While statistical information on socio-economic activities is widely available, the data are often collected or released only at a relatively aggregated level. In these aggregated forms, the data are useful for broad-scale assessments, although we often need to disaggregate the source data in order to provide more localized estimates, and in order to analyze correlations against geophysical variables. Spatial disaggregation techniques can be used in this context, to transform data from a set of source zones into a set of target zones, with different geometry and with a higher general level of spatial resolution. Still, few previous studies in the area have attempted to leverage state-of-the-art spatial disaggregation procedures in the context of socio-economic variables, instead focusing on applications related to population modeling. In this article, we report on experiments with a hybrid spatial disaggregation technique that combines state-of-the-art regression analysis procedures with the classic methods of dasymetric mapping and pycnophylactic interpolation. The hybrid procedure was used together with population density, land coverage, nighttime satellite imagery, and OpenStreetMap road density, as ancillary data to disaggregate different types of socio-economic indicators to a high-resolution grid. Our test specifically leveraged data relative to the Portuguese territory, resulting in the production of raster datasets with a resolution of 30 arc-seconds per cell. The article discusses the spatial disaggregation methodology and the quality of the obtained results under different experimental conditions.
|Journal||International Journal of Data Science and Analytics|
|Publication status||Published - 1 Mar 2018|
- Spatial analysis
- Geographic information systems
- Regression-based spatial disaggregation
- Socio-economic indicators