17 datasets found
  1. Data from: Smart Location Database

    • catalog.data.gov
    • gimi9.com
    • +2more
    Updated Feb 25, 2025
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    U.S. Environmental Protection Agency, Office of Policy, Office of Sustainable Communities (Publisher) (2025). Smart Location Database [Dataset]. https://catalog.data.gov/dataset/smart-location-database8
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    Dataset updated
    Feb 25, 2025
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

  2. H

    Extracted Data From: Smart Location Database

    • dataverse.harvard.edu
    Updated Feb 19, 2025
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    Harvard Dataverse (2025). Extracted Data From: Smart Location Database [Dataset]. http://doi.org/10.7910/DVN/WY9T73
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Harvard Dataverse
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2010
    Area covered
    United States
    Description

    This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information: https://catalog.data.gov/dataset/smart-location-database7 If you have questions about the underlying data stored here, please contact Thomas John (thomas.john@epa.gov). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "The Smart Location Database is a nationwide geographic data resource for measuring location efficiency. It includes more than 90 attributes summarizing characteristics, such as housing density, diversity of land use, neighborhood design, destination accessibility, transit service, employment and demographics. Most attributes are available for every census block group in the United States. A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. EPA first released a beta version of the Smart Location Database in 2011. The initial full version was released in 2013, and the database was updated to its current version in 2021." Quote from https://www.epa.gov/smartgrowth/smart-location-mapping and https://catalog.data.gov/dataset/smart-location-database7

  3. NGBS: Points for Smart Location Practices

    • catalog.data.gov
    Updated Feb 25, 2025
    + more versions
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    U.S. Environmental Protection Agency, Office of Sustainable Communities (Point of Contact) (2025). NGBS: Points for Smart Location Practices [Dataset]. https://catalog.data.gov/dataset/ngbs-points-for-smart-location-practices7
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    Dataset updated
    Feb 25, 2025
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    These map layers present the number of National Green Building Standard points awarded for a project site or lot’s relative walkability, and accessibility to jobs via transit or within a 45-minute drive. This map presents information on the following criteria included in the 2020 National Green Building Standard: • Section 405.6(7) - Points for sites located in census block groups with above-average transit access to employment. (See variable D5b in Smart Location Database Technical Documentation and User Guide (2014) for background) • Section 405.6(8) - Points for sites located in census block groups with above-average access to employment within a 45-minute drive (See variable D5a in Smart Location Database Technical Documentation and User Guide (2014) for background on methods) • Section 501.2(4) - Points for lots located in census block groups with above-average neighborhood walkability (See National Walkability Index for background on methods) • Section 11.501.2(3) - Points for lots located in census block groups with above-average neighborhood walkability (See National Walkability Index for background on methods) Using data available through EPA’s Smart Location Database and National Walkability Index, relative walkability and accessibility to jobs via transit or within a 45-minute drive for census block groups were calculated and ranked into quartile groups. The regional comparison was made by considering the score of each individual census block group as a ratio of the average score of the county in which it is located. Those block groups with scores in the highest two quartiles nationally are eligible for NGBS points per the Sections noted above. Details on methodologies and datasets includes in the Smart Location Database and National Walkability Index can be found here: https://www.epa.gov/smartgrowth/smart-location-mapping#SLD

  4. w

    Smart Location Database - Download

    • data.wu.ac.at
    • datadiscoverystudio.org
    esri rest, zip
    Updated Jan 3, 2018
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    U.S. Environmental Protection Agency (2018). Smart Location Database - Download [Dataset]. https://data.wu.ac.at/schema/data_gov/NzBiNDUyYTQtNGYyZi00MjliLWEyY2EtNDA5YzNiMzY1MTll
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    zip, esri restAvailable download formats
    Dataset updated
    Jan 3, 2018
    Dataset provided by
    U.S. Environmental Protection Agency
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    8a55c7dea243e83f3277763f83d4d392eb07ed69
    Description

