPD141523 – 2-3 PM Speeds in 2023
SPD151623 – 3-4 PM Speeds in 2023
SPD161723 – 4-5 PM Speeds in 2023
SPD171823 – 5-6 PM Speeds in 2023
SPD181923 – 6-7 PM Speeds in 2023
Below are the new 2024 fields as of March 25, 2025
TTD070824– 7-8 AM Travel Time Vehicle Delay in 2024
TTD080924 – 8-9 AM Travel Time Vehicle Delay in 2024
TTD161724 – 4-5 PM Travel Time Vehicle Delay in 2024
TTD171824 – 5-6 PM Travel Time Vehicle Delay in 2024
TTTD070824 – 7-8 AM Travel Time Volume Delay in 2024
TTTD080924 – 8-9 AM Travel Time Volume Delay in 2024
TTTD161724 – 4-5 PM Travel Time Volume Delay in 2024
TTTD171824 – 5-6 PM Travel Time Volume Delay in 2024
PTD070824 – 7-8 AM Planning Time Vehicle Delay in 2024
PTD080924 – 8-9 AM Planning Time Vehicle Delay in 2024
PTD161724 – 4-5 PM Planning Time Vehicle Delay in 2024
PTD171824 – 5-6 PM Planning Time Vehicle Delay in 2024
PTTD070824 – 7-8 AM Planning Time Volume Delay in 2024
PTTD080924 – 8-9 AM Planning Time Volume Delay in 2024
PTTD161724 – 4-5 PM Planning Time Volume Delay in 2024
PTTD171824 – 5-6 PM Planning Time Volume Delay in 2024
TTI060724 – 6-7 AM Travel Time Index in 2024
TTI070824 – 7-8 AM Travel Time Index in 2024
TTI080924 – 8-9 AM Travel Time Index in 2024
TTI091024 – 9-10 AM Travel Time Index in 2024
TTI101124 – 10-11 AM Travel Time Index in 2024
TTI111224 – 11-12 AM Travel Time Index in 2024
TTI121324 – 12-1 PM Travel Time Index in 2024
TTI131424 – 1-2 PM Travel Time Index in 2024
TTI141524 – 2-3 PM Travel Time Index in 2024
TTI151624 – 3-4 PM Travel Time Index in 2024
TTI161724 – 4-5 PM Travel Time Index in 2024
TTI171824 – 5-6 PM Travel Time Index in 2024
TTI181923 – 6-7 PM Travel Time Index in 2024
TTI192024 – 7-8 PM Travel Time Index in 2024
TTI202124 – 8-9 PM Travel Time Index in 2024
SPD060724 – 6-7 AM Speeds in 2024
SPD070824 – 7-8 AM Speeds in 2024
SPD080924 – 8-9 AM Speeds in 2024
SPD091024 – 9-10 AM Speeds in 2024
SPD101124 – 10-11 AM Speeds in 2024
SPD111224 – 11-12 AM Speeds in 2024
SPD121324 – 12-1 PM Speeds in 2024
SPD131424 – 1-2 PM Speeds in 2024
SPD141524 – 2-3 PM Speeds in 2024
SPD151624 – 3-4 PM Speeds in 2024
SPD161724 – 4-5 PM Speeds in 2024
SPD171824 – 5-6 PM Speeds in 2024
SPD181924 – 6-7 PM Speeds in 2024
Note: Null values in fields TTD070817, TTD171817, TTTD070817, TTTD171817, PTD070817, PTD171817, PTTD070817, and PTTD171817 indicate that the values are not comparable to the same values in 2021, 2022, or 2024, and so on.
https://catalog.dvrpc.org/dvrpc_data_license.html
Focus Limited Access Roadway Bottlenecks contain a road segment on a limited access roadway or approach to a limited access roadway with a high peak hour TTI greater than 1.50 or PTI segment greater than 3.00, and high peak hour vehicle and volume delays. Any trailing adjacent segment with a TTI of 1.40 or more is also included as part of the bottleneck. For each bottleneck, peak travel time vehicle and volume delays are summarized for the immediate bottleneck segment and remaining upstream segments with a TTI of 1.40 or more, or until another bottleneck is encountered.
There are 145 Focus Limited Access Roadway Bottlenecks identified: 102 in the Pennsylvania subregion and 43 in the New Jersey subregion. These bottlenecks are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time vehicle and volume delays.
CMP Focus Limited Access Roadway Bottleneck Database Fields
MAPID – Unique map bottleneck identifier by state
MAPID2 – Unique map bottleneck identifier by DVRPC region; NJ bottlenecks start at identifier 200
NAME – Names of the intersecting expressways and ramps
MUNICIPAL – Municipal where intersection exists; for Philadelphia it is the planning district
COUNTY – County where the intersection exists
AMPKVEDEL – AM Peak Vehicle Delay
PMPKVEDEL – PM Peak Vehicle Delay
HIPKVEDEL – Highest AM or PM Peak Vehicle Delay
TDPKVEDEL – AM or PM Time of Day of Highest Vehicle Delay
PKVEDELRK – Peak Vehicle Delay Rank with lowest rank number the most delay
PKVODELRK – Peak Volume Delay Rank with lowest rank number the most delay
AMPKVODEL – AM Peak Volume Delay in HH:MM:SS
PMPKVODEL – PM Peak Volume Delay in HH:MM:SS
HIPKVODEL – Highest of AM or PM Peak Volume Delay in HH:MM:SS
HIPKVOLDEV – Highest of AM or PM Peak Volume Delay in number format
TDPKVODEL – AM or PM Time of Day of Highest Volume Delay
KEYIDX – Unique identifier link to non-spatial data
KEYID – Unique identifier link to non-spatial data (number format)
STATE – AM or PM Time of Day of Highest Volume Delay
COMMENT – Description of the potential cause of the bottleneck as applicable
LOTTRMAXMI – Miles of travel time unreliable for the measure (1.50 or more)
TTTRMAXMI – Miles of truck travel time unreliability for the measure (2.00 or more)
PHEDVAPMI – Total Peak Hours of Excessive Delay weighted by road miles
MAXVCMI – Miles of Travel Demand Model forecasted congestion V/C greater than or equal to 0.85 in 2050
CRINDEXMI – Miles of high crash rate for the measure
CRINDEXMI – Miles of high crash severity for the measure
IMRMAXPMI – CMP Objective Measure score to increase mobility and reliability and meet PM3 targets weighted by road miles, where the maximum score is 4.0
IMRMAXPR – CMP Objective Measure rank of IMRMAXPMI where lower values represent higher scores
IMIAMAXPMI – CMP Objective Measure score to integrate modes and provide transit where it is most needed weighted by road miles, where the maximum score is 2.0
IMIAMAXPR – CMP Objective Measure rank of IMIAMAXPMI where lower values represent higher scores
MRMAXPMI – CMP Objective Measure score to modernize and maintain the existing transportation network, where the maximum score is 1.5
MRMAXPR – CMP Objective Measure rank of MRMAXPMI where lower values represent higher scores
SVRMAXPMI – CMP Objective Measure score to achieve Vision Zero, where the maximum score is 2.0
SVRMAXPR – CMP Objective Measure rank of SVRMAXPMI where lower values represent higher scores
GCMAXPMI – CMP Objective Measure score to maintain the movement of goods by truck and meet PM3 targets, where the maximum score is 1.5
GCMAXPR – CMP Objective Measure rank of GCMAXPMI where lower values represent higher scores
SPMAXPMI – CMP Objective Measure score to maintain and enhance transportation security and prepare for major events, where the maximum score is 1.0
SPMAXPR – CMP Objective Measure rank of SPMAXPMI where lower values represent higher scores
LRPMAXPMI – CMP Objective Measure score to support LRP centers, infill, redevelopment and emerging growth areas, environmental sensitive areas, and Environmental Justice and Equity populations, where the maximum score is 3.0
LRPMAXPR – CMP Objective Measure rank of LRPMAXPMI where lower values represent higher scores
CMPMAXPMI – Total of of the CMP Objective Measure scores, where the maximum score is 15.0
CMPMAXPR – Total CMP Objective Measure rank of CMPMAXPMI where lower values represent higher scores
https://catalog.dvrpc.org/dvrpc_data_license.html
The CMP Corridor and Subcorridor Areas identify congested Corridor and Subcorridor Areas within the DVRPC region, along with areas that are designated as Priority and Major Capacity-Adding Corridor and Subcorridor Areas. A Priority Area indicates greater importance for investments to improve mobility and reliability and a Major SOV Capacity-Adding Area means specific facilities have been deemed appropriate for widening, extending existing roads, or building new roads—though projects will advance based on funding availability and regional priority
https://catalog.dvrpc.org/dvrpc_data_license.html
Focus Intersection Bottlenecks occur along arterials and other non-controlled access roadway facilities, typically at signalized intersections. Focus intersection bottlenecks are identified in the region as ones that have at least one roadway segment approach to an intersection with a peak hour TTI greater than 1.50 or a PTI greater than 3.00 and high peak hour vehicle and volume delays. Intersections with more than one segment approach with high peak hour delays were given added weight to be included as a focus intersection bottleneck.For each bottleneck, peak travel time vehicle and volume delays are summarized for all approach segments that touch the intersection and any other trailing adjacent segments with a TTI of 1.40 or more, or until another bottleneck is encountered.
A total of 299 Focus Intersection Bottlenecks were identified in the DVRPC region: 181 in Pennsylvania and 118 in New Jersey.
These bottlenecks are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time vehicle and volume delays.
