Center for Spatial Studies

Geodemographic classifications are an important tool to support public-service decision making. While people are the focal point of geodemographics, classifications are often built on variables that describe populations rather than individuals. Synthetic populations, model-based approximations of the individual makeup of small census areas, remain largely unused for geodemographic classification, yet they can provide a more direct and holistic understanding of localized resource needs than existing approaches. This paper develops a new method for performing individual-centered geodemographic classifications using synthetic populations. The building blocks of this approach are abstractions of the synthetic population attributed to each small census area via affinity matrices computed from similarities in both the size and attributes among individuals. Using a rank-1 spectral decomposition of an area’s affinity matrix enables rapid computation of a dissimilarity metric which is compatible with cluster analysis techniques used in traditional geodemographic classifications. Using data from the American Community Survey (ACS), an example classification is developed for the Knoxville, TN, USA Public-Use Microdata Area (PUMA) to illustrate how distinctions can be drawn among small census areas in terms of specific types of representative individuals, providing a more tailored view of the groups that serve to benefit from spatial policy interventions. Beyond improving traditional public-domain geodemographic classifications, this approach provides a novel open-source alternative to commercial neighborhood segmentation products with added flexibility for custom research applications.