Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper)

Authors Angela R. Cunningham , Joseph V. Tuccillo , Tyler J. Frazier

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Author Details

Angela R. Cunningham
  • Oak Ridge National Laboratory, TN, USA
Joseph V. Tuccillo
  • Oak Ridge National Laboratory, TN, USA
Tyler J. Frazier
  • Oak Ridge National Laboratory, TN, USA


Our thanks to Daniel Adams and Clinton Stipek. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (

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Angela R. Cunningham, Joseph V. Tuccillo, and Tyler J. Frazier. Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 25:1-25:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Population modeling requires clear definitions of socioeconomic status (SES) to ensure overall estimate accuracy and locate potentially underserved subpopulations. This presents a challenge as SES can be measured in myriad ways and for divergent purposes, and the data required to calculate these metrics may be lacking, particularly in low and middle income countries (LMICs). To support more refined SES measurement, we explore improvements upon the Demographic and Health Survey’s (DHS) Wealth Index (DHS-WI) using alternative characterizations of SES based on multiple correspondence analysis (MCA) and hierarchical clustering. We produce the MCA-derived metrics first on a full suite of household economic, demographic, and dwelling variables, then on a reduced set of occupant-only SES characteristics. We explore the utility of these metrics relative to DHS-WI based on their ability to 1) differentiate DHS household types and 2) identify mixtures of SES levels within DHS samples and mapped at high spatial resolution. We find that our full suite MCA yields more clearly defined SES segments and that our reduced MCA delineates occupant SES most clearly, suggesting potential pathways to improve upon the DHS-WI in future population modeling efforts for LMICs.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Demographic and Health Survey
  • multiple correspondence analysis
  • population modeling
  • socioeconomic status
  • spatial statistics


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