CMP Focus Intersection Bottleneck Database Fields
MAPID – Unique map bottleneck identifier by state
MAPID2 – Unique map bottleneck identifier by DVRPC region; NJ bottlenecks start at identifier 300
NAME – Names of the intersecting streets
MUNICIPAL – Municipal where intersection exists; for Philadelphia it is the planning district
COUNTY – County where the intersection exists
AMPKVEDEL – AM Peak Vehicle Delay
PMPKVEDEL – PM Peak Vehicle Delay
HIPKVEDEL – Highest AM or PM Peak Vehicle Delay
TDPKVEDEL – AM or PM Time of Day of Highest Vehicle Delay
PKVEDELRK – Peak Vehicle Delay Rank with lowest rank number the most delay
PKVODELRK – Peak Volume Delay Rank with lowest rank number the most delay
AMPKVODEL – AM Peak Volume Delay in HH:MM:SS
PMPKVODEL – PM Peak Volume Delay in HH:MM:SS
HIPKVODEL – Highest of AM or PM Peak Volume Delay in HH:MM:SS
HIPKVOLDEV – Highest of AM or PM Peak Volume Delay in number format
TDPKVODEL – AM or PM Time of Day of Highest Volume Delay
RELATEID – Unique identifier link to non-spatial data
RELATEIDN – Unique identifier link to non-spatial data (number format)
STATE – AM or PM Time of Day of Highest Volume Delay
LOTTRMAXMI – Miles of travel time unreliable for the measure (1.50 or more)
PHEDVAPMI – Total Peak Hours of Excessive Delay weighted by road miles
MAXVCMI – Miles of Travel Demand Model forecasted congestion V/C greater than or equal to 0.85 in 2050
CRINDEXMI – Miles of high crash rate for the measure
FATMIMI – Miles of high crash severity (fatalities and major injuries) for the measure
IMRMAXPMI – CMP Objective Measure score to increase mobility and reliability and meet PM3 targets weighted by road miles, where the maximum score is 4.0
IMRMAXPR – CMP Objective Measure rank of IMRMAXPMI where lower values represent higher scores
IMIAMAXPMI – CMP Objective Measure score to integrate modes and provide transit where it is most needed weighted by road miles, where the maximum score is 2.0
IMIAMAXPR – CMP Objective Measure rank of IMIAMAXPMI where lower values represent higher scores
MRMAXPMI – CMP Objective Measure score to modernize and maintain the existing transportation network, where the maximum score is 1.5
MRMAXPR – CMP Objective Measure rank of MRMAXPMI where lower values represent higher scores
SVRMAXPMI – CMP Objective Measure score to achieve Vision Zero, where the maximum score is 2.0
SVRMAXPR – CMP Objective Measure rank of SVRMAXPMI where lower values represent higher scores
GCMAXPMI – CMP Objective Measure score to maintain the movement of goods by truck and meet PM3 targets, where the maximum score is 1.5
GCMAXPR – CMP Objective Measure rank of GCMAXPMI where lower values represent higher scores
SPMAXPMI – CMP Objective Measure score to maintain and enhance transportation security and prepare for major events, where the maximum score is 1.0
SPMAXPR – CMP Objective Measure rank of SPMAXPMI where lower values represent higher scores
LRPMAXPMI – CMP Objective Measure score to support LRP centers, infill, redevelopment and emerging growth areas, environmental sensitive areas, and Environmental Justice and Equity populations, where the maximum score is 3.0
LRPMAXPR – CMP Objective Measure rank of LRPMAXPMI where lower values represent higher scores
CMPMAXPMI – Total of of the CMP Objective Measure scores, where the maximum score is 15.0
CMPMAXPR – Total CMP Objective Measure rank of CMPMAXPMI where lower values represent higher scores
https://catalog.dvrpc.org/dvrpc_data_license.html
The CMP Corridor and Subcorridor Areas identify congested Corridor and Subcorridor Areas within the DVRPC region, along with areas that are designated as Priority and Major Capacity-Adding Corridor and Subcorridor Areas. A Priority Area indicates greater importance for investments to improve mobility and reliability and a Major SOV Capacity-Adding Area means specific facilities have been deemed appropriate for widening, extending existing roads, or building new roads—though projects will advance based on funding availability and regional priority
https://catalog.dvrpc.org/dvrpc_data_license.html
Greater Philadelphia is a region blessed with a variety of opportunities to create a connected network of multi-use trails, including both right-of-ways (ROWs) inherited from the area's long history as a hub of railroad activity and greenway corridors that follow the region's creek buffering and park systems. Some of these opportunities have been capitalized upon—a number of significant trail segments already exist or are currently moving toward construction—but \"gaps\" still exist. In May 2012, DVRPC Board Member Terence Farrell and DVRPC Executive Director Barry Seymour joined Philadelphia Mayor Michael Nutter to celebrate the efforts of foundations, governments, non-profits, economic development professionals, and numerous other groups to fill these gaps and create an interconnected network of multi-use trails called The Circuit.
Existing -- These trails are open for use so get out there and explore them
In Progress -- These trails are currently being designed or built.
Pipeline -- DVRPC, local governments, and non-profit organizations are actively working to move these trails forward by conducting studies, acquiring rights-of-way, engaging local communities, and laying the groundwork to obtain funding for future design and construction.
Planned -- These trails are documented in local, county or regional plans. They represent excellent opportunities for regional-scale, multi-use trails. Studies or plans may have been prepared for these trails, but a sponsor is not actively working to move them forward.
https://catalog.dvrpc.org/dvrpc_data_license.html
This table represents vehicle class counts and can be joined to our Traffic Count Locations data using RECORDNUM as the common identifier.
RECORDNUM - record identifier
AADTT - Average Annual Daily Truck Traffic
BUS_PERCENT - Percent buses (Class 4)
SU_TRUCK_PERCENT - Percent single-unit trucks (Classes 5-7)
MU_TRUCK_PERCENT - Percent multi-unit trucks (Classes 8+)
TRUCK_PERCENT - Total truck percent (Classes 5+)
For more information, contact our Office of Travel Monitoring:
Joshua Rocks, Manager
Phone: 215.238.2854 | Email: jrocks@dvrpc.org
https://catalog.dvrpc.org/dvrpc_data_license.html
The points in this layer represent the locations of public airports and select heliports in DVRPC's 28-County Data Service Area.
The \"DVRPC_REG\" field indicates whether a facility is within the DVRPC 9-county region.
Additional data pertaining to these facilities may be sourced from the Federal Aviation Administration (FAA): https://www.faa.gov/
https://catalog.dvrpc.org/dvrpc_data_license.html
This shapefile is intended as a master shapefile of non-Circuit trails in DVRPC's four New Jersey counties. It was compiled by DVRPC using several sources including county open data portals, PDF park and trail maps available online, and Google Maps' trails layer. Trails were corrected and verified manually using leaf-off Nearmap and Google imagery as well as PDF maps and Strava.com where aerial imagery was not sufficient.
trail_name: The trail name field was inherited from individual source shapefiles and edited when necessary. Where possible, trail_name represents the official name of the trail. In the case that official names were not available, descriptive, colloquial names were added.
county: This field refers to the county the trail exists in.
owner: This field refers to the agency/organization responsible for the trail.
surface_type: The surface_type field includes the primary surface material based on the following categories:
Crushed Stone/Gravel, Dirt, Grass, Paved (includes asphalt, concrete, and other hard surfaces), Stone Dust, and Varies (in the case where a trail is composed of more than one surface material)
multi_use: This field indicates whether a trail is suitable for more than one use, e.g. walking, hiking, biking, horseback riding, etc. Where possible, the width of trails was measured to determine compliance with AASHTO standards.
verif_status: This field indicates whether the trail segment is fully verified. For records that were able to be verified, this field contains the value 'Verified'. For records that were not able to be verified, this field is left blank.
verif_by: This refers to the individual who edited and/or verified the trail segment.
verif_date: Date of verification
comments_dvrpc: This field contains notes from the DVRPC planner/analyst about any issues or observations regarding the trail segment and its verification.
https://catalog.dvrpc.org/dvrpc_data_license.html
DVRPC counts bicyclists and pedestrians because count data helps us understand and plan for the role bicyclists and pedestrians play in our transportation network. Similar to how planners use vehicular traffic counts to analyze roadway facilities, our bicycle and pedestrian counting program allows planners to measure existing levels of bicycling and walking, monitor travel trends, plan for new or improved facilities and measure outcomes of bicycle and pedestrian related projects. Our counting program consists of four types of counts: Project Counts, Cyclical Counts, Screenline Counts and Permanent Counts.
https://catalog.dvrpc.org/dvrpc_data_license.html
This layer represents footprints of public airports and selected heliports in DVRPC's 28-County Data Service Area. The features in this layer were generated by digitizing all discernable runways (paved and unpaved), taxiways, aircraft parking areas (aprons and ramps), and aircraft hangers using the best available aerial imagery.
The \"DVRPC_REG\" field indicates whether a facility is within the DVRPC 9-county region. The \"FP_UPDATED\" field indicates the year of aerial imagery that was used to update the facility's footprint.
Additional data pertaining to these facilities may be sourced from the Federal Aviation Administration (FAA): https://www.faa.gov/
https://catalog.dvrpc.org/dvrpc_data_license.html
Title VI of the Civil Rights Act states that \"no person in the United States, shall, on the grounds of race, color, or national origin be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving federal financial assistance.”
Under Title VI of the Civil Rights Act, Metropolitan Planning Organizations (MPOs) are directed to create a method for ensuring that Title VI compliance issues are investigated and evaluated in transportation decision-making. There is additional guidance from the FHWA’s Title VI and Additional Nondiscrimination requirements (2017), and FTA’s Title VI requirements and guidelines (2012). The Title VI Compliance analysis is used throughout DVRPC to demonstrate compliance with Title VI of the Civil Rights Act.
This assessment, called the Title VI Compliance Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:
Youth
Older Adults
Female
Racial Minority
Ethnic Minority
Foreign-Born
Disabled
Limited English Proficiency
Low-Income
Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an Title IV Indicator tract for that group.
For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/
For technical documentation visit DVRPC's GitHub Title VI Indicator repo: https://github.com/dvrpc/titlevi
Note: Tracts with null values should be symbolized as \"Insufficient or No Data\".
https://catalog.dvrpc.org/dvrpc_data_license.html
Title VI of the Civil Rights Act states that \"no person in the United States, shall, on the grounds of race, color, or national origin be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving federal financial assistance.”
Under Title VI of the Civil Rights Act, Metropolitan Planning Organizations (MPOs) are directed to create a method for ensuring that Title VI compliance issues are investigated and evaluated in transportation decision-making. There is additional guidance from the FHWA’s Title VI and Additional Nondiscrimination requirements (2017), and FTA’s Title VI requirements and guidelines (2012). The Title VI Compliance analysis is used throughout DVRPC to demonstrate compliance with Title VI of the Civil Rights Act.
This assessment, called the Title VI Compliance Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:
Youth
Older Adults
Female
Racial Minority
Ethnic Minority
Foreign-Born
Disabled
Limited English Proficiency
Low-Income
Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an Title IV Indicator tract for that group.
For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/
For technical documentation visit DVRPC's GitHub Title VI Indicator repo: https://github.com/dvrpc/titlevi
Note: Tracts with null values should be symbolized as \"Insufficient or No Data\".
https://catalog.dvrpc.org/dvrpc_data_license.html
This two-phase project defines a method to assess the appropriateness of various modes and intensities of transit service throughout the DVRPC region. Phase I consisted of a statistical evaluation and refinement of New Jersey Transit’s existing transit score method. This included a regression analysis to test the relationships between existing variables, additional variables, and transit mode share for the region, the State of New Jersey, and the combined area of the two. Based on the results of this evaluation, a refined transit score equation was derived for congruent use in both New Jersey and Pennsylvania. This DVRPC Transit Score Tool is as follows (all densities gross):
Transit score = [0.41*(Population per acre)] +
[0.09*(Jobs per acre)] +
[0.74*(Zero car households per acre)]
The DVRPC Transit Score Tool was then classified into five score category ranges (from
‘low’ to ‘high’):
Low: < 0.6
Marginal: 0.6 – 1.0
Medium: 1.01 – 2.5
Medium-High: 2.51 – 7.5
High: > 7.5
Dataset for DVRPC 9-county region updated in 2015 with CTPP zonal data
2010 Population source*: CTPP data product based on 2006 – 2010 5-year American Community Survey (ACS) Data
2010 Employment source**: NETS, 2010, adjusted by DVRPC
2010 Zero Car Household source*: CTPP data product based on 2006 – 2010 5-year American Community Survey (ACS) Data
Acreage for TAZ's is total LAND area.
**Note: 2010 Population and Zero Car HH data from US Census Bureau's 2010 Census for Berks County TAZ's
**Note: Employment data for the Berks County TAZ's is an estimate based on DVRPC projections. Employment data for the remaining TAZ's comes from NETS data for 2010, which were then adjusted at the Board's approval.
