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.

Accurate and consistent estimations on the present and future population distribution, at fine spatial resolution, are fundamental to support a variety of activities. However, the sampling regime, sample size, and methods used to collect census data are heterogeneous across temporal periods and/or geographic regions. Moreover, the data is usually only made available in aggregated form, to ensure privacy. In an attempt to address these issues, several previous initiatives have addressed the use of spatial disaggregation methods to produce high-resolution gridded datasets describing the human population distribution, although these projects have usually not addressed specific population subgroups. This paper describes a spatial disaggregation method based on self-training regression models, innovating over previous studies in the simultaneous prediction of disaggregated counts for multiple inter-related variables, by leveraging multi-output models based on gradient tree boosting. We report on experiments for two case studies, using high-resolution data (i.e., counts for different subgroups available at a resolution of 100 meters) for the municipality of Amsterdam and the region of Greater Copenhagen. Results show that the proposed approach can capture spatial heterogeneity and the dependency on local factors, outperforming alternatives (e.g., seminal disaggregation algorithms, or approaches leveraging individual regression models for each variable) in terms of averaged error metrics, and also upon visual inspection of spatial variation in the resulting maps.

Following the increasing adoption of online social networks, the users of those services can now easily collect and transmit various types of data instantaneously from real-world locations (e.g., geo-referenced pictures, videos, messages, etc.). This paper argues that one can take advantage of the information that is spread throughout social networks like Twitter, in order to provide a realistic solution for situational awareness that can be useful for decision makers during particular events. Through the generation of English captions assigned to geo-referenced photographs extracted from tweets, and by using these captions to infer important features of those photographs, social monitoring approaches based on the images can produce prompt contextual information that helps in disaster monitoring and response. We specifically describe the use of the Show and Tell model for this purpose. We managed to obtain 100% of precision, 94.42% of accuracy, and 93.93% of recall, in the task of classifying geo-referenced Twitter images depicting the accumulation of snow, useful for applications related to snow removal.