Back to Search Start Over

Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators

Authors :
Daniel Feldmeyer
Claude Meisch
Holger Sauter
Joern Birkmann
Source :
ISPRS International Journal of Geo-Information, Vol 9, Iss 9, p 498 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities.

Details

Language :
English
ISSN :
22209964
Volume :
9
Issue :
9
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.8824b8ba75f44030b69032672bf29cb1
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi9090498