Mobility and Poverty in Baltimore City

At the 2013 Federal Reserve Community Development Research Conference a panel presented research examining the relationship between “place” and socioeconomic outcomes for families. Their findings linked patterns of housing turnover and unstable housing conditions to high poverty neighborhoods.

Community-level data, or data available for small areas of geography, make this kind of research possible. The American Community Survey (ACS) is one such source of community-level demographic and socioeconomic data.  The survey, an especially rich, although recently controversial Big Data resource, offers a wide range of data elements for areas as small as the census block and data as specific as the percent of carpoolers working in service occupations in a census tract. Detailed residential mobility data recently available from the ACS allow us to look at the characteristics of residents who recently moved to a neighborhood, including their poverty status.

In the map created using Tableau, mobility and poverty data for Baltimore City census tracts have been aggregated to the Community Statistical Area (CSA), or census tracts aggregated to represent Baltimore communities. The map on the left shows the percentage of residents currently living in a community who resided in a different house in the previous year. The map on the right displays the percentage of those “recent movers” who had incomes below the poverty threshold. For the highlighted communities– Greater Rosemont, Lauraville and Southwest Baltimore– both percentages can be viewed and compared in the table below the maps.

You can click and highlight other CSAs in the map or you can choose a CSA from the drop-down menu. If you click outside of the city boundary to see data for all Baltimore communities, you can see that areas in northeast and northwest Baltimore have the lowest rates of mobility and those communities in the central and downtown areas of the city have the highest. We can also see that the communities with the highest percentages of “movers” with incomes below poverty are concentrated across the inner core of the city.

When we combine poverty and mobility data in this way, important patterns begin to emerge. The data suggest that there are three such patterns for most CSAs:

      1. Residents are consistently transient with similar percentages of recent movers for all residents and for those with incomes below poverty (South Baltimore).
      2. Low-income residents are slightly more mobile than other residents (Lauraville).
      3. Residents with incomes below poverty experience significant housing instability appears to be significant as they represent the majority of movers where most residents in the neighborhood overall are stable (Greater Rosemont).


This information can be used in future research or for policymaking. For instance perhaps we could begin to look at the availability of affordable and quality housing options for low-income families in neighborhoods with high turnover. Or we could examine whether there is relationship between school attendance and high rates of mobility in a students’ neighborhood and explore solutions.

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