    The Smart Location Database (SLD) summarizes over 80 demographic, built environment, transit service, and destination accessibility attributes for every census block group in the United States. Future updates to the SLD will include additional attributes which summarize the relative location efficiency of a block group when compared to other block groups within the same metropolitan region. EPA also plans to periodically update attributes and add new attributes to reflect latest available data. A log of SLD updates is included in the SLD User Guide. See the user guide for a full description of data sources, data currency, and known limitations: https://edg.epa.gov/data/Public/OP/SLD/SLD_userguide.pdf

  5. Data from: Walkability Index

    • catalog.data.gov
    • datasets.ai
    Updated Feb 25, 2025
    + more versions
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    U.S. Environmental Protection Agency, Office of Sustainable Communities (Point of Contact) (2025). Walkability Index [Dataset]. https://catalog.data.gov/dataset/walkability-index8
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    Dataset updated
    Feb 25, 2025
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    The Walkability Index dataset characterizes every Census 2019 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of the built environment that influence the likelihood of walking being used as a mode of travel. The Walkability Index is based on the EPA's previous data product, the Smart Location Database (SLD). Block group data from the SLD was the only input into the Walkability Index, and consisted of four variables from the SLD weighted in a formula to create the new Walkability Index. This dataset shares the SLD's block group boundary definitions from Census 2019. The methodology describing the process of creating the Walkability Index can be found in the documents located at https://edg.epa.gov/EPADataCommons/public/OA/WalkabilityIndex.zip. You can also learn more about the Smart Location Database at https://www.epa.gov/smartgrowth/smart-location-mapping.

  6. H

    Extracted Data From: National Walkability Index

    • dataverse.harvard.edu
    Updated Feb 10, 2025
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    U.S. Environmental Protection Agency (2025). Extracted Data From: National Walkability Index [Dataset]. http://doi.org/10.7910/DVN/QCAQB2
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 10, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    U.S. Environmental Protection Agency
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2019 - Jan 1, 9999
    Description

    This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact John Thomas, U.S. Environmental Protection Agency, at thomas.john@epa.gov. If you have questions about this metadata entry, please contact the CAFE team at climatecafe@bu.edu. "The National Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability. The national dataset includes walkability scores for all block groups as well as the underlying attributes that are used to rank the block groups. The National Walkability Index Methodology and User Guide (pdf) (2.63 MB, 2021) provides information on how to use the tool, as well as the methodology used to derive the index and ranked scores for its inputs. The index was developed using selected variables on density, diversity of land uses, and proximity to transit from the Smart Location Database. " [Quote from https://www.epa.gov/smartgrowth/national-walkability-index-user-guide-and-methodology]

  7. g

    Transpo Walkability

    • genesee2050.com
    Updated Dec 23, 2015
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    plcscheiner (2015). Transpo Walkability [Dataset]. https://www.genesee2050.com/items/86f2229d69e84c2eb9ab8597efcc9d89
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    Dataset updated
    Dec 23, 2015
    Dataset authored and provided by
    plcscheiner
    Area covered
    Description

    The Walkabiliy Index dataset characterizes every Census 2010 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of the built environment that influence the likelihood of walking being used as a mode of travel. The Walkability Index is based on the EPA's previous data product, the Smart Location Database (SLD). Block group data from the SLD was the only input into the Walkability Index, and consisted of four variables from the SLD weighted in a formula to create the new Walkability Index. This dataset shares the SLD's block group boundary definitions from Census 2010. The methodology describing the process of creating the Walkability Index can be found in the documents located at https://edg.epa.gov/data/Public/OP/WalkabilityIndex.zip. You can also learn more about the Smart Location Database at https://edg.epa.gov/data/Public/OP/Smart_Location_DB_v02b.zip.

