The top 200 locations where reported collisions occurred at intersections have been identified. The crash cluster analysis methodology for the top intersection clusters uses a fixed meter search distance of 25 meters (82 ft.) to merge crash clusters together. This analysis was based on crashes where a police officer specified one of the following junction types: Four way intersection, T-intersection, Y-intersection, five point or more. Furthermore, the methodology uses the Equivalent Property Damage Only (EPDO) weighting to rank the clusters. EPDO is based any type of injury crash (including fatal, incapacitating, non-incapacitating and possible) having a weighting of 21 compared to a property damage only crash (which has weighting of 1). The clusters were reviewed in descending EPDO order until 200 locations were obtained. The clustering analysis used crashes from the three year period from 2016-2018. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.

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 FieldsMAPID – Unique map bottleneck identifier by stateMAPID2 – Unique map bottleneck identifier by DVRPC region; NJ bottlenecks start at identifier 300NAME – Names of the intersecting streets MUNICIPAL – Municipal where intersection exists; for Philadelphia it is the planning districtCOUNTY – County where the intersection existsAMPKVEDEL – AM Peak Vehicle DelayPMPKVEDEL – PM Peak Vehicle DelayHIPKVEDEL – Highest AM or PM Peak Vehicle DelayTDPKVEDEL – AM or PM Time of Day of Highest Vehicle DelayPKVEDELRK – Peak Vehicle Delay Rank with lowest rank number the most delayPKVODELRK – Peak Volume Delay Rank with lowest rank number the most delayAMPKVODEL – AM Peak Volume Delay in HH:MM:SSPMPKVODEL – PM Peak Volume Delay in HH:MM:SSHIPKVODEL – Highest of AM or PM Peak Volume Delay in HH:MM:SSHIPKVOLDEV – Highest of AM or PM Peak Volume Delay in number formatTDPKVODEL – AM or PM Time of Day of Highest Volume DelayRELATEID – Unique identifier link to non-spatial dataRELATEIDN – Unique identifier link to non-spatial data (number format)STATE – AM or PM Time of Day of Highest Volume DelayLOTTRMAXMI – 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 measureFATMIMI – Miles of high crash severity (fatalities and major injuries) for the measureIMRMAXPMI – CMP Objective Measure score to increase mobility and reliability and meet PM3 targets weighted by road miles, where the maximum score is 4.0IMRMAXPR – 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 2.0IMIAMAXPR – CMP Objective Measure rank of IMIAMAXPMI where lower values represent higher scoresMRMAXPMI – CMP Objective Measure score to modernize and maintain the existing transportation network, where the maximum score is 1.5MRMAXPR – CMP Objective Measure rank of MRMAXPMI where lower values represent higher scoresSVRMAXPMI – CMP Objective Measure score to achieve Vision Zero, where the maximum score is 2.0SVRMAXPR – CMP Objective Measure rank of SVRMAXPMI where lower values represent higher scoresGCMAXPMI – CMP Objective Measure score to maintain the movement of goods by truck and meet PM3 targets, where the maximum score is 1.5GCMAXPR – CMP Objective Measure rank of GCMAXPMI where lower values represent higher scoresSPMAXPMI – CMP Objective Measure score to maintain and enhance transportation security and prepare for major events, where the maximum score is 1.0SPMAXPR – CMP Objective Measure rank of SPMAXPMI where lower values represent higher scoresLRPMAXPMI – 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 3.0LRPMAXPR – CMP Objective Measure rank of LRPMAXPMI where lower values represent higher scoresCMPMAXPMI – Total of of the CMP Objective Measure scores, where the maximum score is 15.0CMPMAXPR – Total CMP Objective Measure rank of CMPMAXPMI where lower values represent higher scores

For more information on the attributes associated with collisions, please download the codebook.Collected SWITRS (Statewide Integrated Traffic Records System) Data from 2014 - 2019. Geocoded and prepared by RoadSafe GIS All collisions on non-state highways marked in SWITRS as occurring in LA City jurisdictionCollisions on state highways where all or a portion of the highway is a surface street:State highways 42, 213, 1, 27 (and the intersection of Ferry@Seaside and Navy@Seaside on SR 47)State highway 110 – All between postmile values 0 and .745State highway 170 – All between postmile values 9 and 10.61State highway 2– All postmile values below 14.08 (Glendale Fwy begins)Collisions at ramp intersections with local roads. In some cases it can be difficult to distinguish an intersection with a local road or another ramp segment. However, all the collisions with these ramp values are included: 1 – Ramp Exit, Last 50 Feet3 – Ramp Entry, First 50 Feet4 – Not State Highway, Ramp-related, Within 100 Feet5 – Intersection6 – Not State Highway, Intersection-related, Within 250 FeetAdditional fields in the collision data table for a standardized matching address (match_addr), primary road name (m_primaryr), secondary road name (m_secondrd). These are very useful fields for ranking by intersections. RoadSafe GIS utilizes these fields for generating rankings by various safety performance functions.Party and victim data tables that correspond to the SWITRS collision file.Display Note: 3090 Case IDs can not be match with LA City streets from data provided by SWITRS. Note that the field names have been updated to reflect the original headers provided by CHP in addition to our several added fields.