Source of TAZ boundaries: DVRPC
https://catalog.dvrpc.org/dvrpc_data_license.html
TAZ layer for extended region, aligned with MCDs in DVRPC region using Census block boundaries to divide zones within municipalities (updated in Feb 2015)
The following changes were made in Jan 2015:
Zone 14024 incorporated into Zone 14014. (Quakertown)
Zone 18005 incorporated into Zone 18008. (Trenton)
https://catalog.dvrpc.org/dvrpc_data_license.html
The Delaware Valley Regional Planning Commission (DVRPC) is committed to upholding the principles and intentions of the 1964 Civil Rights Act and related nondiscrimination statutes in all of the Commission’s work, including publications, products, communications, public input, and decision-making processes. Language barriers may prohibit people who are Limited in English Proficiency (also known as LEP persons) from obtaining services, information, or participating in public planning processes. To better identify LEP populations and thoroughly evaluate the Commission’s efforts to provide meaningful access, DVRPC has produced this Limited-English Proficiency Plan. This is the data that was used to make the maps for the upcoming plan.
Census tables used to gather data from the 2019 - 2023 American Community Survey 5-Year Estimates
ACS 2019-2023, Table C16001: Language Spoken at Home by Ability to Speak English for the Population 5 Years and Over.
ACS data are derived from a survey and are subject to sampling variablity.
*Limited English Proficiency (LEP) refers to those persons that speak English less than \"very well\". DVRPC has mapped the below Language Groups for our Plan.
Spanish
Russian
Chinese
Korean
Vietnamese
Source of tract boundaries: US Census Bureau. The TIGER/Line Files
Please refer to U:\\_OngoingProjects\\LEP\\ACS_5YR_C16001_LEP_metadata.xlsx for full attribute look up and fields used in making the DVRPC LEP Map Series.
Please contact Chris Pollard (cpollard@dvrpc.org) should you have any questions about this dataset.
https://catalog.dvrpc.org/dvrpc_data_license.html
The Delaware Valley Regional Planning Commission (DVRPC) is committed to upholding the principles and intentions of the 1964 Civil Rights Act and related nondiscrimination statutes in all of the Commission’s work, including publications, products, communications, public input, and decision-making processes. Language barriers may prohibit people who are Limited in English Proficiency (also known as LEP persons) from obtaining services, information, or participating in public planning processes. To better identify LEP populations and thoroughly evaluate the Commission’s efforts to provide meaningful access, DVRPC has produced this Limited-English Proficiency Plan. This is the data that was used to make the maps for the upcoming plan.
Public Use Microdata Area (PUMA), are geographies of at least 100,000 people that are nested within states or equivalent entities. States are able to delineate PUMAs within their borders, or use PUMA Criteria provided by the Census Bureau.
Census tables used to gather data from the 2019- 2023 American Community Survey 5-Year Estimates
ACS 2019-2023, Table B16001: Language Spoken at Home by Ability to Speak English for the Population 5 Years and Over.
ACS data are derived from a survey and are subject to sampling variablity.
*Limited English Proficiency (LEP) refers to those persons that speak English less than \"very well\". DVRPC has mapped the below Language Groups for our Plan.
Spanish
Russian
Chinese
Korean
Vietnamese
Source of PUMA boundaries: US Census Bureau. The TIGER/Line Files
Please refer to U:\\_OngoingProjects\\LEP\\ACS_5YR_B16001_PUMAs_metadata.xlsx for full attribute loop up and fields used in making the DVRPC LEP Map Series.
Please contact Chris Pollard (cpollard@dvrpc.org) should you have any questions about this dataset.
https://catalog.dvrpc.org/dvrpc_data_license.html
As a part of DVRPC's long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon. DVRPC has updated forecasts through the horizon year of the 2050 Long-Range Plan. The 2050 Version 2.1 Population and Employment Forecasts (2050 Version 2.0, v2.1) were adopted by the DVRPC Board on October 24, 2024, They update the 2050 v1.0 forecasts with a new county-level age-cohort model and new base data from the 2020 Decennial Census, 2020 Bureau of Economic Analysis (BEA), and 2021 National Establishments Time Series (NETS). The age-cohort model calculates the future population for five-year age-sex cohorts using the 2020 Census base population and anticipated birth, death, and migration rates. These anticipated rates were developed using historic birth and death records from New Jersey and Pennsylvania state health department data, as well as historic net migration data, calculated from decennial census data. Employment forecasts were developed by multiplying population forecasts by a ratio of working age population to jobs, calculated from 2022 ACS and BEA data.
The municipal and TAZ forecasts use the growth factors from the v1.0 forecasts, scaled to the new county and regional population totals from the age-cohort model. While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC's travel demand model, and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request.
DVRPC has prepared regional- and county-level population and employment forecasts in five-year increments for years 2020-2050. 2019 land use model results are also available.
For further information on the forecasting process and methodologies, see 2050 v2.1 Population and Employment Forecasts Analytical Data Report at (https://www.dvrpc.org/products/26105).
https://catalog.dvrpc.org/dvrpc_data_license.html
As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon. DVRPC has updated forecasts through the horizon year of the 2050 Long-Range Plan. The 2050 Version 2.0 Population and Employment Forecasts (2050 Version 2.0, v2.0) were adopted by the DVRPC Board on October 24, 2024, They update the 2050 v1.0 forecasts with a new county-level age-cohort model and new base data from the 2020 Decennial Census, 2020 Bureau of Economic Analysis (BEA), and 2021 National Establishments Time Series (NETS). The age-cohort model calculates future population for five year age-sex cohorts using the 2020 Census base population, and anticipated birth, death, and migration rates. These anticipated rates were developed using historic birth and death records from New Jersey and Pennsylvania state health department data, as well as historic net migration data, calculated from decennial census data. Employment forecasts were developed by multiplying population forecasts by a ratio of working age population to jobs, calculated from 2022 ACS and BEA data.
The municipal and TAZ forecasts use the growth factors from the v1.0 forecasts, scaled to the new county and regional population totals from the age-cohort model. While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC’s travel demand model, and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request.
DVRPC has prepared regional- and county-level population and employment forecasts in five-year increments for years 2020–2050. 2019 land use model results are also available. A forthcoming Analytical Data Report will document the forecasting process and methodologies.
https://catalog.dvrpc.org/dvrpc_data_license.html
As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon, or to the horizon year of the long-range plan. Allocation of growth is forecasted using a land use model, UrbanSim, and working closely with member county planning staffs. DVRPC has prepared regional, county, and municipal-level population and employment forecasts in five-year increments through 2050, using 2015 Census population estimates and 2015 National Establishments Time Series (NETS) employment data as the base. A forthcoming Analytical Data Report will document the forecasting process and methodologies.While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC’s travel demand model and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request. Note: while 2019 land use model results are provided, the forecast was only adopted for 2015, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.
https://catalog.dvrpc.org/dvrpc_data_license.html
As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon, or to the horizon year of the long-range plan. Allocation of growth is forecasted using a land use model, UrbanSim, and working closely with member county planning staffs. DVRPC has prepared regional, county, and municipal-level population and employment forecasts in five-year increments through 2050, using 2015 Census population estimates and 2015 National Establishments Time Series (NETS) employment data as the base. A forthcoming Analytical Data Report will document the forecasting process and methodologies.This data provides the municipal level forecast for our 8 counties outside of Philadelphia, and Planning District-level forecast within Philadelphia. Note: while 2019 land use model results are provided, the forecast was only adopted for 2015, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.
https://catalog.dvrpc.org/dvrpc_data_license.html
As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon. DVRPC has updated forecasts through the horizon year of the 2050 Long-Range Plan. The 2050 Version 2.0 Population and Employment Forecasts (2050 Version 2.0, v2.0) were adopted by the DVRPC Board on October 24, 2024, They update the 2050 v1.0 forecasts with a new county-level age-cohort model and new base data from the 2020 Decennial Census, 2020 Bureau of Economic Analysis (BEA), and 2021 National Establishments Time Series (NETS). The age-cohort model calculates future population for five year age-sex cohorts using the 2020 Census base population, and anticipated birth, death, and migration rates. These anticipated rates were developed using historic birth and death records from New Jersey and Pennsylvania state health department data, as well as historic net migration data, calculated from decennial census data. Employment forecasts were developed by multiplying population forecasts by a ratio of working age population to jobs, calculated from 2022 ACS and BEA data.
The municipal and TAZ forecasts use the growth factors from the v1.0 forecasts, scaled to the new county and regional population totals from the age-cohort model. While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC’s travel demand model, and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request.
DVRPC has prepared regional- and county-level population and employment forecasts in five-year increments for years 2020–2050. 2019 land use model results are also available. A forthcoming Analytical Data Report will document the forecasting process and methodologies.
https://catalog.dvrpc.org/dvrpc_data_license.html
This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the urban footprint. There are 2,644 Urban Areas (UAs) in this data release (2025 release downloaded via the Census Mapping Files webpage) with either a minimum population of 5,000 or a housing unit count of 2,000 units. Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.
Public domain
DVRPC manually adjusted TIGER MCD dataset by adding Philly Planning District boundaries. To get the Planning District boundaries, census tracts were dissolved into most appropriate CPA geography. (Planning Districts are sometime referred to as County Planning Areas at DVRPC)
Updated in Jan 2023 to reflect Pine Valley/Pine Hill merger (Pine Valley was incorporated into Pine Hill)
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.
See https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc_Ch3.pdf for more information
Downloaded from: https://www2.census.gov/geo/tiger/TGRGDB20/ on August 13, 2020
Public domain
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
Census Designated Places (CDPs) are the statistical counterparts of incorporated places, and are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries usually are defined in cooperation with local or tribal officials and generally updated prior to each decennial census. These boundaries, which usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity boundary, have no legal status, nor do these places have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary. CDPs must be contained within a single state and may not extend into an incorporated place. There are no population size requirements for CDPs, but they must include some residential population or housing.
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/PLACE/ on June 22, 2023
Public domain
Protected Open Space for the DVRPC Region as of 2020. DVRPC maintains an inventory of protected public and private open space in the Delaware Valley. The inventory tracks all publicly-owned open space, preserved farmland, and non profit protected open space. State, county and municipal programs preserve farms by purchasing development rights with public funds. Non-profits (land trusts and conservancies) protect privately owned open space lands by purchasing easements or by acquiring land outright with a combination of public and private funds. This file is a compilation from the following sources and is not necessarily parcel-based: Bucks County Planning Commission; Burlington County Department of Economic Development and Regional Planning; Camden County Division of Open Space and Farmland Preservation; Camden County Improvement Authority; Chester County Planning Commission; Delaware County Planning Department; Gloucester County Planning Division; Mercer County Planning Division; Montgomery County Planning Commission; Natural Lands Trust; New Jersey Conservation Foundation; New Jersey Department of Environmental Protection; New Jersey State Agriculture Development Committee; Pennsylvania Department of Conservation and Natural Resources; Philadelphia City Planning Commission; Philadelphia Department of Parks and Recreation In the cases where land had multiple owners, the following heirarchy was applied: 1. Preserved Farmland 2. Federal 3. State 4. County 5. Municipal 6. Nonprofit
https://catalog.dvrpc.org/dvrpc_data_license.html
FREIGHT CENTERS
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
Greater Philadelphia has 67 identified “Freight Centers,” which are major focal points and trip generators. The Freight Centers are a testament to the broad range of manufacturing, warehousing, transportation, quarry, and utility activity that occurs locally. They also highlight the strong linkage between freight transportation and job retention and creation.
https://catalog.dvrpc.org/dvrpc_data_license.html
Energy Use and Greenhouse Gas Emissions Inventory for Greater Philadelphia
https://www.dvrpc.org/Products/18018
Energy Use is defined as the BBTU (Billion British Thermal Units) equivalent of energy consumed by end point users. For electricity this is the BBTU equivalent of the electricity at point of consumption, not the heat content of the fuels used for electricity generation.