  8. a

    PUBLIC TRANSIT ACCESSIBILITY, Copy for Urban Ag

    • hub.arcgis.com
    • chi-phi-nmcdc.opendata.arcgis.com
    Updated May 10, 2019
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    New Mexico Community Data Collaborative (2019). PUBLIC TRANSIT ACCESSIBILITY, Copy for Urban Ag [Dataset]. https://hub.arcgis.com/maps/202f1ee44c1d4564af7934c28c902bd2
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    Dataset updated
    May 10, 2019
    Dataset authored and provided by
    New Mexico Community Data Collaborative
    Area covered
    Description

    This map provides a sample of variables from EPA's Smart Location Database (SLD), a consolidated geographic data resource for measuring location efficiency. The SLD includes over 90 different variables characterizing the built environment, accessibility to destinations, employment, and demographics for every census block group in the United States. Data reflects conditions in 2010 unless otherwise noted. For more info see the Smart Location Database website or http://www2.epa.gov/smart-growth/smart-location-database-technical-documentation-and-user-guide

  9. Data from: Walkability Index

    • data.memphistn.gov
    Updated Jun 27, 2019
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    Environmental Protection Agency (2019). Walkability Index [Dataset]. https://data.memphistn.gov/Neighborhoods/Walkability-Index/fudf-qubq
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    csv, application/rssxml, xml, tsv, application/rdfxml, application/geo+json, kmz, kmlAvailable download formats
    Dataset updated
    Jun 27, 2019
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Authors
    Environmental Protection Agency
    Description

    The Walkability Index dataset characterizes every Census 2010 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of the built environment that influence the likelihood of walking being used as a mode of travel. The Walkability Index is based on the EPA's previous data product, the Smart Location Database (SLD). Walkabilty has been linked to increased physical activity and stronger social ties within the communities, promoting better health outcomes than less walkable areas.

  10. l

    TblEmployment

    • geohub.lacity.org
    • visionzero.geohub.lacity.org
    • +1more
    Updated Sep 30, 2016
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    Los Angeles Department of Transportation (2016). TblEmployment [Dataset]. https://geohub.lacity.org/items/562d2f0dd4fd4e9ca65e40b3e00cbe95
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    Dataset updated
    Sep 30, 2016
    Dataset authored and provided by
    Los Angeles Department of Transportation
    Description

    Abstract: Dataset represents the count of total jobs and retail jobs within ¼ mi of each intersection. Relations: Join to the Intersection Table using the “boeint_fkey” field. Source: ACS 2014 5-Year Estimatesboeint_fkeyUnique identifier for the intersection as part of the Bureau of Engineering’s Centerline networkempl_ctNumber of jobs within ¼ mi of the intersectionretail_ctNumber of retail jobs within ¼ mi of the intersectionmedianwg_ctNumber of median wage workers in the intersecting block group. The definition of a median wage worker is based of the U.S. Environmental Protection Agency’s (EPA) Smart Location Database (SLD). More information about the EPA SLD can be found here: https://www.epa.gov/smartgrowth/smart-location-mapping

  11. a

    Jobs within 45 min transit ride SLD 2018

    • affh-data-and-mapping-resources-v-2-0-cahcd.hub.arcgis.com
    Updated Feb 28, 2023
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    Housing and Community Development (2023). Jobs within 45 min transit ride SLD 2018 [Dataset]. https://affh-data-and-mapping-resources-v-2-0-cahcd.hub.arcgis.com/datasets/jobs-within-45-min-transit-ride-sld-2018/explore
    Explore at:
    Dataset updated
    Feb 28, 2023
    Dataset authored and provided by
    Housing and Community Development
    Area covered
    Description

    This layer visualizes the number of jobs available within a 45 minute transit ride based on the EPA Smart Location database. The Smart Location Database summarizes more than 90 different indicators associated with the built environment and location efficiency. Indicators include density of development, diversity of land use, street network design, and accessibility to destinations as well as various demographic and employment statistics. Most attributes are available for all U.S. block groups.Citation: EPA SLD, 2021Data Source: https://www.epa.gov/smartgrowth/smart-location-mappingData downloaded from source: 1/10/2023

