Investigating MAUP Effects on Census Data Using Approximately Equal-Population Aggregations (Short Paper)

Authors Yue Lin , Ningchuan Xiao



PDF
Thumbnail PDF

File

LIPIcs.GIScience.2023.47.pdf
  • Filesize: 0.63 MB
  • 6 pages

Document Identifiers

Author Details

Yue Lin
  • Department of Geography, The Ohio State University, Columbus, OH, USA
Ningchuan Xiao
  • Department of Geography, The Ohio State University, Columbus, OH, USA

Cite AsGet BibTex

Yue Lin and Ningchuan Xiao. Investigating MAUP Effects on Census Data Using Approximately Equal-Population Aggregations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 47:1-47:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.47

Abstract

The modifiable areal unit problem (MAUP) can significantly impact the use of census data as different choices in aggregating geographic zones can lead to varying outcomes. Previous research studied the effects using random aggregations, which, however, may lead to the use of impractical and unrealistic zones that deviate from recommended census geography criteria (e.g., equal population). To address this issue, this study proposes the use of approximately equal-population aggregations (AEPAs) for exploring MAUP effects on various statistical properties of census data, including Moran coefficients, correlation coefficients, and regression statistics. A multistart and recombination algorithm (MSRA) is used to generate multiple sets of high-quality AEPAs for testing MAUP effects. The results of our computational experiments highlight the need for more well-defined census geographies and realistic alternative zones to fully understand MAUP effects on census data.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Modeling and simulation
Keywords
  • Census
  • heuristics
  • modifiable areal unit problem
  • spatial aggregation
  • spatial autocorrelation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Samantha Cockings, Andrew Harfoot, David Martin, and Duncan Hornby. Maintaining existing zoning systems using automated zone-design techniques: methods for creating the 2011 census output geographies for england and wales. Environment and Planning A, 43(10):2399-2418, 2011. Google Scholar
  2. A Stewart Fotheringham and David WS Wong. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 23(7):1025-1044, 1991. Google Scholar
  3. Myung Jin Kim. Give-and-take heuristic model to political redistricting problems. Spatial Information Research, 27(5):539-552, 2019. Google Scholar
  4. David Martin. Optimizing census geography: The separation of collection and output geographies. International Journal of Geographical Information Science, 12(7):673-685, 1998. Google Scholar
  5. Stan Openshaw. A million or so correlation coefficients: Three experiments on the modifiable areal unit problem. In Statistical Applications in the Spatial Science, pages 127-144. Pion, 1979. Google Scholar
  6. Stan Openshaw. The Modifiable Areal Unit Problem. Geo Books, Norwich, 1983. Google Scholar
  7. Stan Openshaw and RS Baxter. Algorithm 3: A procedure to generate pseudo-random aggregations of n zones into m zones, where m is less than n. Environment and Planning A, 9(12):1423-1428, 1977. Google Scholar
  8. Stan Openshaw and Liang Rao. Algorithms for reengineering 1991 census geography. Environment and Planning A, 27(3):425-446, 1995. Google Scholar
  9. United States Census Bureau. Glossary: Census tract, 2022. URL: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13.
  10. Ningchuan Xiao, Peixuan Jiang, Myung Jin Kim, and Anuj Gadhave. A multistart heuristic approach to spatial aggregation problems. In International Conference on GIScience Short Paper Proceedings, volume 1, pages 349-351, 2016. Google Scholar