Reconstructing Historical Housing Data Using Kriging Interpolation and Zonal Statistics

Administrative boundaries in the United States frequently shift due to population growth and redistricting, complicating longitudinal analyses of census-based socio-economic data. In California, the number and configuration of census tracts and block groups have changed substantially between 1990 and 2020, often resulting in misaligned or missing historical data-especially for variables such as housing value. This study presents a geospatial methodology for reconstructing historical housing data using Kriging interpolation and zonal statistics. Kriging, a geostatistical method that accounts for spatial autocorrelation, was applied to estimate missing median house values across decades. The interpolated surfaces were then aggregated to consistent 2020 census block group geographic units via zonal statistics, enabling cross-temporal comparison on a uniform spatial basis. Using California as a case study, this work offers a reproducible framework for reconstructing historical datasets across evolving administrative boundaries, supporting more accurate spatial and socio-economic research.