For more information on the attributes associated with collisions, please download the codebook.Collected SWITRS (Statewide Integrated Traffic Records System) Data from 2009 - 2013. Geocoded and prepared by RoadSafe GIS All collisions on non-state highways marked in SWITRS as occurring in LA City jurisdictionCollisions on state highways where all or a portion of the highway is a surface street:State highways 42, 213, 1, 27 (and the intersection of Ferry@Seaside and Navy@Seaside on SR 47)State highway 110 – All between postmile values 0 and .745State highway 170 – All between postmile values 9 and 10.61State highway 2– All postmile values below 14.08 (Glendale Fwy begins)Collisions at ramp intersections with local roads. In some cases it can be difficult to distinguish an intersection with a local road or another ramp segment. However, all the collisions with these ramp values are included: 1 – Ramp Exit, Last 50 Feet3 – Ramp Entry, First 50 Feet4 – Not State Highway, Ramp-related, Within 100 Feet5 – Intersection6 – Not State Highway, Intersection-related, Within 250 FeetAdditional fields in the collision data table for a standardized matching address (match_addr), primary road name (m_primaryr), secondary road name (m_secondrd). These are very useful fields for ranking by intersections. RoadSafe GIS utilizes these fields for generating rankings by various safety performance functions.Party and victim data tables that correspond to the SWITRS collision file.Note that the field names have been updated to reflect the original headers provided by CHP in addition to our several added fields. You can access their raw data template here.

This map layer was created by the Cape Cod Commission GIS Department in Winter of 2018/2019 by geoprocessing and digitizing the most current Mean High Water (MHW) layer, Wetlands layer, and aerial photo to depict as accurately as possible the highest extent of Cape Cods High Water coastline. This newly created line was proportioned into 100 ft segments. These segments were coded with the following attributes: Salt Marsh, Undeveloped, Coastal Bank, Beach Owner, LittoralID, and RibbonScore. The first three fields characterize the presense or absense of the land form. The Beach Owner field is coded with "N": National Park Service, "T": Town Owned, and "O": Other. The LittoralID field is a unique identifier corresponding to the all of segments within a discreet shoreline system.RibbonScore is the cumulative score, ranging from -5 to 11, that was scored by GIS analysis based on each 100ft segments' relative vulnerability to major coastal threats. These conditions were chosen based on the South Carolina Beachfront Vulnerability Index (https://coast.noaa.gov/digitalcoast/stories/vulnerability-index.html). The factors affecting vulnerability include: distance of structures from the shoreline; presence of coastal beach, coastal dunes, barrier beaches, and/or salt marshes; long-term coastal erosion rates from USGS and MA CZM; presence of coastal engineering structures; beach nourishment projects (1995-2014); floodplain intersects with roads/structures; SLOSH zones intersect with roads/structures; and SLR scenarios intersect with roads/structures. Currently, the Cape Cod commission is ranking -5 to -1 as Low; 0 to 5 as Medium and 6-11 as High.

The top locations where reported collisions occurred at intersections have been identified. The crash cluster analysis methodology for the top intersection clusters uses a fixed meter search distance of 25 meters (82 ft.) to merge crash clusters together. This analysis was based on crashes where a police officer specified one of the following junction types: Four way intersection, T-intersection, Y-intersection, five point or more. Furthermore, the methodology uses the Equivalent Property Damage Only (EPDO) weighting to rank the clusters. EPDO is based any type of injury crash (including fatal, incapacitating, non-incapacitating and possible) having a weighting of 21 compared to a property damage only crash (which has weighting of 1). The clustering analysis used crashes from the three year period from 2019-2021. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.

For more information on the attributes associated with collisions, please download the codebook.Collected SWITRS (Statewide Integrated Traffic Records System) Data from 2014 - 2019. Geocoded and prepared by RoadSafe GIS All collisions on non-state highways marked in SWITRS as occurring in LA City jurisdictionCollisions on state highways where all or a portion of the highway is a surface street:State highways 42, 213, 1, 27 (and the intersection of Ferry@Seaside and Navy@Seaside on SR 47)State highway 110 – All between postmile values 0 and .745State highway 170 – All between postmile values 9 and 10.61State highway 2– All postmile values below 14.08 (Glendale Fwy begins)Collisions at ramp intersections with local roads. In some cases it can be difficult to distinguish an intersection with a local road or another ramp segment. However, all the collisions with these ramp values are included: 1 – Ramp Exit, Last 50 Feet3 – Ramp Entry, First 50 Feet4 – Not State Highway, Ramp-related, Within 100 Feet5 – Intersection6 – Not State Highway, Intersection-related, Within 250 FeetAdditional fields in the collision data table for a standardized matching address (match_addr), primary road name (m_primaryr), secondary road name (m_secondrd). These are very useful fields for ranking by intersections. RoadSafe GIS utilizes these fields for generating rankings by various safety performance functions.Party and victim data tables that correspond to the SWITRS collision file.Display Note: 3090 Case IDs can not be match with LA City streets from data provided by SWITRS. Note that the field names have been updated to reflect the original headers provided by CHP in addition to our several added fields.