Emissions (MTCO2E - Metric tons of carbon dioxide equivalent) from the following sectors and fuel sources are not included in this allocation: enteric fermentation and manure management; commerical and general aviation; marine and port-related activity; off-road vehicles and equipment; freight and intercity passenger rail; coal; fuels used predominently in the industrial sector (e.g., petroleum coke); and land use, land use change, and forestry.
Population and Employment data used in this analysis came from DVRPC's Municipal-level Poulation and Employment Forecasts.
GEOID_10 - Unique GIS ID for Municipality.
ACRES - Total Municipal Acres.
USEACRES - Energy Use per Acres (BBTUs).
EUPOPEM - Energy Use per Person/Job (BBTUs).
EMIACRE - GHG Emissions per Acre (MTCO2E).
EMPOPE - GHG Emissions per Person/Job (MTCO2E).
EMSQACRE - GHG Emissions/Sequestration including Land Use, Land Use Change, and Foresty per Acres (MTCO2E).
EMSQPOPE - GHG Emissions/Sequestration including Land Use, Land Use Change, and Foresty per Person/Job (MTCO2E).
DVRPC estimates that net GHG emissions decreased 10 percent from 2010 to 2015, from emissions equivalent to the release of 82 million metric tons of carbon dioxide (MMTCO2e) to about 74 MMTCO2e. GHG emissions were 21 percent lower in 2015 than in 2005. The top three drivers of reductions between 2010 and 2015 were, in decreasing levels of significance, change in electricity generation mix (a cleaner electricity grid due to the continued switch from coal to natural gas), decreased on-road emissions per mile traveled, and decreased electricity consumption per household. Continued reductions will require sustained, concerted and aggressive action at the household, firm, community, regional, state, national, and global level, as well as continued technical advancement. More information can be found at www.dvrpc.org/EnergyClimate/Inventory.htm.
Please refer to DVRPC's Energy Use and Greenhouse Gas Emissions Inventory for Greater Philadelphia: Methods and Sources for further details on analysis methods and data sources.
https://catalog.dvrpc.org/dvrpc_data_license.html
MAJOR REGIONAL TRANSPORTATION PROJECTS (POINTS) - Updated 7/2024
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
Major Regional Projects are large-scale projects that will have a significant impact on regional travel. Almost all system expansion projects are Major Regional Projects, as are reconstruction projects on the region’s freeways. Major Operational Improvement initiatives are also listed in the Plan. For the sake of brevity, smaller-scale projects that were identified in the needs assessment are not listed in the Plan document. Instead, the various funding categories in the Plan serve as placeholders for their funding, and they may be explicitly listed in future iterations of the TIP.
https://catalog.dvrpc.org/dvrpc_data_license.html
MAJOR REGIONAL TRANSPORTATION PROJECTS (LINES) - Updated 7/2024
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
Major Regional Projects are large-scale projects that will have a significant impact on regional travel. Almost all system expansion projects are Major Regional Projects, as are reconstruction projects on the region’s freeways. Major Operational Improvement initiatives are also listed in the Plan. For the sake of brevity, smaller-scale projects that were identified in the needs assessment are not listed in the Plan document. Instead, the various funding categories in the Plan serve as placeholders for their funding, and they may be explicitly listed in future iterations of the TIP.
https://catalog.dvrpc.org/dvrpc_data_license.html
PLANNED CENTERS, CORE CITIES & NEIGHBORHOOD CENTERS
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
The concept of Centers is the cornerstone of the Connections 2050 Long-Range Plan. Centers provide a focal point in the regional landscape that recognizes the regional and local significance of places, while reinforcing a sense of community for local residents. Centers serve as a basis for organizing and focusing the development landscape and provide a framework for the most efficient provision of supportive infrastructure systems, including water, sewer, and transportation. By concentrating growth around and within Centers, the region can both preserve open space and reduce infrastructure costs. The densities and mixed uses inherent within Centers can enhance the feasibility of walking, bicycling, and public transit as alternatives to the automobile.
https://catalog.dvrpc.org/dvrpc_data_license.html
GREENSPACE NETWORK
The Plan proposes linking and expanding
the region’s existing protected natural
areas into a Greenspace Network, where
parks, forests, meadows, stream corridors,
and
floodplains are joined together in an
interconnected system. The Greenspace
Network is based on the twin principles
of protecting core natural resource areas
and linking them with greenways to create
a connected system of naturally vegetated
open space spanning urban, suburban, and
rural areas.
For more information, see Chaper 4 of Connections 2040 document
**PLEASE NOTE: GEOGRAPHY IS FOR ILLUSTRATIVE PURPOSES ONLY AND IS DESIGNED TO BE USED AT THE DVRPC REGIONAL SCALE ONLY**
https://catalog.dvrpc.org/dvrpc_data_license.html
The dataset is an update of a feature class representing four different Pinelands boundaries in Southern New Jersey. It represents the Pinelands Preservation Area District and the Pinelands Protection Area as per the New Jersey Pinelands Protection Act of 1979 (P.L. 1979, Chapter 111, approved June 28, 1979), the Pinelands Comprehensive Management Plan area (N.J.S.A. 13:18A-1 et. seq.), and the Pinelands National Reserve boundary as per the National Parks and Recreation Act of 1978 (P.L. 95-625, Sec. 502). Originally the dataset was created manually by interpreting text documents which described the boundary lines and drafting this information onto mylar using USGS photo quads as a base. In 1994, the coverage was digitized and converted to New Jersey State Plane NAD 83 Feet. In 2002, the boundaries were adjusted to a seamless parcel layer. In 2014/15 the boundaries were adjusted to The State of New Jersey Composite of Parcels Data layer developed by the New Jersey Office of Information Technology and an updated Pinelands Management Area layer to make the lines coincidental where applicable. The composite parcel layer is \"In work\", so changes made in the composite parcel layer after the release of this data set will not be reflected in this version. The boundaries are intended to provide reasonable representation of the boundaries for planning purposes. They are not survey grade. The current geometry is not static and is prone to change.
**Downloaded March 2020**
The geodetic accuracy and precision of data contained within the coverage has not been developed nor verified by a licensed surveyor and shall not be or is intended to be used in matters requiring delineation of true ground horizontal and/or vertical controls. Acknowledgement of the data set provider, New Jersey Pinelands Commission, would be appreciated for maps, data or other products derived from this data set. See Legal Constraints Use limitation (Distribution Liability) for legal disclaimer.
LAND USE VISION
The Connections 2050 Land Use Vision defines a regional visualization for Centers-based development and the preservation of agricultural and natural lands. The Land Use Vision divides the entire region up into four layers: Infill and Redevelopment areas, Emerging Growth areas, Rural Resource Lands, and the Greenspace Network. Overlaid on these land use areas are over 125 Centers, which are points of more concentrated development around which new development should be focused.
GREENSPACE NETWORK
The Plan proposes linking and expanding the region’s existing protected natural areas into a Greenspace Network, where parks, forests, meadows, stream corridors, and floodplains are joined together in an interconnected system. The Greenspace Network is based on the twin principles of protecting core natural resource areas and linking them with greenways to create a connected system of naturally vegetated open space spanning urban, suburban, and rural areas.
**PLEASE NOTE: GEOGRAPHY IS FOR ILLUSTRATIVE PURPOSES ONLY AND IS DESIGNED TO BE USED AT THE DVRPC REGIONAL SCALE ONLY**
attribute field
name = Greenspace Network name
lup_type = Land Use Vision Type
label_id = Used for cartographic labeling of Greenspace Network
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents: (1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan. (2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
7/25/23 - Land Use Vision GIS dataset was merged with Greenspace Network
https://catalog.dvrpc.org/dvrpc_data_license.html
Major Regional Projects are large-scale projects that will have a significant impact on regional travel. Almost all system expansion projects are Major Regional Projects, as are reconstruction projects on the region’s freeways. Major Operational Improvement initiatives are also listed in the Plan. For the sake of brevity, smaller-scale projects that were identified in the needs assessment are not listed in the Plan document. Instead, the various funding categories in the Plan serve as placeholders for their funding, and they may be explicitly listed in future iterations of the TIP.
Updated 7/2025
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
https://catalog.dvrpc.org/dvrpc_data_license.html
PLANNING CENTERS
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
The Connections 2050 Long-Range Plan identifies four types of Centers: Suburban Centers, Town Centers, Rural Centers, and Planned Centers. The characteristics of each type are as follows:
SUBURBAN CENTERS
Are regionally significant; While not necessarily single municipalities, are perceived as single “places;” Are suburban in character; Are less dense than town centers; Lack the integrated mix of uses found in town centers;
Are defined primarily by a concentration and variety of office, retail, professional, and light industrial uses, and generally have more jobs than residents; and are generally auto dependent rather than transit oriented or pedestrian scale.
TOWN CENTERS
Have a mixture of high-density residential and commercial land use; Have an integrated mix of land uses;Have a unique history, character, and sense of place; Are of relatively higher density than their surrounding land uses;
Have a distinct downtown/main street area surrounded by relatively dense residential development;
Are pedestrian friendly and often transit oriented; and Are surrounded by suburban land uses.
RURAL CENTERS
Have a minimum density of six people and three employees per developed acre; Have an integrated mix of land uses; Have a unique history, character, and sense of place; Are of relatively higher density than the surrounding area;
Have a distinct downtown/main street (though smaller than a town center); and Are surrounded by rural and agricultural land uses.
PLANNED CENTERS
Are planned town-center-type developments on greenfields in Growing Suburbs or Rural Areas or through redevelopment on greyfields and/or brownfields in Developed Communities; and Have plans that call for village-type development, incorporating mixed, integrated land uses, relatively high densities, and pedestrian connections.
https://catalog.dvrpc.org/dvrpc_data_license.html
Update: Connections 2050 Plan Centers at the heart of its vision for the region’s future, using them as a foundational framework to guide regional growth, development, and transportation investment in smart, efficient way to make the most of existing assets. By linking land use and transportation planning, Plan Centers help direct resources to where they can have the greatest impact, cost-effectively. These are locations of local government, essential services, economic activity, and community life. The core variables that define Centers are transit access and development density. See Appendix B of Update Connections 2050 for more information on Centers typologies (www.dvrpc.org/plan).
The six Center categories are defined as follows:
1. Regional Core—The central business district of the region, encompassing Greater Center City Philadelphia, University City, and downtown Camden. Geography is determined by the highest level of existing density.
2. City Centers—Dense, mixed-use neighborhoods or urban core cities with strong transit access and walkable main streets. These serve as models for urban development.
3. Town Centers—Compact, mixed-use boroughs or census-designated places with existing or historical transit service and a walkable main street. These Centers represent ideal development in a regional (non-urban) context.
4. Suburban Centers—Areas with higher density than their surroundings or clusters of jobs and commercial activity, but limited or no direct transit access. These Centers often face a jobs-housing imbalance and are key locations for multimodal and quality-of-life investments.
5. TOD Opportunity Centers—Areas with access to regional rail or strong transit service that could evolve into Town Centers through Transit-Oriented Development (TOD) strategies. These strategies may include increasing density, improving walkability, and enhancing multimodal connections.
