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 produc-
High-resolution population grids built from historical census data can ease the analyses ofgeographical population changes, at the same time also facilitating the combination of populationdata with other GIS layers to perform analyses on a wide range of topics. This article reports onexperiments with a hybrid spatial disaggregation technique that combines the ideas of dasymetricmapping and pycnophylactic interpolation, using modern machine learning methods to combinedifferent types of ancillary variables, in order to disaggregate historical census data into a 200 mresolution grid. We specifically report on experiments related to the disaggregation of historicalpopulation counts from three different national censuses which took place around 1900, respectively inGreat Britain, Belgium, and the Netherlands. The obtained results indicate that the proposed methodis indeed highly accurate, outperforming simpler disaggregation schemes based on mass-preservingareal weighting or pycnophylactic interpolation. The best results were obtained using modernregression methods (i.e., gradient tree boosting or convolutional neural networks, depending on thecase study), which previously have only seldom been used for spatial disaggregation.
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