Association for Public Policy Analysis & Management

Neighborhoods are not only where people live, but also where they frequently access various resources that are crucial to their well-being. Combining traditional socioeconomic data on neighborhood characteristics and a new type of geographical data on the locations of various resources, amenities, and services near neighborhoods (Google Maps), this paper explores the relationship between the spatial concentration of poverty and the spatial distribution of resources such as grocery stores and supermarkets, hospitals and pharmacies, schools and libraries, financial institutions, fire stations, post offices, parks, gyms, and public transit.

Previous research on similar topics such as “food desert” often has three limitations. First, most existing studies were only able to look at single cities and have limited generalizability. Second, previous studies often used Euclidian distances rather than travel time to measure proximity, which may have neglected disadvantages in terms of access to cars and public transportation and thus could be a poor measure of actual accessibility. Third, most studies only focused on the access to food and did not examine the access to other resources. Using data from Google Maps, this study aims to address these data and measurement issues.

The analytic sample in this paper consists of all 26,478 tracts in the 20 largest Metropolitan Statistical Areas in the U.S., which cover approximately 124.6 million people. I use web-scraping tools to retrieve data from Google Maps. For each tract, I locate the three closest places of interest for each of the 12 types of places (three closest hospitals, three closest banks, etc.) and obtain their coordinates as well as their names and other information provided by Google Maps. I then use Google Maps to calculate the distances between the tract and the nearby places of interest using five different measures: Euclidian distance, Manhattan distance, travel time by car, on foot, and by public transportation. Finally, I merge this data with the latest 5-year American Community Survey data.

The results show that 1) neighborhoods with higher poverty rates are farther away from almost all types of places examined; 2) when distance is measured in travel time by public transportation, poor neighborhoods are even more disadvantaged; 3) when vehicle occupancy is taken into consideration, the poorest quartile of neighborhoods on average spend between 50% and 80% more time traveling to access various resources.

The study makes three key contributions. First, the study contributes to our knowledge of the spatial concentration and multi-dimensionality of disadvantage, and has implications for the perpetuation and reproduction of disadvantage. Second, the study has policy implications in terms of urban planning, housing policies, public service provision, etc. Third, Google Maps data is a potentially powerful tool and data source that is largely underused in social sciences and policy research. This study serves as an example of how Google Maps data can be used to both answer important policy relevant questions and improve measurements and methods.