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Reconstructing Historical Housing Data Using Kriging Interpolation and Zonal Statistics.

Shuang Tian1, Fang Qiu2

  • 1University of Texas at Dallas, Richardson, TX, 75080, USA. Shuang.Tian@utdallas.edu.

Scientific Data
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study reconstructs historical housing data across changing US administrative boundaries. A geospatial method using Kriging interpolation and zonal statistics enables accurate, long-term socio-economic analysis.

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Area of Science:

  • Geospatial analysis
  • Socio-economic research
  • Geostatistics

Background:

  • Administrative boundaries shift, complicating longitudinal census data analysis.
  • California's census tracts changed significantly 1990-2020, causing data misalignment.
  • Historical housing data, like median house value, is often missing or inconsistent.

Purpose of the Study:

  • To develop a geospatial methodology for reconstructing historical housing data.
  • To enable accurate cross-temporal socio-economic analysis despite shifting boundaries.
  • To provide a reproducible framework for historical data reconstruction.

Main Methods:

  • Kriging interpolation to estimate missing median house values.
  • Geostatistical analysis accounting for spatial autocorrelation.
  • Zonal statistics to aggregate data to consistent 2020 census block groups.

Main Results:

  • Successfully reconstructed historical housing data for California.
  • Enabled cross-temporal comparisons on a uniform spatial basis.
  • Demonstrated a reproducible framework for addressing boundary changes.

Conclusions:

  • The geospatial methodology effectively reconstructs historical housing data.
  • This approach supports more accurate spatial and socio-economic research.
  • The framework is adaptable for other regions and datasets facing boundary evolution.