Spatial Disaggregation of Population Subgroups Leveraging Self-Trained Multi-Output Gradient Boosting Regression Trees

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.</p

Investigating neighbourhood concentration of immigrants in Poland: explorative evidence from Kraków

Aim. This study contributes to research on new immigrant destinations in CEE by investigating the neighbourhood concentration of immigrants in Poland. We focus on Kraków – the second largest city – for which we have built a unique register-based dataset containing geocoded individual level data. To our knowledge, it is the first high-quality dataset of this type, prepared and used for research purposes in Poland. We use it to describe immigrants’ spatial allocation at a relatively early stage of immigration using the kNN approach. Results and conclusions. We find that whereas foreigners compose around 4.2% of city population, 50% of the city inhabitants live in the 200 kNNs with a share of foreigners below 2.2%. The DI for the immigrants is 0.45. Yet, a relatively high concentration could be seen among foreigners from Asia and America. However, immigrants from Ukraine and other Eastern European, non-EU countries are much more evenly spread around the city

Migration studies with a Compositional Data approach: a case study of population structure in the Capital Region of Denmark

Data normalization for removing the influence of population density in Population Geography is a common procedure that may come with an unperceived risk. In this regard, data are constrained to a constant sum and they are therefore not independent observations, a fundamental requirement for applying standard multivariate statistical tools. Compositional Data (CoDa) techniques were developed to solve the issues that the standard statistical tools have with close data (i.e., spurious correlations, predictions outside the range, and sub-compositional incoherence) but they are still not commonly used in the field. Hence, we present in this article a case study where we analyse at parish level the spatial distribution of Danes, Western migrants and non-Western migrants in the Capital region of Denmark. By applying CoDa techniques, we have been able to identify the spatial population segregation in the area and we have recognized some patterns that can be used for interpreting housing prices variations. Our exercise is a basic example of the potential of CoDa techniques, which generate more robust and reliable results than standard statistical procedures, but it can be generalized to other population datasets with more complex structures

A 3D Routing Service for Indoor Environments

Large and complex buildings with substantial numbers of visitors require fast and effective navigation support to help first-time and infrequent guests to easily find their destination and avoid stressful situations. Most existing solutions are based on in-situ localization and routing, therefore requiring expensive indoor positioning infrastructure. In contrast, the objective of this research is the development of a cheap and easily deployable indoor routing service that visitors can use to plan the route to their destination before their visit. It visualizes both the interior space of a building and its users’ individual routing paths in a virtual 3D environment. The proposed solution is entirely based on open source tools and has no installation requirements for the user. Its functionality is demonstrated in a building at the Aalborg University Copenhagen campus. This kind of ex-situ 3D digital navigation promises to help users gain a better understanding of the explored environment, and to improve people’ cognitive spatial maps when combined with animated stimuli and landmarks