Smart Location Database: A National Dataset for Characterizing Accessibility and the Built Environment at the Neighborhood Scale
- The Smart Location Database
- Sample uses of the Smart Location Database
- Enhancing the Smart Location Database
A large body of research has demonstrated that land use and the built environment can have a significant effect on travel behavior. An EPA-funded meta-analysis of this literature, "Travel and the Built Environment," summarized the measurable effects of built environment variables on residents’ travel behavior. It found that driving is most strongly related to measures of destination accessibility and street network design, while walking is most strongly related to measures of land use diversity, intersection density, and the number of destinations within walking distance.
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 such data more widely accessible.
The Smart Location Database
EPA’s Smart Location Database characterizes several built environment and regional accessibility variables for every census block group 1 in the United States. This dataset is available to the public for download, 2 web service, or viewing online . Table 1 lists key variables in Smart Location Database version 0.2b. Please review the technical documentation for a full description of all available variables, data sources, data currency, and known limitations. Note: EPA is currently updating and enhancing the Smart Location Database (see details below).
Table 1. Key Variables in the Smart Location Database
|Smart Location Database Variable||Year||Coverage|
|Total acres||Entire U.S.|
|Acres of private land excluding parks and conservation areas (for density calculations)||2011||Entire U.S.|
|Housing units||2010||Entire U.S.|
|Land use diversity||2009||Entire U.S.|
|Street intersection density||2010||Contiguous U.S.|
|Number of fixed-guideway 3 transit stops within ¼ mile||2009||Entire U.S.|
|Number of fixed-guideway transit stops within ½ mile||2009||Entire U.S.|
|Proportion of block group within ¼ mile of fixed-guideway transit||2009||Entire U.S.|
|Proportion of block group within ½ mile of fixed-guideway transit||2009||Entire U.S.|
|Working-age population within 30-minute transit commute||2008*||34 metropolitan regions|
|Jobs within 30-minute transit commute||2010*||34 metropolitan regions|
|Working-age population within 30 miles, distance weighted||2009||Contiguous U.S.|
|Jobs within 30 miles, distance weighted||2009||Contiguous U.S.|
* See technical documentation for more details on data currency.
This map shows one Smart Location Database variable mapped in the Portland, Oregon metropolitan region.
Sample Uses of the Smart Location Database
- Scenario planning and travel demand studies
The Smart Location Database can be used in scenario planning and travel demand studies when more detailed or consistent local data are not available. For instance, using elasticities found in the research literature,4 analysts can adjust outputs of travel or activity models that are otherwise insensitive to variation in the built environment. Similarly, this database can be used in sketch planning applications ranging from climate action plans to health impact assessments. A data exchange template is available for using Smart Location Database variables with scenario planning tools that employ the SPARC common data schema .
- Composite indicators of location efficiency
The Smart Location Database has been used to develop a composite indicator of location efficiency that summarizes several variables known to be correlated with driving and vehicle miles traveled. Such an indicator can be used to consistently compare the relative location efficiency of block groups within the same metropolitan region. EPA is currently working with the General Services Administration to develop and test such an indicator to evaluate federal facility locations.
- Nationwide studies that compare urban form among metropolitan regions
The Smart Location Database can be used in nationwide studies that compare metropolitan regions based on urban form characteristics. For instance, analysts could explore questions such as, What percentage of residents live in walkable neighborhoods? Another example is Residential Construction Trends in America’s Metropolitan Regions: 2012 Edition. This EPA study 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 used variables in the Smart Location Database in conjunction with impervious surface cover data from the National Land Cover Database to develop a model and spreadsheet tool for estimating new impervious surface growth associated with development scenarios. This model is sensitive not only to density of development but also centrality within the surrounding metropolitan region. For more details, see Impervious Surface Growth Model.
Enhancing the Smart Location Database
EPA is updating the Smart Location Database. The new version is expected to be available by early summer 2013. The update will feature:
- Census 2010 block group boundaries.
- Improved methodologies for calculating street intersection density, land use diversity, and several other variables.
- Several new demographic variables associated with travel demand.
- 2010 employment data with breakdowns in five key employment categories.
- Improved measures of transit accessibility with expanded coverage.
EPA will also be conducting research to measure the effect of built environment characteristics at the workplace location on employee travel behavior. This research will support the development of a new tool to estimate vehicle miles traveled and greenhouse gas emissions associated with commuting per worker based on workplace location.
For questions about the Smart Location Database and associated projects, please contact Kevin Ramsey (202-566-1153, firstname.lastname@example.org).