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Enhancing Areal Interpolation Frameworks through Dasymetric Refinement to Create Consistent Population Estimates
Hamidreza Zoraghein1, Stefan Leyk1
1Department of Geography, University of Colorado Boulder, Boulder, USA.
International Journal of Geographical Information Science : IJGIS
|March 20, 2019
Summary
This study improves population estimates by refining areal interpolation methods. Dasymetric refinement using parcel data and land cover enhances accuracy for small area population dynamics research.
Area of Science:
- Demography
- Geographic Information Systems (GIS)
- Spatial Analysis
Background:
- Accurate micro-scale population dynamics require consistent small area data.
- Changing census boundaries between survey years create challenges for temporal analysis.
- Existing areal interpolation methods can be limited by boundary shifts.
Purpose of the Study:
- To develop accurate and consistent population estimates across changing census boundaries.
- To advance areal interpolation methods using dasymetric refinement.
- To evaluate the effectiveness of different dasymetric refinement strategies for population estimation.
Main Methods:
- Areal interpolation with three levels of dasymetric refinement.
- Utilizing residential parcels as binary and housing type ancillary variables.
- Employing Expectation Maximization (EM) algorithm.
- Incorporating road buffers and developed land cover classes for refinement.
Main Results:
- All three levels of dasymetric refinement effectively reduced population estimation errors.
- Different geographic and demographic settings showed varying degrees of accuracy improvement.
- Combining refinement strategies can further enhance accuracy in specific areas.
Conclusions:
- Dasymetric refinement significantly improves population estimates consistency and accuracy.
- The methods provide a foundation for advanced spatio-temporal demographic research.
- The study highlights the importance of ancillary data in spatial demographic analysis.
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