This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high pedestrian crash counts resulting in serious injuries observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018 and 2019. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 231 KBNumber of Files: The dataset contains a total of 245 road intersection records (7 "high-high" clusters and 238 "high-low" outliers)Date Created: 21st May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved a pedestrian with a severe or fatal injury type were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which these pedestrian crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with pedestrian crashes that resulted in a severe injury identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high motorcycle (Motorcycle: Above 125cc, Motorcycle: 125cc and under, Quadru-cycle, Motor Tricycle) crash counts that resulted in injuries (slight, serious, fatalities) observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018 and 2019. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 157 KBNumber of Files: The dataset contains a total of 158 road intersection records (11 "high-high" clusters and 147 "high-low" outliers)Date Created: 22nd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved either a motor tricycle, motorcycle above 125cc, motorcycle below 125cc and quadru-cycles and that were additionally associated with a slight, severe or fatal injury type were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which these motorcycle crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with motorcycle crashes identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

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 data illustrates the “Urbanized Area” for the Municipal Separate Storm Sewer System (MS4) program from the 2010 census. "Urbanized area" means a place and the adjacent densely populated territory that together have a minimum population of 50,000 people, as defined by the United States bureau of the census and as determined by the latest available decennial census. The data is provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the United States Environmental Protection Agency. The urbanized area is the regulated area for municipalities that are regulated under the MS4 program, including but not limited to cities, township, and villages."2020 Census Populations of 50K or more" and "Automatically Designated Areas" was provided by US EPA in July 2023 and combined with Michigan Open GIS Data (Minor Civil Divisions: Cities, Townships and Villages) using ESRI's ArcGIS Pro Software. Tools used include Pairwise Intersect, Merge, Pairwise Erase, and manual editing to combine the two layers.Please contact the individuals below with any questions.Christe Alwin: ALWINC@michigan.gov (point of contact)Patrick Klein: kleinp3@michigan.gov (creator)

FIELD NAME

DESCRIPTION

Name

Short name of the municipality (Lansing)

Label

The municipalities full name (City of Lansing)

Type

The type of municipality (city, township, or village)

SQMILEArea of the shape in Square Miles

ACRES

Area of the shape in Acres

Published in June 2024. Learn more about EGLE's Municipal Storm Water Program.Additional information describing Part 21 Wastewater Discharge Permits.

This dataset provides comprehensive information on road intersection pedestrian crashes resulting in serious injuries and/or fatalities recognised as "high-low" outliers within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 5% of the total "high-low" outlier pedestrian road intersection crashes resulting in serious injuries and/or fatalities for the years 2017, 2018 and 2019. The dataset is meticulously organised according to support metric values, ranging from 0,05 to 0,099, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 477 KBNumber of Files: The dataset contains a total of 10260 association rulesDate Created: 24th May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the road intersection pedestrian crashes resulting in serious injuries and/or fatalities that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,05 support metric value. Consequently, commonly occurring crash attributes among at least 5% of the "high-low" outlier road intersection pedestrian crashes resulting in serious injuries and/or fatalities were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

This dataset provides comprehensive information on road intersection crashes recognised as "high-high" clusters within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 5% of the total "high-high" cluster road intersection crashes for the years 2017, 2018, 2019, and 2021. The dataset is meticulously organised according to support metric values, ranging from 0,05 to 0,0235, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 499 KBNumber of Files: The dataset contains a total of 7186 association rulesDate Created: 23rd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-high" cluster fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes that occurred within the "high-high" cluster fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,05 support metric value. Consequently, commonly occurring crash attributes among at least 5% of the "high-high" cluster road intersection crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

The data illustrates the expanded “Urbanized Area” for the Municipal Separate Storm Sewer System (MS4) program from the 2020 census data. "Urbanized area" means a place and the adjacent densely populated territory that together have a minimum population of 50,000 people, as defined by the United States bureau of the census and as determined by the latest available decennial census. The data is provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the United States Environmental Protection Agency. The urbanized area is the regulated area for municipalities that are regulated under the MS4 program, including but not limited to cities, township, and villages."2020 Census Populations of 50K or more" and "Automatically Designated Areas" was provided by US EPA in July 2023 and combined with Michigan Open GIS Data (Minor Civil Divisions: Cities, Townships and Villages) using ESRI's ArcGIS Pro Software. Tools used include Pairwise Intersect, Merge, Pairwise Erase, and manual editing to combine the two layers.Please contact the individuals below with any questions.Christe Alwin: ALWINC@michigan.gov (point of contact)Patrick Klein: kleinp3@michigan.gov (creator)FIELD NAMEDESCRIPTIONNameShort name of the municipality (Lansing)LabelThe municipalities full name (City of Lansing)TypeThe type of municipality (city, township, or village)SQMILEArea of the shape in Square MilesACRESArea of the shape in AcresPublished in June 2024. Learn more about EGLE's Municipal Storm Water Program.Additional information describing Part 21 Wastewater Discharge Permits.

This dataset provides comprehensive information on road intersection pedestrian crashes recognised as "high-low" outliers within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 10% of the total "high-low" outlier pedestrian road intersection crashes for the years 2017, 2018, 2019, and 2021. The dataset is meticulously organised according to support metric values, ranging from 0,10 to 0,021, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 37,8 KBNumber of Files: The dataset contains a total of 624 association rulesDate Created: 24th May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the road intersection pedestrian crashes that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,10 support metric value. Consequently, commonly occurring crash attributes among at least 10% of the "high-low" outlier road intersection pedestrian crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

This dataset provides comprehensive information on road intersection crashes recognised as "high-low" outliers within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 5% of the total "high-low" outlier road intersection crashes for the years 2017, 2018, 2019, and 2021. The dataset is meticulously organised according to support metric values, ranging from 0,05 to 0,0278, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 675 KBNumber of Files: The dataset contains a total of 10212 association rulesDate Created: 23rd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,05 support metric value. Consequently, commonly occurring crash attributes among at least 5% of the "high-low" outlier road intersection crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

This dataset provides comprehensive information on road intersection crashes involving public transport (Bus, Bus-train, Combi/minibusses, midibusses) recognised as "high-high" clusters within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 10% of the total "high-high" cluster public transport road intersection crashes for the years 2017, 2018, 2019, and 2021.The dataset is meticulously organised according to support metric values, ranging from 0,10 to 0,171, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 160 KBNumber of Files: The dataset contains a total of 1620 association rulesDate Created: 23rd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-high" cluster fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes involving public transport that occurred within the "high-high" cluster fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,10 support metric value. Consequently, commonly occurring crash attributes among at least 10% of the "high-high" cluster road intersection public transport crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

This dataset provides comprehensive information on unsignalled road intersection crashes recognised as "high-low" clusters within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 10% of the total "high-high" cluster unsignalled road intersection crashes resulting for the years 2017, 2018 and 2019. The dataset is meticulously organised according to support metric values, ranging from 0,10 to 0,223, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 57,4 KB Number of Files: The dataset contains a total of 1050 association rulesDate Created: 24th May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the unsignalled road intersection crashes that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of these crashes was processed using Python software and involved the use of a 0,05 support metric value. Consequently, commonly occurring crash attributes among at least 10% of the "high-low" outlier unsignalled road intersection crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019