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

• Published in: ISPRS international journal of geo-information Vol. 9; no. 9; p. 498
• Main Authors: Feldmeyer, Daniel, Meisch, Claude, Sauter, Holger, Birkmann, Joern
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2020
• Subjects: Adaptation; Algorithms; Artificial intelligence; Boundaries; Climate adaptation; Climate change; Climate change adaptation; Climatic changes; Computation; Crowdsourcing; Datasets; Deep learning; Digital map services; Digital mapping; Economic indicators; Economics; Emigration; Geospatial data; Indicators; Influence; Learning algorithms; Machine learning; Mathematical models; Monitoring; Municipalities; Neural networks; Older people; OpenStreetMap; Principal components analysis; Regression analysis; Regression models; resilience; Resolution; Socioeconomic aspects; Socioeconomics; Spatial variations; Statistical analysis; Statistical models; Sustainability; Sustainable development; Temporal resolution; Unemployment; Vulnerability; Germany
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi9090498

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.