  12. EPA-Enhanced Qualified Opportunity Zones (January 2021)

    • datasets.ai
    0
    Updated Jul 2, 2020
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    U.S. Environmental Protection Agency (2020). EPA-Enhanced Qualified Opportunity Zones (January 2021) [Dataset]. https://datasets.ai/datasets/epa-enhanced-qualified-opportunity-zones-january-20215
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    0Available download formats
    Dataset updated
    Jul 2, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Authors
    U.S. Environmental Protection Agency
    Description

    This layer contains Census Tracts that have been designated as Qualified Opportunity Zones and contains additional data determined by the EPA to be of interest to users who are seeking revitalization-oriented information about these tracts. Based on nominations of eligible census tracts by the Chief Executive Officers of each State, Treasury has completed its designation of Qualified Opportunity Zones. Each State nominated the maximum number of eligible tracts, per statute, and these designations are final. The statute and legislative history of the Opportunity Zone designations, under IRC § 1400Z, do not contemplate an opportunity for additional or revised designations after the maximum number of zones allowable have been designated in a State or Territory. The data in this layer was updated in January 2021. For more information on Opportunity Zones, please visit: https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx

    EPA has added these indicators to the QOZ tracts list:

    1. Count of Superfund facilities from EPA National Priorities List (NPL). Count was generated by performing spatial join of Tract boundaries to NPL points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/superfund/superfund-data-and-reports

    2. Count of Brownfields properties from EPA Assessment, Cleanup and Redevelopment Exchange System (ACRES). Count was generated by performing spatial join of Tract boundaries to ACRES points--yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_ACRES.csv

    3. Technical Assistance Communities from EPA Office of Community Revitalization (OCR). 13 layers were merged into one; count was generated by performing spatial join of Tract boundaries to combined point layer—yielding per tract counts. Please note that technical assistance communities are often serving areas larger than a single Census tract. Please contact OCR with questions. Spatial Extent: all US states and territories. Source: https://epa.maps.arcgis.com/home/item.html?id=b8795575db194340a4ad1c251e4d6ca1

    4. Lead Paint Index from Environmental Justice Screening and Mapping Tool (EJSCREEN). Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

    5. Air Toxics Respiratory Index from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

    6. Demographic Index Indicator from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

    7. Estimated Floodplain Indicator from EPA EnviroAtlas. Floodplain raster was converted to polygon feature class; Y/N indicator was generated by performing a spatial join of Tract boundaries to the Floodplain polygons. Spatial Extent: Continental US. Source: https://gaftp.epa.gov/epadatacommons/ORD/EnviroAtlas/Estimated_floodplain_CONUS.zip

    8. National Walkability Index from EPA Smart Location Tools. The National Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability. Tract values assigned by averaging values from block group-level table. Spatial Extent: all US states and territories. Source: EPA Office of Policy—2020 NWI update

    9. Impaired Waters Indicator from EPA Office of Water (OW). Y/N indicator was generated by performing spatial joins of Tract boundaries to 3 separate impaired waters layers (point, line and polygon). Y was assigned for all intersected geographies. Extent: all US states and Puerto Rico. Source: https://watersgeo.epa.gov/GEOSPATIALDOWNLOADS/rad_303d_20150501_fgdb.zip

    10. Tribal Areas Indicator from EPA. Y/N indicator was generated by performing spatial joins of Tract boundaries to 4 separate Tribal areas layers (Alaska Native Villages, Alaska Allotments, Alaska Reservations, Lower 48 Tribes). Y as assigned for all intersected geographies. Spatial Extent: Alaska and Continental US. Source: https://edg.epa.gov/data/PUBLIC/OEI/OIAA/TRIBES/EPAtribes.zip

    11. Count of Resource Conservation and Recovery Act (RCRA) Corrective Action facilities. Count was generated by performing spatial join of Tract boundaries to Corrective Action points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/cleanups/cimc-web-map-service-and-more

    12. Count of Toxics Release Inventory facilities from EPA. Count was generated by performing spatial join of Tract boundaries to TRI points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_TRI.csv

    13. Social Vulnerability Index (SVI) Housing/Transportation Index from CDC, published in 2018. The Housing/Transportation Index includes ACS 2014-2018 data on crowding in housing and no access to vehicle, among others. County values assigned to tracts by joining Tracts to county-level table. For detailed documentation: https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdfSpatial Extent: all US states. Source: https://epa.maps.arcgis.com/home/item.html?id=cbd68d9887574a10bc89ea4efe2b8087

    14. Low Access to Food Store Indicator from USDA Food Access Atlas. Y/N indicator was generated by performing a table join of Tracts to the Food Access table records meeting the test criteria. Spatial Extent: all US states. Source: https://www.ers.usda.gov/data-products/food-access-research-atlas/download-the-data/

    15. Overall Social Vulnerability Index (SVI) from CDC. Values (RPL_THEMES) assigned by joining the Tract boundaries to source Tract-level table. Spatial Extent: All US states. Source: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

    16. Rural Communities Indicator from USDA Economic Research Service (ERS). Source tract-level table was flagged as rural where RUCA Codes in 4-10 or 2 and 3 where area >= 400 sq. miles and pop density

  13. Z

    New York City Multi-scalar Street Segment Data

    • data.niaid.nih.gov
    Updated Aug 4, 2024
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    Shi, Ge (2024). New York City Multi-scalar Street Segment Data [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10628027
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    Dataset updated
    Aug 4, 2024
    Dataset authored and provided by
    Shi, Ge
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    New York
    Description

    This dataset compiles a comprehensive database containing 90,327 street segments in New York City, covering their street design features, streetscape design, Vision Zero treatments, and neighborhood land use. It has two scales-street and street segment group (aggregation of same type of street at neighborhood). This dataset is derived based on all publicly available data, most from NYC Open Data. The detailed methods can be found in the published paper, Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City. To use it, please refer to the metadata file for more information and cite our work. A full list of raw data source can be found below:

    Motor Vehicle Collisions – NYC Open Data: https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95

    Citywide Street Centerline (CSCL) – NYC Open Data: https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b

    NYC Building Footprints – NYC Open Data: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh

    Practical Canopy for New York City: https://zenodo.org/record/6547492

    New York City Bike Routes – NYC Open Data: https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7

    Sidewalk Widths NYC (originally from Sidewalk – NYC Open Data): https://www.sidewalkwidths.nyc/

    LION Single Line Street Base Map - The NYC Department of City Planning (DCP): https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page

    NYC Planimetric Database Median – NYC Open Data: https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d

    NYC Vision Zero Open Data (including multiple datasets including all the implementations): https://www.nyc.gov/content/visionzero/pages/open-data

    NYS Traffic Data - New York State Department of Transportation Open Data: https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb

    Smart Location Database - US Environmental Protection Agency: https://www.epa.gov/smartgrowth/smart-location-mapping

    Race and ethnicity in area - American Community Survey (ACS): https://www.census.gov/programs-surveys/acs

  14. n

    Data from: Role of vehicle technology on use: Joint analysis of the choice...

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated Aug 23, 2023
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    Debapriya Chakraborty (2023). Role of vehicle technology on use: Joint analysis of the choice of plug-in electric vehicle ownership and miles traveled [Dataset]. http://doi.org/10.25338/B8C64G
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    zipAvailable download formats
    Dataset updated
    Aug 23, 2023
    Dataset provided by
    University of California, Davis
    Authors
    Debapriya Chakraborty
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body and fuel type to project future VMT changes and mobile source emission levels. The current report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. We use the 2019 California Vehicle Survey data here that allows us to analyze the driving behavior associated with more recent EV models (with potentially longer ranges). Important findings from the model include:

    Household characteristics like size or having children have an expected impact on vehicle preference: larger vehicles are preferred. College education, rooftop solar ownership, and the number of employed workers in a household affect the preference for BEVs and PHEVs in the small car segment dominated by the Leaf, Bolt, Prius-Plug-in and the Volt often used as a commuter car. Among built environment factors, population density and the walkability index of a neighborhood have a statistically significant impact on the type of vehicle choice and VMT. It is observed that a 10% increase in population density reduces the preference for ICEV pickup trucks by 0.34% and VMT by 0.4%. However, if the increase in population density is 25%, the reduction in preference for pickup trucks is 8.4% and VMT is 8.6%. The other built environment factor we consider is the walkability index. If the walkability index of a neighborhood increases by 25%, it reduces the preference for ICEV pickup trucks by 15% and their VMT by 16%. Overall, these results suggest that if policies encourage mixed development of neighborhoods and increase density, it can have an important impact on ownership and usage of gas guzzlers like pickup trucks and help in the process of electrification of the transportation sector.

    Methods The dataset used in this report was created using the following public data sources:

    2019 California Vehicle Survey: "Transportation Secure Data Center." ([2019]). National Renewable Energy Laboratory. Accessed [04/26/2023]: www.nrel.gov/tsdc. The Smart Mapping Tool by EPA: https://www.epa.gov/smartgrowth/smart-location-mapping

    American Community Survey: https://www.census.gov/programs-surveys/acs

  15. a

    State of Black LA Community Indicators Year 2

    • equity-lacounty.hub.arcgis.com
    • hub.arcgis.com
    • +1more
    Updated Feb 13, 2024
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    County of Los Angeles (2024). State of Black LA Community Indicators Year 2 [Dataset]. https://equity-lacounty.hub.arcgis.com/datasets/state-of-black-la-community-indicators-year-2
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    Dataset updated
    Feb 13, 2024
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. Countywide Statistical Areas (CSA) are current as of October 2023.

    Fields ending in _yr1 were calculated for the original 2021-2022 SBLA report, while fields ending in _yr2 or without a year suffix were calculated for the 2023-2025 version. Eviction Filings per 100 (eviction_filings_per100) and Life Expectancy (life_expectancy) did not have updated data and are the same data shown in the Year 1 report.

    Population and demographic data are from US Census American Community Survey (ACS) 5-year estimates, aggregated up from census tract or block group to CSA. Year 1 data are from 2020, year 2 data are from 2022.

    Poverty Data (200% FPL) are from LA County ISD-eGIS Demographics. Year 1 data are from 2021, Year 2 are from 2022.

    The 2023-2025 report includes several new indicators that are calculated as the percent of countywide population by race that resides in a geographic area of interest. Population for these indicators is estimated based on intersection with census block group centroids. These indicators are:

    Indicator

    Fields

    Source

    Health Professional Shortage Areas (HPSA) for Primary Care

    hpsa_primary_pct hpsa_primary_black_pct

    LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-primary-care/about

    Health Professional Shortage Areas (HPSA) for Mental Health

    hpsa_mental_pct hpsa_mental_black_pct

    LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-mental-health/about

    Concentrated Disadvantage

    cd_pct cd_black_pct

    LA County ISD-Enterprise GIS https://egis-lacounty.hub.arcgis.com/datasets/lacounty::concentrated-disadvantage-index-2022/explore

    Firearm Dealers

    firearm_dl_count (count of dealers in CSA) firearm_dl_per10000 (rate of dealers per 10,000)

    LA County DPH Office of Violence Prevention (OVP)

    High and Very High Park Need Areas

    parks_need_pct parks_need_black_pct

    LA County Parks Needs Assessment Plus (PNA+) https://lacounty.maps.arcgis.com/apps/instant/media/index.html?appid=3d0ef36720b447dcade1ab87a2cc80b9

    High Quality Transit Areas

    hqta_pct hqta_black_pct

    SCAG https://lacounty.maps.arcgis.com/home/item.html?id=43e6fef395d041c09deaeb369a513ca1

    High Walkability Areas

    walk_total_pct walk_black_pct

    EPA Walkability Index https://www.epa.gov/smartgrowth/smart-location-mapping#walkability