6. Village Centers—Moderate-density, mixed-use clusters typically located in rural or agricultural settings. They often feature an identifiable downtown or main street and are important nodes of local cultural and economic activity.
https://catalog.dvrpc.org/dvrpc_data_license.html
*USE geoid20 TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
Blocks (Census Blocks or Tabulation Blocks) are statistical areas bounded by visible features, such as streets, roads, streams, and railroad tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county limits and short line-of-sight extensions of streets and roads. Generally, blocks are small in area; for example, a city block bounded on all sides by streets. Blocks in suburban and rural areas may be larger, more irregular in shape, and bounded by a variety of features, such as roads, streams, and transmission lines. In remote areas, blocks may even encompass hundreds of square miles. Blocks cover the entire territory of the United States, Puerto Rico, and the Island Areas. Blocks nest within all other tabulated census geographic entities at the time of the decennial census and are the basis for all tabulated data from that census. Census Block Numbers—Blocks are numbered uniquely with a four-digit census block number from 0000 to 9999 within census tract, which nest within state and county. The first digit of the census block number identifies the block group. Block numbers beginning with a zero (in Block Group 0) are intended to include only water area, but not all water-only blocks have block numbers beginning with 0 (zero).
Downloaded from https://www2.census.gov/geo/tiger/TIGER2020/TABBLOCK20 on May 17, 2023
Public domain
Current (2021) and projected numbers of Plug-in Electrical Vehicles (PEVs) at the census block group level for the Delaware Valley region. The projected PEV distribution is based on a scenario in which 5 percent of passenger vehicles in the Greater Philadelphia region (or about 200,000 vehicles) are PEVs.
Also includes data projecting workplace charging demand in number of charging events and kilowatt-hours of demand by census block group based on the aforementioned PEV projections for the following three scenarios:
- workplace charging is free (free charging),
- workplace charging is twice the cost of home charging (paid charging).
The following datasets were used to obtain these results using the EV Planning Toolkit:
- 2021 vehicle registration data provided by the Pennsylvania Department of Transportation
- 2021 vehicle registration data from the New Jersey Motor Vehicle Commission provided by the New Jersey Department of Environmental Protection
- LEHD Origin-Destination Employment Statistics (LODES), version 7
- Block group-to-block group commuting distances from DVRPC's Travel Improvement Model 2.1, accessed December 2017.
- Block group-to-block group commuting distances calculated from Esri’s ArcGIS Network Analyst, used in 2017.
Field | Alias | Description |
GEOID10 | GEOID10 | Census Block Group identifier |
Mun_Name | Municipality Name | The name of the municipality in which the Block Group lies |
GEOID_Muni | GEOID of Municipality | Municipality identifier |
SQMI_LAND | Land area | Square miles of land area |
POP | Population | Number of people |
HOUSUNIT | Housing Units | Number of housing units |
JOBS | Jobs | Number of jobs |
PASS_VEH | Number of Passenger Vehicles | Number of passenger vehicles per block group as of 2021 |
CurPEV | Current Number of PEVs | Number of PEVs per block group as of 2021 |
FutPEV | Projected Number of PEVs | Number of projected PEVs per block group at 5% regional penetration |
CuPEV_SM | Current PEVs per square mile | Number of PEVs per square mile in the block group as of 2021 |
FUPEV_SM | Projected PEVs per square mile | Number of projected PEVs per square mile per block group at 5% regional penetration |
CuPEVPop | Current number of PEVs per 100 people | Number of PEVs per 100 people per block group as of 2021 |
FuPEVPop | Projected number of PEVs per 100 people | Number of projected PEVs per 100 people per block group at 5% regional penetration |
CuPEV_HU | Current number of PEVs per 100 housing units | Number of PEVs per 100 housing units per block group as of 2021 |
FuPEV_HU | Projected number of PEVs per 100 housing units | Number of projected PEVs per 100 housing units per block group at 5% regional penetration |
PerCuPEV | Current Percentage of Passenger Vehicles That Are PEVs | Percentage of total passenger vehicles that are PEVs per block group as of 2021 |
PerFuPEV | Projected Percentage of Passenger Vehicles That Are PEVs | Percentage of total passenger vehicles that are projected to be PEVs per block group at 5% regional penetration |
FC_KD | Free Charging - kWh of Demand | Kilowatt-hours of workplace charging demand per day per block group when workplace charging is free at 5% regional PEV penetration |
FC_CE | Free Charging - Number of Charging Events | Number of workplace charging events per day per block group when workplace charging is free at 5% regional PEV penetration |
FC_KD_SM | Free Charging - kWh of Demand per sq. mi. | Kilowatt-hours of workplace charging demand per day per square mile per block group when workplace charging is free at 5% regional PEV penetration |
FC_CE_SM | Free Charging - Charging Events per sq. mi. | Number of workplace charging events per day per square mile per block group when workplace charging is free at 5% regional PEV penetration |
FC_KPE | Free Charging - kWh per charging event | Kilowatt-hours per workplace charging event per block group when workplace charging is free at 5% regional PEV penetration |
FC_KD_JB | Free Charging - kWh of Demand per Job | Kilowatt-hours of workplace charging demand per day per job per block group when workplace charging is free at 5% regional PEV penetration |
FC_CE_JB | Free Charging - Charging Events per Job | Number of workplace charging events per job per block group when workplace charging is free at 5% regional PEV penetration |
PC_KD | Paid Charging - kWh of Demand | Kilowatt-hours of workplace charging demand per day per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration |
PC_CE | Paid Charging - Number of Charging Events | Number of workplace charging events per day per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration |
PC_KD_SM | Paid Charging - kWh of Demand per sq. mi. | Kilowatt-hours of workplace charging demand per day per square mile per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration |
PC_CE_SM | Paid Charging - Charging Events per sq. mi. | Number of workplace charging events per day per square mile per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration |
PC_KPE | Paid Charging - kWh per charging event | Kilowatt-hours per workplace charging event per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration |
PC_KD_JB | Paid Charging - kWh of Demand per Job | Kilowatt-hours of workplace charging demand per day per job per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration |
PC_CE_JB | Paid Charging - Charging Events per Job | Number of workplace charging events per job per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration |
If you have any questions regarding this analysis or datasets used in the analysis, please contact: Sean Greene, Manager, Air Quality Programs | sgreene@dvrpc.org | (215) 238-2860
https://catalog.dvrpc.org/dvrpc_data_license.html
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) five-digit Zonal Improvement Plan (ZIP) Code service routes that the Census Bureau creates using whole blocks to present statistical data from censuses and surveys. The Census Bureau defines ZCTAs by allocating each block that contains addresses to a single ZCTA, usually to the ZCTA that reflects the most frequently occurring ZIP Code for the addresses within that tabulation block. Blocks that do not contain addresses, but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA; those surrounded by multiple ZCTAs are added to a single ZCTA based on limited buffering performed between multiple ZCTAs. The Census Bureau identifies five-digit ZCTAs using a five-character numeric code that represents the most frequently occurring USPS ZIP Code within that ZCTA, and this code has a fixed length of five digits and may contain leading zeros. Not all ZIP Codes in use by the USPS may have a ZCTA delineated to represent them, The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. In addition, the ZCTA delineation process primarily uses residential addresses and has a bias towards ZIP Codes used for city-style mail delivery, thus there may be ZIP Codes that are primarily nonresidential or used for PO boxes only that may not have a corresponding ZCTA. ZIP Code is a trademark of the U.S. Postal Service.
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/ZCTA520/ on June 22, 2023
Public domain
Traditional Neighborhood Development (TND) generally describes a mixed-use community that is walkable and compact, with residential, retail, office, and civic buildings in close proximity to one another. These developments get their name from the traditional planning principles that guided the development of many older small towns and neighborhoods around the country. Unlike many conventional suburban developments, TNDs typically include a range of housing types and provide residents with the option of walking, biking, or driving to places within their neighborhood. Some TNDs include a transit connection while others do not.
A number of states have written formal TND ordinances into their planning and zoning enabling acts. The Pennsylvania Municipalities Planning Code (MPC) includes enabling legislation for TND (Article VII-A: 2000). In New Jersey, the standard method for creating large scale, single-developer, traditional development is through a Planned Unit Development (PUD).
------
The Smart Growth Project Database is an application that tracks a variety of noteworthy development projects throughout the Greater Philadelphia Region. The database focuses on three types of Smart Growth projects: development near transit, traditional neighborhood development (TND), and conservation subdivisions. These types of development were selected because they often embody several of the core principles of Smart Growth:
Providing a mix of land uses,
Creating a range of housing options,
Preserving open space, farmland, and critical environmental areas,
Creating walkable neighborhoods, and
Providing a variety of transportation choices.
This database is meant to serve as a quick and convenient reference tool for planners, local officials, and citizens who are interested in Smart Growth. It includes projects that are proposed, in progress, and complete. The details of proposed developments are subject to change and projects will be updated and added as new information becomes available. The inclusion of any particular project within this application does not serve as an endorsement by DVRPC or its partner agencies. Furthermore, this application focuses on recent developments because they are the most relevant to today's real estate and economic climate.
https://catalog.dvrpc.org/dvrpc_data_license.html
The Update: Connections 2050 Land Use Vision defines a regional visualization for Centers-based development and the preservation of agricultural and natural lands. The Land Use Vision divides the entire region up into four layers: Infill and Redevelopment areas, Emerging Growth areas, Rural Resource Lands, and the Greenspace Network. Overlaid on these land use areas are 176 Centers, which are points of more concentrated development around which new development should be focused. Plan Centers provide a structure for coordinating land use with infrastructure investments, enabling the most efficient provision of transportation, water, sewer, and other critical services. Prioritizing growth in and around Centers supports the Plan’s goals by preserving open space, protecting natural resources, and fostering vibrant, walkable, and connected communities. By emphasizing Centers, the Plan promotes an accessible and more efficient regional development pattern that enhances quality of life for all.
GREENSPACE NETWORK The Plan proposes linking and expanding the region’s existing protected natural areas into a Greenspace Network, where parks, forests, meadows, stream corridors, and floodplains are joined together in an interconnected system. The Greenspace Network is based on the twin principles of protecting core natural resource areas and linking them with greenways to create a connected system of naturally vegetated open space spanning urban, suburban, and rural areas.
PLEASE NOTE: GEOGRAPHY IS FOR ILLUSTRATIVE PURPOSES ONLY AND IS DESIGNED TO BE USED AT THE DVRPC REGIONAL SCALE ONLY
attribute fields
name = Greenspace Network name
lup_type = Land Use Vision Type
label_id = Used for cartographic labeling of Greenspace Network
All Update: Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents: (1) Update: Connections 2050 Summary Document (https://www.dvrpc.org/products/25137b) identifies the vision, goals, strategies, and a summary of the financial plan. (2) The Update: Connections 2050 Appendices (www.dvrpc.org/Products/25140b) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan. 7/25/23 - Land Use Vision GIS dataset was merged with Greenspace Network
https://catalog.dvrpc.org/dvrpc_data_license.html
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity that can be updated by local participants prior to each decennial census as part of the Census Bureau’s Participant Statistical Areas Program (PSAP). The Census Bureau delineates census tracts in situations where no local participant responded or where state, local, or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of statistical data.
Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census. Census tracts occasionally are split due to population growth or merged as a result of substantial population decline.
Census tract boundaries generally follow visible and identifiable features. They may follow nonvisible legal boundaries, such as minor civil division (MCD) or incorporated place boundaries in some states and situations, to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. Tribal census tracts are a unique geographic entity defined within federally recognized American Indian reservations and off-reservation trust lands and can cross state and county boundaries. The tribal census tracts may be completely different from the standard county-based census tracts defined for the same area. (see “Tribal Census Tract”).
