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Spatiotemporal aggregation for temporally extensive international microdata.

Tracy A Kugler1, Steven M Manson2, Joshua R Donato3

  • 1Minnesota Population Center, University of Minnesota, 50 Willey Hall, 225 19th Avenue South, Minneapolis, MN 55455, USA, phone: +1-612-626-3933, fax: +1-612-626-8375.

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Summary
This summary is machine-generated.

This study presents a method for creating consistent small geographic areas from census data, even when boundaries change. This approach enhances data usability for policy and research by combining individual-level detail with spatial specificity.

Keywords:
census microdatacluster analysisregionalization

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Area of Science:

  • Geographic Information Systems (GIS)
  • Spatial Analysis
  • Demography

Background:

  • Census microdata offers individual-level detail but lacks spatial specificity.
  • Aggregate data provides spatial specificity but lacks individual-level flexibility.
  • Challenges include ensuring data confidentiality and harmonizing changing administrative boundaries over time.

Purpose of the Study:

  • To develop a strategy for regionalizing subnational administrative units.
  • To harmonize changes in unit boundaries over time for consistent geographic identifiers.
  • To provide small-area geographic identifiers for census microdata to support policy and research.

Main Methods:

  • A regionalization and harmonization strategy was developed.
  • The strategy creates units that satisfy spatial and other constraints.
  • Tested on case studies from Malawi, Brazil, and the United States, employing various algorithms.
  • A semi-automated strategy was developed to meet data restrictions, computational, and user demands.

Main Results:

  • Successfully created a strategy for regionalizing and harmonizing administrative units.
  • Demonstrated the application of the strategy for generating small-area geographic identifiers.
  • Validated the approach through case studies in Malawi, Brazil, and the United States.
  • Developed a semi-automated method balancing data constraints and user needs.

Conclusions:

  • The described strategy effectively regionalizes subnational units and harmonizes boundary changes.
  • This provides valuable small-area geographic identifiers for census microdata.
  • The approach enhances the utility of microdata for spatial analysis, policy-making, and research.