LIPIcs.GIScience.2023.47.pdf
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Investigating MAUP Effects on Census Data Using Approximately Equal-Population Aggregations (Short Paper)
Authors
Yue Lin 
,
Ningchuan Xiao
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12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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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
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References
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