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/TRACT/ on June 22, 2023
Public domain
As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon, or to the horizon year of the long-range plan. Allocation of growth is forecasted using a land use model, UrbanSim, and working closely with member county planning staffs. DVRPC has prepared regional, county, and municipal-level population and employment forecasts in five-year increments through 2050, using 2015 Census population estimates and 2015 National Establishments Time Series (NETS) employment data as the base. A forthcoming Analytical Data Report will document the forecasting process and methodologies. This data provides the county-level forecast. Note: while 2019 land use model results are provided, the forecast was only adopted for 2015, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.
https://catalog.dvrpc.org/dvrpc_data_license.html
The Development Intensity Zones (DIZ) geography as a block group-based assessment of existing development, largely formed from two analysis dimensions—density and proximity. Zones are classified from least to greatest development intensity.
https://github.com/dvrpc/development-intensity-zones/blob/main/images/image.png
Protected zones are within block group-based geographies but are removed from density and proximity index calculations. DIZ also uses the presence of structures (i.e. housing units, commercial and industrial square footage) to calculate these indices. The DIZ covers the 9-county DVRPC region, along with surrounding counties for greater context beyond the region. The DIZ can be used for a variety of purposes. It provides simple classifications of the region’s areas by which data can be summed and analyzed. It can be used for policy applications, such as incentivizing where future growth or investment goes based on smart growth principles. It’s important to note that the DIZ classifications are based on the current presence of development, largely using housing units for residential intensity and commercial square feet for non-residential intensity. This captures used space and space with potential for use (vacant space) and the presence of infrastructure to build buildings, but if one’s analysis is more suited for the presence of people, other metrics such as population, households, and employment may be more appropriate. If future growth in development is desirable for one’s analysis, the DIZ does not capture that. For more information, see Methodology.
Version 1 release Sept. 2023
https://catalog.dvrpc.org/dvrpc_data_license.html
Every five years, since 1990, the Delaware Valley Regional Planning Commission has produced a GIS Land Use layer for its 9-county region. This shapefile contains 1990 Land Use.
https://catalog.dvrpc.org/dvrpc_data_license.html
2024 Updates
DVRPC performed an analysis to create the CHSTP Priority Score, a layer that helps users visualize where there is a potentially high need to improve transit service for vulnerable populations to reach essential services in the Greater Philadelphia region.
As a metropolitan planning organization, Delaware Valley Regional Planning Commission (DVRPC) is responsible for updating the region's Coordinated Human Services Transportation Plan (CHSTP). The CHSTP update engaged a variety of stakeholders to identify unmet needs and service gaps, recommend innovative transportation access solutions, and empower communities to climb \"ladders of opportunity\" toward greater social and economic mobility.
As part of the CHSTP update, DVRPC created the CHSTP Priority Score Map Toolkit. This interactive web-based tool demonstrates disparities in access to essential services like hospitals, health clinics, recreational spaces, senior centers, and more in the Greater Philadelphia region. Users can view layers representing different datasets including the locations of essential services; bus routes, transit stops, and rail lines; transit walksheds; distributions of vulnerable populations like seniors, households in poverty, and people with disabilities; and areas where transit access is low.
https://github.com/dvrpc/gis-chstp includes all code for the analysis.
Below are several of the analyses included in this dataset.
Vulnerable Populations answers the question, “Who lives here?” and highlights populations in need.
Essential Services answers the question, “Where do people need to go?” and highlights areas with more services in the region.
Population-Services Mismatch answers the question, “Where is there a gap between areas of need and essential services?” This layer highlights areas where there are higher numbers of vulnerable populations but fewer essential services and vice versa.
Transit Accessibility answers the question, “How is transit service distributed?” and highlights areas in the region with lower transit accessibility.
Priority Score answers the question, “Where can transit service be improved to help vulnerable populations access essential services?” This layer, the result of our analysis, highlights areas with higher numbers of vulnerable populations or essential services, but lower transit accessibility and vice versa.
Name | Field | Source | Additional Info | Notes | |
VULNERABLE POPULATIONS | Total Number of Households | hh | ACS | B11001_001E | American Community Survey 5-Year Data (2018-2022) |
Total Number of People | pop | ACS | B01003_001E | American Community Survey 5-Year Data (2018-2022) | |
Households with 1 or More People with Disability | hh1_dis | ACS | B22010, Estimate; Household received Food Stamps/SNAP in the past 12 months | American Community Survey 5-Year Data (2018-2022) | |
Number of Households Below Poverty Line | hh_pov | ACS | B17017 Estimate; Income in the past 12 months below poverty level: | American Community Survey 5-Year Data (2018-2022) | |
People 65 or Older | _65older | ACS | B01001, summarized by sex and age groups | American Community Survey 5-Year Data (2018-2022) | |
Vulnerable Population Rank | vul_pop_rank | DVRPC | calculated | ||
ESSENTIAL SERVICES | Activity Centers for Seniors or Disabled | ss_cnt | Overture Map | Overture Map (2024) | |
Food Stores | food_cnt | Overture Map | Overture Map (2024) | ||
Health Care Facilities | hc_cnt | Overture Map | Overture Map (2024) | ||
Number of Educational Institutions | school_cnt | NCES | https://nces.ed.gov/collegenavigator/ ; https://nces.ed.gov/surveys/pss/privateschoolsearch/ ; https://nces.ed.gov/ccd/schoolsearch/ | ||
Parks/Open Space Present | os_check | DVRPC | DVRPC Parks/Open Space (2016) | ||
Trails | trail_cnt | DVRPC | DVRPC Circuit Trails (2020) | ||
Essential Services Total | es_sum | DVRPC | calculated | ||
Jobs | sum_jobs | Census LODES | Census LODES | ||
Essential Services Rank | es_rank | DVRPC | calculated | ||
Access Gap | access_gap_rank | DVRPC | calculate the difference of vulnerable population rank and essential service rank for access gap | ||
TRANSIT ACCESSIBILITY | Transit Accessibilty Zones | t_45min_zone_cnt ; t_zone_quantile | DVRPC | DVRPC Travel Models (2023), How many areas a person could access in a 45 minute transit trip | |
Essential Services in 45 minute TAZ zones | t_es_cnt; t_job_cnt; t_45min_es_job_avg | Overture Maps, DVRPC travel model | jobs in block group and other essential services grouped into separate bins then averaged | ||
Daily Departures (by TAZ) | total_departures, depart_quantile | GTFS - SEPTA, NJTRANSIT, PATCO | Frequency of service | ||
Walkability Rank | walkshed_quantile | DVRPC pedestrian network, GTFS - SEPTA, NJTRANSIT, PATCO | Walkability of the block group to transit stations/stops | ||
Transit Accessibilty Rank | transit_access_rank | DVRPC | |||
Priority Score | chstp_score | DVRPC | calculated |
Overture Maps: CDLA Permissive 2.0 license, DVRPC: https://catalog.dvrpc.org/dvrpc_data_license.html, Federal sources: public domain
Major Regional Projects are large-scale projects that will have a significant impact on regional travel. Almost all system expansion projects are Major Regional Projects, as are reconstruction projects on the region’s freeways. Major Operational Improvement initiatives are also listed in the Plan. For the sake of brevity, smaller-scale projects that were identified in the needs assessment are not listed in the Plan document. Instead, the various funding categories in the Plan serve as placeholders for their funding, and they may be explicitly listed in future iterations of the TIP.
Updated 7/2025
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
https://catalog.dvrpc.org/dvrpc_data_license.html
PLANNING AREAS
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
Greater Philadelphia is a complex mosaic of 352 diverse cities, boroughs, and townships. The Connections 2045 Long-Range Plan characterizes each of the region’s municipalities as either a Core City, Developed Community, Growing Suburb, or Rural Area, as a means of categorizing the types of communities and defining the corresponding long-range planning policies most appropriate for each type. This categorization is shown on the Planning Areas and Centers dataset.
Many municipalities have areas within their boundaries that fit the characteristics of more than one of these Planning Area types. Gloucester Township (in Camden County, New Jersey), for example, has neighborhoods that are fully developed, but it also has a significant number of undeveloped acres and forecasted population and employment growth more characteristic of a Growing Suburb. The intent of the Plan is to assign to each municipality the planning area type associated with the long-range planning policies that will be most beneficial to the entire community. While the Planning Areas and Centers map is a guide for policy direction at the regional scale, actual approaches should always be guided by local conditions.
The region’s four Core Cities are Philadelphia, Trenton, Camden, and Chester. Targeted infrastructure investment, maintenance and rehabilitation, comprehensive neighborhood revitalization, and efforts focused on reinforcing a network of social and educational programs will help to rebuild and revitalize the region’s cities.
Developed Communities are places that have already experienced most of their population and employment growth, and include inner ring communities adjacent to the Core Cities; railroad boroughs and trolley car communities; and mature suburban townships. Many of these communities are stable and thriving, offering affordable housing opportunities; access to transit; safe pedestrian and bicycling environments; and a strong community identity. Others, however, are experiencing population and employment losses; have deteriorating infrastructure systems; have aging resident populations living on limited incomes; and have stagnant or declining tax bases that cannot keep pace with rising service demands. Rehabilitation and maintenance of infrastructure systems and the housing stock, and local economic and community development can help to reinforce location advantages, while stabilizing neighborhoods and stemming decline.
Growing Suburbs are communities that have a significant number of developable acres remaining and are experiencing or are forecast to experience significant population and/or employment growth. Key planning policies in these communities focus on the need to improve the form of development, reduce congestion, and mitigate the negative consequences of unmanaged growth, and include growth management and enhanced community design. Smart growth techniques that support a more concentrated development pattern (such as clustering, mixed uses, transit-oriented development, and transfer of development rights) can provide the critical mass necessary to support new transit services and other alternatives to the automobile. The quality of design and architectural character of the built environment, open space preservation, and the creation of an integrated system of open space and recreation are all priorities in these communities.
Rural Areas include the region’s agricultural communities and communities with large remaining natural areas. Key policy approaches for these communities focus on preservation and limiting development, and include limited expansion of infrastructure systems, preservation of a rural lifestyle and village character, support for continued farming, and enhanced natural resource protection. Livable communities in these areas include rural centers that have an identi fi able main street, a mix of uses, higher densities than their surrounding uses, and a true sense of place.
https://catalog.dvrpc.org/dvrpc_data_license.html
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
County Subdivisions are the primary divisions of counties and equivalent entities. They include census county divisions, census subareas, minor civil divisions, and unorganized territories and can be classified as either legal or statistical. Each county subdivision is assigned a five-character numeric Federal Information Processing Series (FIPS) code based on alphabetical sequence within state, and an eight-digit National Standard (NS) code.
Pennsylvania, New Jersey and Maryland contain Minor Civil Divisions (MCDs):
Minor civil divisions (MCDs) are the primary governmental or administrative divisions of a county in many states (parishes in Louisiana), and of the county equivalents in Puerto Rico and the Island Areas. MCDs in the United States, Puerto Rico, and the Island Areas represent many different kinds of legal entities with a wide variety of governmental or administrative functions. MCDs include areas variously designated as barrios, barrios-pueblo, boroughs, charter townships, commissioner districts, election districts, election precincts, gores, grants, locations, magisterial districts, parish governing authority districts, plantations, purchases, reservations, supervisor’s districts, towns, and townships.
