Next Article in Journal / Special Issue
Leveraging OSM and GEOBIA to Create and Update Forest Type Maps
Previous Article in Journal
The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets
Previous Article in Special Issue
Change Detection from Remote Sensing to Guide OpenStreetMap Labeling
Open AccessArticle

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

1
Institute of Spatial and Regional Planning, University of Stuttgart, 70569 Stuttgart, Germany
2
Administration de la Gestion de l’eau, Ministère de l’Environnement, du Climat et du Développement Durable, 4361 Esch-sur-Alzette, Luxembourg
3
Department of Ecology, Ecosystem and Landscape Ecology, University of Innsbruck, 6020 Innsbruck, Austria
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 498; https://doi.org/10.3390/ijgi9090498
Received: 1 May 2020 / Revised: 4 August 2020 / Accepted: 19 August 2020 / Published: 21 August 2020
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. View Full-Text
Keywords: indicators; machine learning; OpenStreetMap; vulnerability; resilience; climate change adaptation indicators; machine learning; OpenStreetMap; vulnerability; resilience; climate change adaptation
Show Figures

Figure 1

MDPI and ACS Style

Feldmeyer, D.; Meisch, C.; Sauter, H.; Birkmann, J. Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators. ISPRS Int. J. Geo-Inf. 2020, 9, 498.

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.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop