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OA/SmartLocationDatabase (MapServer)

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Service Description: Full Metadata 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. See https://www.epa.gov/smartgrowth/smart-location-mapping for more information.

Map Name: Smart Location Database V3

Legend

All Layers and Tables

Dynamic Legend

Dynamic All Layers

Layers: Description: Full Metadata 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. See https://www.epa.gov/smartgrowth/smart-location-mapping for more information.

Copyright Text: EPA Office of Community Revitalization and EPA National Geospatial Support Team

Spatial Reference: 102100  (3857)


Single Fused Map Cache: false

Initial Extent: Full Extent: Units: esriMeters

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

Document Info: Supports Dynamic Layers: true

MaxRecordCount: 1000

MaxImageHeight: 4096

MaxImageWidth: 4096

Supported Query Formats: JSON, geoJSON

Supports Query Data Elements:

Min Scale: 0

Max Scale: 0

Supports Datum Transformation: true



Child Resources:   Info   Dynamic Layer

Supported Operations:   Export Map   Identify   QueryDomains   QueryLegends   Find   Return Updates   Generate KML