Next Article in Journal
Effects of Seismogenic Faults on the Predictive Mapping of Probability to Earthquake-Triggered Landslides
Previous Article in Journal
Decomposition of Repulsive Clusters in Complex Point Processes with Heterogeneous Components
Open AccessArticle

Spatial Disaggregation of Historical Census Data Leveraging Multiple Sources of Ancillary Information

1
IST/INESC-ID, Universidade de Lisboa, 1649-004 Lisboa, Portugal
2
Digital Humanities Hub, Lancaster University, Lancaster LA1 4YW, UK
3
FCT/NOVA LINCS, Universidade NOVA de Lisboa, 1099-085 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 327; https://doi.org/10.3390/ijgi8080327
Received: 30 May 2019 / Revised: 20 July 2019 / Accepted: 24 July 2019 / Published: 26 July 2019
  |  
PDF [22415 KB, uploaded 13 August 2019]
  |     |  

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. View Full-Text
Keywords: spatial disaggregation; regression analysis; deep learning; historical census data spatial disaggregation; regression analysis; deep learning; historical census data
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Monteiro, J.; Martins, B.; Murrieta-Flores, P.; Pires, J.M. Spatial Disaggregation of Historical Census Data Leveraging Multiple Sources of Ancillary Information. ISPRS Int. J. Geo-Inf. 2019, 8, 327.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top