Economic Dynamics of Aging Suburbs

Final Project ReportDownload
The blue neighborhood was built in the decades after WWII. The yellow neighborhood was built during the Clinton administration. Does increased sprawl in the latter predict economic challenges in the former?

Sacramento Area Council of Governments (SACOG) is the metropolitan planning organization (MPO) for the Sacramento CA region, and works energetically to integrate transportation, land use, and air quality planning as mandated by California law SB 375. SACOG funded a research project designed to characterize the changing economic fortunes of American suburbs over the 30-year period from 1980 to 2010, to identify aging suburbs build in the ’50s, ’60s, and ’70s, and to examine whether aging suburbs struggle more economically when joined by new green-fields suburbs built out post-1980. By analyzing the factors associated with the economic dynamics of aging, troubled suburbs, SACOG works to empower local communities who seek to craft policies that improve their citizen’s well-being.

Homogeneous middle-aged suburbs (HMSs) – census tracts whose housing stock was predominantly constructed during a given decade – in the Sacramento CA metro area. Brightly colored tracts in top left, top right, and bottom left are HMSs built in the 1950’s (red), 1960’s (yellow) and 1970’s (green). In each of these maps, “Greenfield neighbors” – tracts that developed in a later decade – are shown in gray. The map in the bottom left shows all tracts in the region, colored by mean year of housing unit construction.

In order to investigate this question, I assembled a longitudinal index of Community Economic Condition (CECI) from a database of variables associated with housing units (the physical plant of suburban communities), workforce (the economic muscles of the community), and households (the resident human population of these communities). The Index and each of its component variables were collected from decennial census data at the level of census tract, for the maximum combination of spatial breadth and temporal depth (the final portion of the national territory was only first tracted out in the 1990; in the 2010 census these areas contained some of the least densely populated regions of the country, holding about 10% of the nation’s present population).

2010 Census tracts included in the study

Performing this research required assembling, cleaning, adjusting for inflation, and reallocating to consistent 2010 tract boundaries a database of 16 indicator variables for every tract in the country and repeated over four decennial epochs (1980 through 2010). This database allowed me (and now SACOG planners) to ask questions and test hypotheses about the complex stories of rising and falling neighborhood fortunes in different parts of the country.

Click through this image to interact with a live dashboard of these data

I did all spatial data processing using ArcGIS for Desktop, with tabular data visualizations using Tableau and statistical analysis in SPSS.

  • Step 1: select a focal HMS
  • Step 2: compute sprawl metric for all Greenfield neighbors to the focal HMS
  • Step 3: Select only those Greenfield neighbors within a 3 mile radius of the focal HMS
  • Step 4: Identify the Greenfield’s neighbor with largest value on the sprawl metric
  • Step 5: associate the sprawl metric for that neighbor with the focal HMS. This becomes the predictor variable for CECI response.