Delaware contains Census County Divisions (CCDs):
Census county divisions (CCDs) are areas delineated by the Census Bureau in cooperation with state, tribal, and local officials for statistical purposes. CCDs have no legal function and are not governmental units. CCD boundaries usually follow visible features and usually coincide with census tract boundaries. The name of each CCD is based on a place, county, or well-known local name that identifies its location. CCDs exist where:
There are no legally established MCDs.
The legally established MCDs do not have governmental or administrative purposes.
The boundaries of the MCDs change frequently.
The MCDs are not generally known to the public.
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/COUSUB/ on June 22, 2023
Public domain
PLANNING CENTERS
All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents:
(1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan.
(2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan.
The Connections 2050 Long-Range Plan identifies four types of Centers: Suburban Centers, Town Centers, Rural Centers, and Planned Centers. The characteristics of each type are as follows:
SUBURBAN CENTERS
Are regionally significant; While not necessarily single municipalities, are perceived as single “places;” Are suburban in character; Are less dense than town centers; Lack the integrated mix of uses found in town centers;
Are defined primarily by a concentration and variety of office, retail, professional, and light industrial uses, and generally have more jobs than residents; and are generally auto dependent rather than transit oriented or pedestrian scale.
TOWN CENTERS
Have a mixture of high-density residential and commercial land use; Have an integrated mix of land uses;Have a unique history, character, and sense of place; Are of relatively higher density than their surrounding land uses;
Have a distinct downtown/main street area surrounded by relatively dense residential development;
Are pedestrian friendly and often transit oriented; and Are surrounded by suburban land uses.
RURAL CENTERS
Have a minimum density of six people and three employees per developed acre; Have an integrated mix of land uses; Have a unique history, character, and sense of place; Are of relatively higher density than the surrounding area;
Have a distinct downtown/main street (though smaller than a town center); and Are surrounded by rural and agricultural land uses.
PLANNED CENTERS
Are planned town-center-type developments on greenfields in Growing Suburbs or Rural Areas or through redevelopment on greyfields and/or brownfields in Developed Communities; and Have plans that call for village-type development, incorporating mixed, integrated land uses, relatively high densities, and pedestrian connections.
https://catalog.dvrpc.org/dvrpc_data_license.html
Conservation Subdivision Design (CSD) is a development strategy that can help communities manage their growth and preserve open space and natural areas in residential housing developments. Unlike conventional subdivisions, CSD concentrates home construction on only a portion of a site in order to protect sensitive and valuable open space, habitat, and other environmental resources. Natural Lands Trust is a non-profit conservation organization that is a leader in the field of conservation design. Natural Lands Trust have developed Growing Greener: Conservation by Design ordinances that have been adopted by many communities in southeastern Pennsylvania.
------
The Smart Growth Project Database is an application that tracks a variety of noteworthy development projects throughout the Greater Philadelphia Region. The database focuses on three types of Smart Growth projects: development near transit, traditional neighborhood development (TND), and conservation subdivisions. These types of development were selected because they often embody several of the core principles of Smart Growth:
Providing a mix of land uses,
Creating a range of housing options,
Preserving open space, farmland, and critical environmental areas,
Creating walkable neighborhoods, and
Providing a variety of transportation choices.
This database is meant to serve as a quick and convenient reference tool for planners, local officials, and citizens who are interested in Smart Growth. It includes projects that are proposed, in progress, and complete. The details of proposed developments are subject to change and projects will be updated and added as new information becomes available. The inclusion of any particular project within this application does not serve as an endorsement by DVRPC or its partner agencies. Furthermore, this application focuses on recent developments because they are the most relevant to today's real estate and economic climate.
https://catalog.dvrpc.org/dvrpc_data_license.html
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
Block Groups (BGs) are statistical divisions of census tracts, are generally defined to contain between 600 and 3,000 people, and are used to present data and control block numbering. A block group consists of clusters of blocks within the same census tract that have the same first digit of their four-digit census block number. For example, blocks 3001, 3002, 3003, . . . , 3999 in census tract 1210.02 belong to BG 3 in that census tract. Most BGs were delineated by local participants in the Census Bureau’s Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where a local or tribal government declined to participate in PSAP, and a regional organization or the State Data Center was not available to participate. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within the census tract. Within the standard census geographic hierarchy, BGs never cross state, county, or census tract boundaries, but may cross the boundaries of any other geographic entity. Tribal census tracts and tribal BGs are separate and unique geographic areas defined within federally recognized American Indian reservations and can cross state and county boundaries (see “Tribal Census Tract” and “Tribal Block Group”). The tribal census tracts and tribal block groups may be completely different from the standard county-based census tracts and block groups defined for the same area.
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/BG/ on June 22, 2023
Public domain
Current (2021) and projected numbers of Plug-In Electrical Vehicles (PEVs) at the municipal level for the Delaware Valley region. The municipal PEV counts were aggregated from the census block group counts, assigning the counts from each block group to the municipality housing that block group’s centroid. The projected PEV distribution is based on a scenario in which 5 percent of passenger vehicles in the Greater Philadelphia region (or about 200,000 vehicles) are PEVs.
Datasets Used in Analysis
2021 vehicle registration data provided by the Pennsylvania Department of Transportation.
2021 vehicle registration data from the New Jersey Motor Vehicle Commission provided by the New Jersey Department of Environmental Protection.
2015-2019 American Community Survey 5-year Estimates.
LEHD Origin-Destination Employment Statistics (LODES), version 7.
Block group-to-block group commuting distances from DVRPC's Travel Improvement Model 2.1, accessed December 2017.
Block group-to-block group commuting distances calculated from Esri’s ArcGIS Network Analyst, used in 2017.
Field | Alias | Description |
GEOID10 | GEOID10 | Municipal identifier |
MUN_NAME | Municipality | Name of municipality |
CO_NAME | County | Name of county |
STATE | State | Name of state |
POP | Population | Number of people |
HOUSUNIT | Housing Units | Number of housing units |
JOBS | Jobs | Number of jobs |
PASS_VEH | Number of Passenger Vehicles | Number of passenger vehicles as of 2021 |
CurPEV | Current Number of PEVs | Number of PEVs as of 2021 |
FutPEV | Projected Number of PEVs | Number of projected PEVs at 5% regional penetration |
If you have any questions regarding this analysis or datasets used in the analysis, please contact: Sean Greene, Manager, Air Quality Programs | sgreene@dvrpc.org | (215) 238-2860
https://catalog.dvrpc.org/dvrpc_data_license.html
Transit-Oriented Development (TOD) is often cited as one of the most effective ways of promoting Smart Growth. TOD is compact, pedestrian-friendly, mixed-use development within a short distance of a transit stop. When properly designed, TODs can promote multiple transportation options and help reduce automobile dependency. Developments typically must meet a number of criteria related to density, parking, and design to be considered a TOD.
The projects highlighted here are characterized as Development Near Transit rather than TODs because they sometimes do not meet all of the criteria typically associated with TOD. However, these developments are notable for their proximity to transit and the transit- and pedestrian-friendly features they include.
------
The Smart Growth Project Database is an application that tracks a variety of noteworthy development projects throughout the Greater Philadelphia Region. The database focuses on three types of Smart Growth projects: development near transit, traditional neighborhood development (TND), and conservation subdivisions. These types of development were selected because they often embody several of the core principles of Smart Growth:
Providing a mix of land uses,
Creating a range of housing options,
Preserving open space, farmland, and critical environmental areas,
Creating walkable neighborhoods, and
Providing a variety of transportation choices.
This database is meant to serve as a quick and convenient reference tool for planners, local officials, and citizens who are interested in Smart Growth. It includes projects that are proposed, in progress, and complete. The details of proposed developments are subject to change and projects will be updated and added as new information becomes available. The inclusion of any particular project within this application does not serve as an endorsement by DVRPC or its partner agencies. Furthermore, this application focuses on recent developments because they are the most relevant to today's real estate and economic climate.
https://catalog.dvrpc.org/dvrpc_data_license.html
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and census areas; the latter of which are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. Additionally, the Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: municipios in Puerto Rico, districts and islands in American Samoa, municipalities in the Commonwealth of the Northern Mariana Islands, and islands in the U.S. Virgin Islands. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and, thus, constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation in decennial censuses. All of the counties in Connecticut and Rhode Island and nine counties in Massachusetts were dissolved as functioning governmental entities; however, the Census Bureau continues to present data for these historical entities in order to provide comparable geographic units at the county level of the geographic hierarchy for these states and represents them as nonfunctioning legal entities in data products. Each county or statistically equivalent entity is assigned a three-character numeric Federal Information Processing Series (FIPS) code based on alphabetical sequence that is unique within state, and an eight-digit National Standard (NS) code.
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/COUNTY/ on June 22, 2023
Public domain
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
States and Equivalent Entities are the primary governmental divisions of the United States. In addition to the 50 states, the Census Bureau treats the District of Columbia, Puerto Rico, American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands as the statistical equivalents of states for the purpose of data presentation.
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/STATE/ on June 22, 2023
Public domain
The Transportation and Community Development Initiative (TCDI) is a grant opportunity that supports smart growth initiatives that implement the Connections 2045 Plan for Greater Philadelphia. TCDI focuses on linking land use and transportation planning by:
Improving the overall character and quality of life
Enhancing the existing transportation infrastructure capacity
Promoting and encouraging the use of transit, bike, and pedestrian transportation modes
Building capacity in our older suburbs and neighborhoods
Reinforcing and implementing improvements in designated Centers
Protecting our environment
https://catalog.dvrpc.org/dvrpc_data_license.html
*USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*
The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).
The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).
Public Use Microdata Areas (PUMAs) are statistical geographic areas for the dissemination of decennial census and American Community Survey (ACS) Public Use Microdata Sample files in which the Census Bureau provides selected extracts of raw data from a small sample of census records that are screened to protect confidentiality. The ACS also uses the PUMAs as a tabulation geographic entity.
For the 2020 Census, the State Data Centers in each state, the District of Columbia, and Puerto Rico are involved in the delineation of the 2020 PUMAs. Counties and census tracts are used to define PUMAs, and each PUMA must include at least 100,000 people based on the 2020 Census published counts. For the 2020 Census in Guam and the U.S. Virgin Islands, the Census Bureau establishes a single, separate PUMA for each of these two Island Areas. American Samoa and the Commonwealth of the Northern Mariana Islands do not have PUMAs, because the total population of each is under 100,000 people.
Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/PUMA/ on June 22, 2023
Public domain
2020 Census Tract to MCD lookup table
https://catalog.dvrpc.org/dvrpc_data_license.html
Simple municipal name/GEOID lookup table.The table combines GEOID with census county names and municipal names. Stored as view in the demographics schema.
Public domain
As part of the Regional Housing Initiative (RHI), the team conducted a submarket analysis. This analysis identifies 2020 census tracts with similar housing characteristics (density, price, market conditions) and groups them accordingly. This submarket analysis uses a Latent Profile Analysis (LPA) via the mclust package in R to group the region's 1,407 eligible census tracts (tracts with no households or population were removed) into one of eight submarkets. The team reviewed the existing conditions of these submarkets to identify their housing challenges and appropriate policies and strategies for each submarket.
Census tables used to gather data from the 2016-2020 American Community Survey 5-Year Estimates.
