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Because Muncie's Densities Are Not Manhattan's: Using Geographical Weighting in the EM Algorithm for Areal
Jonathan P Schroeder1, David C Van Riper1
1Minnesota Population Center, University of Minnesota, Minneapolis, MN, USA.
Geographical Analysis
|March 22, 2014
Summary
A new geographically weighted expectation-maximization (GWEM) algorithm improves spatial data interpolation accuracy. Hybrid approaches combining GWEM with target-density weighting (TDW) offer substantial improvements for population density estimation.
Area of Science:
- Geographic Information Science
- Spatial Statistics
- Geocomputation
Background:
- Areal interpolation estimates variable distributions across zones using source data.
- Ancillary control zones often guide interpolation by estimating variable density.
- Existing methods may lack spatial variability in density estimation.
Purpose of the Study:
- Introduce a novel density estimation approach: geographically weighted expectation-maximization (GWEM).
- Evaluate GWEM's accuracy for areal interpolation using land-use/land-cover data and US census data.
- Compare GWEM with existing methods and explore hybrid approaches.
Main Methods:
- Developed the geographically weighted expectation-maximization (GWEM) algorithm, integrating expectation-maximization (EM) and geographically weighted regression.
- Applied GWEM to estimate population density using 1980 US census tract data and land-use/land-cover ancillary data.
- Compared GWEM performance against traditional methods and target-density weighting (TDW) using 1970 census data.
Main Results:
- GWEM demonstrated higher accuracy than several previously studied interpolation methods.
- Target-density weighting (TDW) using 1970 data often outperformed GWEM.
- Hybrid GWEM-TDW approaches significantly improved population density estimation accuracy.
Conclusions:
- The GWEM algorithm offers a more accurate and spatially flexible approach to density estimation in areal interpolation.
- Hybrid methods combining GWEM with TDW show strong potential for enhancing spatial data interpolation.
- Findings have implications for demographic analysis and spatial modeling requiring accurate areal interpolation.
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