Smart Location Database
A Resource for Measuring Location Efficiency and the Built Environment
- The Smart Location Database
- Sample Variables Included in the Smart Location Database
- Uses of the Smart Location Database
A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. An EPA-funded meta-analysis of this literature, "Travel and the Built Environment," summarized the measurable effects of several built environment variables on residents’ travel behavior. These findings can help to inform travel demand studies as well as land use scenario impact analysis. However, developing data about these built environment characteristics can be expensive and time consuming. EPA’s Smart Location Database makes these data more widely accessible.
The Smart Location Database
|Ways to Access the Smart Location Database|
|Interactive map viewer 3|
|Download data for your community 4|
|Download data for the entire nation:|
|Web services 5|
The Smart Location Database is a nationwide geographic data resource for measuring location efficiency.1 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 2 in the United States.
EPA first released the Smart Location Database in 2011 and released version 2.0 in July 2013. Please review the user guide (PDF) (35 pp, 1.22MB, About PDF) for a full description of all available variables, data sources, data currency, and known limitations.
Below is a map that illustrates one variable available in the Smart Location Database. The map shows patterns of spatial variation in transit service availability and density within Los Angeles and its surrounding cities and suburbs.
Transit Service Density in the Los Angeles Metropolitan Region
(Aggregate frequency of transit service per hour per square mile during evening peak period) Source: EPA analysis of public transit service data available in GTFS format from Metro-Los Angeles, Metrolink Trains, Municipal Area Express (MAX), Torrance Transit, and Riverside Transit Agency
Sample Variables Included in the Smart Location Database
|Density||Gross residential density (housing units per acre) on unprotected land
Gross population density (people per acre) on unprotected acre
Gross employment density (jobs per acre) on unprotected acre
|Diversity of land use||Jobs per housing unit
Employment entropy (a measure of employment diversity)
Employment and housing entropy
|Urban design||Street intersections per square mile
High-speed road network density
|Transit service (available in areas with transit agencies that share service data in the GTFS format )||Aggregate transit service frequency, afternoon peak period
Transit service density, afternoon peak period
Distance to nearest transit stop
|Destination accessibility by transit (only in areas with GTFS data availability)||Jobs within a 45-minute transit commute
Working-age population within a 45-minute transit commute
|Destination accessibility by car||Jobs within a 45-minute drive
Working-age population within a 45-minute drive
|Demographics||Percentage of households with no car, 1 car, or 2 or more cars
Percentage of workers that are low, medium, or high wage (by home and work locations)
|Employment||Employment totals broken down by 5-tier classification scheme
Employment totals broken down by 8-tier classification scheme
Uses of the Smart Location Database
- Accessing and comparing neighborhood conditions
Users can browse a simple interactive map to assess and compare conditions across different neighborhoods in their communities.
- Developing indicators of location efficiency
EPA is using the Smart Location Database to develop simple indicators of location efficiency. For instance, EPA is working with the U.S. General Services Administration to develop a Smart Location Index that scores census block groups based on their built environment and accessibility characteristics. Block groups that are associated with reduced vehicle miles traveled receive higher scores compared to other block groups within the same metropolitan region. Advanced users could create similar composite indicators to compare walkability, compact design, or other built environment characteristics. EPA hopes to include additional indicators of location efficiency in forthcoming updates to the Smart Location Database.
- Scenario planning and travel demand modeling
Planners can use the Smart Location Database as baseline data in scenario planning, sketch planning, and travel demand studies when more detailed or consistent local data are unavailable. Analysts can also use elasticities found in the research literature6 to adjust outputs of travel or activity models that are otherwise insensitive to variation in the built environment.
- Conducting nationwide research studies and developing tools
Building on previous research, EPA is conducting a nationwide modeling study to predict employee commute travel (e.g., average trip distances, mode share, vehicle miles traveled, etc.) based on workplace neighborhood characteristics. If successful, this study will produce equations for refining travel demand models. This study and others like it also make it possible to create simple online tools to help more communities analyze the potential outcomes of proposed land use development.
- Comparing urban form among metropolitan regions
Researchers can use the Smart Location Database in nationwide studies that compare metropolitan regions based on urban form characteristics. For instance, analysts could determine the percentage of residents that live in walkable or transit-rich neighborhoods. EPA’s 2012 study Residential Construction Trends in America’s Metropolitan Regions used the Smart Location Database in conjunction with data from the National Land Cover Database and the American Community Survey to measure and compare infill housing development.
- Modeling impervious surface growth
EPA analyzed variables in a previous version of the Smart Location Database and the National Land Cover Database to create a model and simple spreadsheet tool for estimating new impervious surface growth associated with land use development scenarios. This model is sensitive not only to density of development but also to its relative centrality within the surrounding metropolitan region. For details, see EPA’s Impervious Surface Growth Model.
For questions about the Smart Location Database and associated projects, please contact Kevin Ramsey (202-566-1153, firstname.lastname@example.org).