Spatial Disaggregation of Historical Census DataLeveraging Multiple Sources of Ancillary Information

Monteiro, João and Martins, Bruno and Murrieta-Flores, Patricia and Pires, João Moura (2019) Spatial Disaggregation of Historical Census DataLeveraging Multiple Sources of Ancillary Information. ISPRS International Journal of Geo-Information, 8 (8).

Full text not available from this repository.

Abstract

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

Item Type:
Journal Article
Journal or Publication Title:
ISPRS International Journal of Geo-Information
Subjects:
ID Code:
143039
Deposited By:
Deposited On:
07 Apr 2020 12:40
Refereed?:
Yes
Published?:
Published
Last Modified:
18 Oct 2020 05:24