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/

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

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

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.

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

National walkability index in 2021. The National Walkability Index identifies areas with mixtures of land use and transportation infrastructure that may encourage walking as a mode of transportation. This index is comprised of four ranked measures: intersection density, distance to the nearest transit stop, employment diversity, and employment and housing diversity. More walkable areas rate higher on intersection density, have lower distances to the nearest transit stop, and have higher employment and employment plus housing diversity.The Environmental Protection Agency’s (EPA) Smart Location Database was created to address the demand for tools that compare location efficiency. The Smart Location Database (SLD) summarizes several demographic, employment, and built environment variables for every Census block group.

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

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]

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.

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.

The Walkability Index is intended to help address a growing demand for data products and tools that enable users to consistently compare multiple places based on their suitability for walking as a means of travel. It may be of use as source data for transportation or land use sketch planning tools.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). 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.

Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.

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

A collection of performance indicators for consistently comparing neighborhoods (census block groups) across the US in regards to their accessibility to jobs or workers via public transit service. Accessibility was modeled by calculating total travel time between block group centroids inclusive of walking to/from transit stops, wait times, and transfers. Block groups that can be accessed in 30 minutes or less from the origin block group are considered accessible. Indicators reflect public transit service in December 2012 and employment/worker counts in 2010. Coverage is limited to census block groups within metropolitan regions served by transit agencies who share their service data in a standardized format called GTFS. All variable names refer to variables in EPA's Smart Location Database. For instance EmpTot10_sum summarizes total employment (EmpTot10) in block groups that are reachable within a 30-minute transit and walking commute. See Smart Location Database User Guide for full variable descriptions.

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

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

The data is available as comma-separated-value (CSV) files and can be opened with any appropriate software.

The dataset is developed for measuring performance across 100 cities selected under the recent Smart Cities Mission in India. The data is primarily collected from Census of India 2011 publications.

This dataset provides a detailed insight into the daily activities of citizens in a futuristic smart city. It covers various aspects such as:

Demographics (Age, Gender) Mobility (Mode of Transport, Walking Steps) Lifestyle & Social Engagement (Work, Shopping, Entertainment, Social Media) Health & Well-being (Calories Burned, Sleep Hours) Energy & Sustainability (Home Energy Consumption, Carbon Footprint, Charging Station Usage) With 1000 rows and 15 columns, this dataset is ideal for data analysis, machine learning, and visualization projects related to urban mobility, sustainability, health trends, and behavioral analytics.

This dataset can be used to:

✅ Analyze citizen behavior trends

✅ Understand sustainable urban mobility

✅ Predict energy consumption patterns

✅ Identify health and social media habits

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services (LBS) across various sectors, including transportation, logistics, and e-commerce, is a primary driver. Furthermore, the proliferation of smartphones and connected devices, coupled with advancements in GPS technology and mapping software, continues to fuel market growth. The rising demand for high-resolution, real-time mapping data for autonomous vehicles and smart city initiatives also significantly contributes to market expansion. Competition among established players like Google, TomTom, and ESRI, alongside emerging innovative companies, is fostering continuous improvement in map accuracy, functionality, and data accessibility. This competitive landscape drives innovation and lowers costs, making digital maps increasingly accessible to a broader range of users and applications. However, market growth is not without its challenges. Data security and privacy concerns surrounding the collection and use of location data represent a significant restraint. Ensuring data accuracy and maintaining up-to-date map information in rapidly changing environments also pose operational hurdles. Regulatory compliance with differing data privacy laws across various jurisdictions adds another layer of complexity. Despite these challenges, the long-term outlook for the digital map market remains positive, driven by the relentless integration of location intelligence into nearly every facet of modern life, from personal navigation to complex enterprise logistics solutions. The market's segmentation (although not explicitly provided) likely includes various map types (e.g., road maps, satellite imagery, 3D maps), pricing models (subscriptions, one-time purchases), and industry verticals served. This diversified market structure further underscores its resilience and potential for sustained growth. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.