    High Poverty and High Segregation Areas

    highpovseg_total_pct highpovseg_black_pct

    CTCAC/HCD Opportunity Area Maps https://www.treasurer.ca.gov/ctcac/opportunity.asp

    LA County Arts Investments

    arts_dollars (total $$ for CSA) arts_dollars_percap (investment dollars per capita)

    LA County Department of Arts and Culture https://lacountyartsdata.org/#maps

    Strong Start (areas with at least 9 Strong Start indicators)

    strongstart_total_pct strongstart_black_pct

    CA Strong Start Index https://strongstartindex.org/map

    For more information about the purpose of this data, please contact CEO-ARDI.

    For more information about the configuration of this data, please contact ISD-Enterprise GIS.

  16. ENERGY STAR Certified Smart Thermostats

    • catalog.data.gov
    • data.energystar.gov
    • +1more
    Updated Jun 28, 2025
    + more versions
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    U.S. Environmental Protection Agency (2025). ENERGY STAR Certified Smart Thermostats [Dataset]. https://catalog.data.gov/dataset/energy-star-certified-smart-thermostats
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    Dataset updated
    Jun 28, 2025
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    Certified models meet all ENERGY STAR requirements as listed in the Version 1.0 ENERGY STAR Program Requirements for Connected Thermostats that are effective as of December 23, 2016. A detailed listing of key efficiency criteria are available at https://www.energystar.gov/products/heating_cooling/smart_thermostats/key_product_criteria.

  17. Promoting risk reduction among young adults with asthma during wildfire...

    • catalog.data.gov
    • gimi9.com
    Updated Jan 16, 2023
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    U.S. EPA Office of Research and Development (ORD) (2023). Promoting risk reduction among young adults with asthma during wildfire smoke [Dataset]. https://catalog.data.gov/dataset/promoting-risk-reduction-among-young-adults-with-asthma-during-wildfire-smoke
    Explore at:
    Dataset updated
    Jan 16, 2023
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    Poor air quality (AQ) from wildfire smoke is associated with increased asthma attacks and emergency department visits. Young adults are less likely to adhere to AQ alerts than older adults. This study utilized data collected as part of a 2020 pilot study testing two smartphone application (app) interventions in young adults with asthma compared to a control group. NIH R21 1R21NR019071-01 (PI: Postma, Julie M) 4/1/2020-3/31/2022 Promoting Risk Reduction Among Young Adults with Asthma During Wild?re Smoke Events The goal of this proposal was to assess the feasibility of the `Smoke Sense,' an EPA-developed smart phone air quality application (app), to positively impact health outcomes among young adults with asthma. This study is a clinical intervention. This dataset is not publicly accessible because: The data is associated with the manuscript whose corresponding author is Dr. Julie Postma Assistant Dean of Research, Associate Professor Washington State University College of Nursing. It can be accessed through the following means: Assistant Dean of Research, Associate Professor Washington State University College of Nursing Email: jpostma@wsu.edu; Phone: 253-445-4612 WSU Puyallup Research and Extension Center 2606 W. Pioneer Ave, Puyallup, WA 98371-4998 www.nursing.edu. Format: Ascii flat files. This dataset is associated with the following publication: Postma, J., A. Rappold, T. Odom Maryon, H. Haverkamp, S. Amiri, R. Bindler, J. Whicker, and V. Walden. Promoting risk reduction among young adults with asthma during wildfire smoke. Public Health Nursing. John Wiley & Sons, Inc., Hoboken, NJ, USA, 39(2): 405-414, (2022).

  18. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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U.S. Environmental Protection Agency, Office of Policy, Office of Sustainable Communities (Publisher) (2025). Smart Location Database [Dataset]. https://catalog.data.gov/dataset/smart-location-database8
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Data from: Smart Location Database

Related Article
Explore at:
Dataset updated
Feb 25, 2025
Dataset provided by
United States Environmental Protection Agencyhttp://www.epa.gov/
Description

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

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