Data Dictionary
Field | Name | Source |
submarket | Housing submarket | DVRPC |
hhinc_med | Median household income | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
rent_med | Median gross rent | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
ten_rent | Percent of households that are renter-occupied | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
ten_own | Percent of households that are owner-occupied | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
vcy | Residential vacancy rate | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
hhi_150p | Percent of households with incomes of $150,000 or higher | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
yb_59e | Percent of housing units built in 1959 or earlier | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
yb_6099 | Percent of housing units built between 1960 and 1999 | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
yb_00p | Percent of housing units built since 2000 | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
unit_1 | Percent of housing units that are 1 unit in structure | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
unit_2to4 | Percent of housing units that are 2 to 4 units in structure | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
unit_5p | Percent of housing units that are 5 or more units in structure | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
pct_subsidized | Percent of housing units that are federally subsidized (Public housing, Section 8, LIHTC) | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020, National Housing Preservation Database (NHPD) |
med21 | Median single family home sale price, 2021 | The Warren Group, 2021 |
pct_diff | Median percent change in median single family home sale price, 2016-2021 | The Warren Group, 2016 & 2021 |
hhs_1 | Percent of households that are 1-person households | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
hhs_2to4 | Percent of households that are 2- to 4-person households | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
hhs_5p | Percent of households that are 5 or more person households | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
hu_acre | Housing units per acre | U.S. Census Bureau, ACS 5-Year Estimates, 2016-2020 |
Please contact Brian Carney, bcarney@dvrpc.org, for more information.
https://catalog.dvrpc.org/dvrpc_data_license.html
SEPTA Transit Stop locations for Summer 2024. Data downloaded from ArcGIS online hosted feature services Feb 2025.
SEPTA Transit Routes for Summer 2024. Data downloaded from ArcGIS online hosted feature services Feb 2025.
Monthly TIP Action updates in the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
Monthly TIP action updates in the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
RideScore is database that assesses the physical and demographic characteristics around transit stations that relate to how supportive of bicycling the area is, or could be. The database focuses on commuter rail stations outside of Center City Philadelphia as well as trolley and subway terminals where having direct, safe, and nonmotorized access between transit stations and a person's origin and/or destination is a challenge.
To assess this relationship, DVRPC staff collected data on various station area characteristics that are likely to be factors in the transit-bicycle connection. The sum of those factors is calculated to give each station a total RideScore. Together, these individual factors, and a station's total RideScore, will give transit agencies, trail advocacy groups and municipalities a picture of the station area characteristics that contribute to demand for bicycle facilities and amenities. Ultimately, RideScore may help communities identify and prioritize improvements designed to strengthen bicycle and transit connectivity.
For more information, please contact Betsy Mastaglio at bmastaglio@dvrpc.org
Visit : http://www.dvrpc.org/webmaps/ridescore/
Field | Description |
STATION | Station Name |
ZONE | transit line zone |
LINE | Line |
WLKTEXT | Stations Located Near a Walkable Retail District Value |
WLKSCORE | Stations Located Near a Walkable Retail District Score |
WLKLABEL | Stations Located Near a Walkable Retail District Category |
TVVTOTAL | Transit Vehicle Volumes Value |
TVVSCORE | Transit Vehicle Volumes Score |
TVVLABEL | Transit Vehicle Volumes Category |
OBFTEXT | On or Near On-Road Bicycle Facilities Value |
OBFSCORE | On or Near On-Road Bicycle Facilities Score |
OBFLABEL | On or Near On-Road Bicycle Facilities Category |
CONTOTAL | Connectivity Value |
CONDENSITY | Connectivity Density |
CONSCORE | Connectivity Score |
CONLABEL | Connectivity Category |
CTNTEXT | Station Proximity to Circuit Trail Network Value |
CTNSCORE | Station Proximity to Circuit Trail Network Score |
CTNLABEL | Station Proximity to Circuit Trail Network Category |
CCTOTAL | Civic and Cultural Attractions Value |
CCSCORE | Civic and Cultural Attractions Score |
CCLABEL | Civic and Cultural Attractions Category |
OBDTEXT | Outdoor Bicycle Destinations Value |
OBDSCORE | Outdoor Bicycle Destinations Score |
OBDLABEL | Outdoor Bicycle Destinations Category |
NPPTOTAL | Non-Parking Passenger Boards Value |
NPPSCORE | Non-Parking Passenger Boards Score |
NPPLABEL | Non-Parking Passenger Boards Category |
EMPTOTAL | Number of Employees within 1/2 mile of Station Value |
EMPSCORE | Number of Employees within 1/2 mile of Station Score |
EMPLABEL | Number of Employees within 1/2 mile of Station Category |
DENPOP | Population Density Total People |
DENAREA | Population Density Total Area |
DENTOTAL | Population Density Value |
DENSCORE | Population Density Score |
DENLABEL | Population Density Category |
RS_FINAL | RideScore |
SYMBOL | Map Symbol Values |
RS_LOOKUP | Station and Rail Line Lookup |
OPERATE | Operator |
MCD | Municipality |
COUNTY | County |
GEOID_10 | GEOID_10 |
LOC | Location |
https://catalog.dvrpc.org/dvrpc_data_license.html
Road safety audit study locations in the DVRPC region
https://catalog.dvrpc.org/dvrpc_data_license.html
The Transportation Improvement Program (TIP) is the regionally agreed-upon list of priority transportation projects, as required by federal law (ISTEA, TEA-21, SAFETEA LU, MAP-21, the FAST Act, and the new Infrastructure Investment and Jobs Act/Bipartisan Infrastructure law (IIJA/BIL)).
The TIP document must list all projects that intend to use federal funds, along with all non-federally funded projects that are regionally significant. It also includes all other State funded capital projects. The projects are multi-modal; that is, they include bicycle, pedestrian, ITS, and freight related projects, as well as the more traditional highway and public transit projects.
The DVRPC FY2025 TIP for PA (FY25-FY28) was adopted by the DVRPC Board on July 25, 2024, and is in effect from October 1, 2024 to September 30, 2026. This important document catalogs the latest program of transportation projects for the Pennsylvania counties in the bi-state region.
Please note that not all TIP projects are able to be mapped.
https://catalog.dvrpc.org/dvrpc_data_license.html
The Transportation Improvement Program (TIP) is the regionally agreed-upon list of priority transportation projects, as required by federal law (ISTEA, TEA-21, SAFETEA LU, MAP-21, the FAST Act, and the new Infrastructure Investment and Jobs Act/Bipartisan Infrastructure law (IIJA/BIL)).
The TIP document must list all projects that intend to use federal funds, along with all non-federally funded projects that are regionally significant. It also includes all other State funded capital projects. The projects are multi-modal; that is, they include bicycle, pedestrian, ITS, and freight related projects, as well as the more traditional highway and public transit projects.
The DVRPC FY2025 TIP for PA (FY25-FY28) was adopted by the DVRPC Board on July 25, 2024, and is in effect from October 1, 2024 to September 30, 2026. This important document catalogs the latest program of transportation projects for the Pennsylvania counties in the bi-state region.
Please note that not all TIP projects are able to be mapped.
https://catalog.dvrpc.org/dvrpc_data_license.html
This layer consists of points representing park and ride origin survey locations.
Only train stations and park and rides that have publicly accessible parking and have been surveyed by DVRPC. Each station’s data is based on fieldwork completed in one day, typically in fall or spring between the hours of 9 AM and 4 PM.
A publicly accessible web mapping application is available for use. To ensure privacy of the surveyed vehicles, individual points are aggregated to a standard geography in order to visualize trends of how people are using public transportation as part of their commute. In this case, the standard geography is 1/2 square mile hexagons that are large enough to anonymize the points within them, but also small enough to effectively display the trends in the data:
DVRPC Park & Ride Passenger Origins
https://catalog.dvrpc.org/dvrpc_data_license.html
Hexbins for the park and ride web application
https://catalog.dvrpc.org/dvrpc_data_license.html
Generalized bus routes with operating patterns dissolved into a single record per line number. This layer was created and maintained by the GIS Department. The purpose of this layer is to display active bus route variations known as patterns within the current bus service. The layer’s Projected Coordinate System is NAD_1983_StatePlane_New_Jersey_FIPS_2900 and Geographic Coordinate System is GCS_North_American_1983. Data represents scheduled, not necessarily operated service patterns. Bus line numbers represent internal route numbers and include all light rail service. The external names are as follows: 7 = Newark Light Rail 342 = Hudson-Bergen Light Rail 343 = River LINE 258 = go25– Ò
Public domain
This layer was created and maintained by the GIS Department. The purpose of this layer is to indicate all active bus stops within the current bus service. Bus stops that serve multiple routes contain a separate record for each route. The layer’s Projected Coordinate System is NAD_1983_StatePlane_New_Jersey_FIPS_2900 and Geographic Coordinate System is GCS_North_American_1983. Data represents scheduled, not necessarily operated stops. Bus line numbers represent internal route numbers and include all light rail service. The external names are as follows: 7 = Newark Light Rail 342 = Hudson-Bergen Light Rail 343 = River LINE 258 = go25
Public domain
The Federal Highway Administration (FHWA) established the Highway Safety Improvement Program (HSIP) to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.
The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses on results.
Under the enabling transportation legislation enacted in December 2015, Fixing America's Surface Transportation (FAST), New Jersey receives approximately $57 million annually for safety improvements.”
The HSIP requires a statewide strategic highway safety plan to set goals and prioritize safety investments. The HSIP also requires annual progress reporting to the FHWA.
Data source and extract/transform script - https://github.com/dvrpc/planning-data-management/tree/main/gis-hsip
The Federal Highway Administration (FHWA) established the Highway Safety Improvement Program (HSIP) to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.
The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses on results.
Under the enabling transportation legislation enacted in December 2015, Fixing America's Surface Transportation (FAST), New Jersey receives approximately $57 million annually for safety improvements.”
The HSIP requires a statewide strategic highway safety plan to set goals and prioritize safety investments. The HSIP also requires annual progress reporting to the FHWA.
Data source and extract/transform script - https://github.com/dvrpc/planning-data-management/tree/main/gis-hsip
The Federal Highway Administration (FHWA) established the Highway Safety Improvement Program (HSIP) to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.
The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses on results.
Under the enabling transportation legislation enacted in December 2015, Fixing America's Surface Transportation (FAST), New Jersey receives approximately $57 million annually for safety improvements.”
The HSIP requires a statewide strategic highway safety plan to set goals and prioritize safety investments. The HSIP also requires annual progress reporting to the FHWA.
Data source and extract/transform script - https://github.com/dvrpc/planning-data-management/tree/main/gis-hsip
The Federal Highway Administration (FHWA) established the Highway Safety Improvement Program (HSIP) to achieve a significant reduction in traffic fatalities and serious injuries on all public roads.
The HSIP emphasizes a data-driven, strategic approach to improving highway safety that focuses on results.
Under the enabling transportation legislation enacted in December 2015, Fixing America's Surface Transportation (FAST), New Jersey receives approximately $57 million annually for safety improvements.”
The HSIP requires a statewide strategic highway safety plan to set goals and prioritize safety investments. The HSIP also requires annual progress reporting to the FHWA.
Data source and extract/transform script - https://github.com/dvrpc/planning-data-management/tree/main/gis-hsip
![https://github.com/dvrpc/development-intensity-zones/blob/main/images/image.png](\"https://github.com/dvrpc/development-intensity-zones/blob/main/images/image.png\"){kind=link}