The top 200 locations where reported collisions occurred at intersections have been identified. The crash cluster analysis methodology for the top intersection clusters uses a fixed meter search distance of 25 meters (82 ft.) to merge crash clusters together. This analysis was based on crashes where a police officer specified one of the following junction types: Four way intersection, T-intersection, Y-intersection, five point or more. Furthermore, the methodology uses the Equivalent Property Damage Only (EPDO) weighting to rank the clusters. EPDO is based any type of injury crash (including fatal, incapacitating, non-incapacitating and possible) having a weighting of 21 compared to a property damage only crash (which has weighting of 1). The clusters were reviewed in descending EPDO order until 200 locations were obtained. The clustering analysis used crashes from the three year period from 2017-2019. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.

AnalysisFEMA's National Flood Hazard Layer (NFHL) and the CDC's Social Vulnerability Index (SVI) were cross referenced to produce a Place Vulnerability Analysis for Hudson County, NJ. Using ArcGIS Pro, the location of interest (Hudson County) was first determined and the Flood Hazard and SVI layers were clipped to this extent. A new feature class, intersecting the two, was then created using the Intersect Tool. The output of this process was the Hudson County Place Vulnerability Layer. Additional Layers were added to the map to assess important special needs infrastructure, community lifelines, and additional hazard risks within the most vulnerable areas of the county.LayersWildfire Hazard Potential: Shows the average wildfire hazard potential for the US on a scale of 1-5. The layer was obtained using ESRI's Living Atlas. Source: https://napsg.maps.arcgis.com/home/item.html?id=ce92e9a37f27439082476c369e2f4254 NOAA Storm Events Database 1950-2021: Shares notable storm events throughout the US recorded by NOAA between the years of 1950-2021. The layer was obtained using ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=88cc0d5e55f343c28739af1a091dfc91 Category 1 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 1 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=49badb9332f14079b69cfa49b56809dc Category 2 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 2 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=b4e4f410fe9746d5898d98bb7467c1c2 Category 3 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 3 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=876a38efe537489fb3bc6b490519117f U.S. Sea Level Rise Projections: Shows different sea level rise projections within the United States. The layer was obtained via ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=8943e6e91c304ba2997d83b597e32861Power Plants: Includes all New Jersey power plants about 1 Megawatt capacity. The layer was obtained via the NJDEP Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=282eb9eb22cc40a99ed509a7aa9f7c90Solid & Hazardous Waste Facilities: Includes hazardous waste facilities, medical waste facilities, incinerators, recycling facilities, and landfill sites within New Jersey. Obtained via the NJDEP Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=896615180fb04d8eafda0df9df9a1d73Solid Waste Landfill Sites over 35 Acres: Includes solid waste landfill sites in New Jersey that are larger than 35 acres. Obtained via the NJDEP Bureau of GIS website. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=2b4eab598df94ffabaa8d92e3e46deb4NJ Transit Rail Lines: A layer showing segments of the NJ Transit Rail System and terminals. Data was obtained via the NJ Transit GIS Department. Source: https://www.arcgis.com/home/item.html?id=e6701817be974795aecc7f7a8cc42f79Medical Emergency Response Structures: Contains emergency response centers within the U.S. based off National Geospatial Data Asset data from the U.S. Geological Survey. The layer was obtained using ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=2c36dbb008844081b017da6fd3d0d28bSchools: Shows the location of New Jersey schools, including public, private and charter schools. Obtained via the New Jersey Office of GIS. Source: https://njdep.maps.arcgis.com/home/item.html?id=d8223610010a4c3887cfb88b904545ffChild Care Centers: Shows the location of active child care centers in New Jersey. The layer was obtained via the NJ Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=0bc9fe070d4c49e1a6555c3fdea15b8aNursing Homes: A layer containing the locations of nursing homes and assisted care facilities in the United States. Obtained via the HIFLD website. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=78c58035fb3942ba82af991bb4476f13cCDC's Social Vulnerability Index (SVI) - ATSDR's Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=05709059044243ae9b42f469f0e06642

The top locations where reported collisions occurred at intersections have been identified. The crash cluster analysis methodology for the top intersection clusters uses a fixed meter search distance of 25 meters (82 ft.) to merge crash clusters together. This analysis was based on crashes where a police officer specified one of the following junction types: Four way intersection, T-intersection, Y-intersection, five point or more. Furthermore, the methodology uses the Equivalent Property Damage Only (EPDO) weighting to rank the clusters. EPDO is based any type of injury crash (including fatal, incapacitating, non-incapacitating and possible) having a weighting of 21 compared to a property damage only crash (which has weighting of 1). The clustering analysis used crashes from the three year period from 2015-2017. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.