DOI: 10.5281/zenodo.7854525
title: From Paris to Prague: A Standardized European POI Dataset Based on OpenStreetMap Data

description: Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses, examine POI spatial distributions, conduct comparative regional studies, enhancing understanding of the economic activity, distribution, attractions, and subsequently, economic health, growth potential, and cultural opportunities. The paper describes the materials and methods used in generating the dataset, including OSM data retrieval, processing, standardization, and hexagonal grid generation. The dataset can be used independently or integrated with other relevant datasets for more comprehensive spatial distribution studies in future research.
description: {"references": ["OpenStreetMap contributors, OpenStreetMap. (2023). https://www.openstreetmap.org.", "A. Psyllidis, S. Gao, Y. Hu, E.-K. Kim, G. McKenzie, R. Purves, M. Yuan, C. Andris, Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the future, Comput. Urban Sci. 2 (2022). https://doi.org/10.1007/s43762-022-00047-w.", "M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis, Building a Framework for Predictive Science, ArXiv [Cs.MS]. (2012). http://arxiv.org/abs/1202.1056 (accessed April 21, 2023).", "osmosis: Osmosis is a command line Java application for processing OSM data, Github, n.d. https://github.com/openstreetmap/osmosis (accessed April 21, 2023).", "K. Jordahl, J. Van den Bossche, M. Fleischmann, J. Wasserman, J. McBride, J. Gerard, J. Tratner, M. Perry, A.G. Badaracco, C. Farmer, G.A. Hjelle, A.D. Snow, M. Cochran, S. Gillies, L. Culbertson, M. Bartos, N. Eubank, maxalbert, A. Bilogur, S. Rey, C. Ren, D. Arribas-Bel, L. Wasser, L.J. Wolf, M. Journois, J. Wilson, A. Greenhall, C. Holdgraf, Filipe, F. Leblanc, geopandas/geopandas: v0.8.1, Zenodo, 2020. https://doi.org/10.5281/ZENODO.3946761.", "L. Tsiattalou, tfidf_matcher: TFIDF / KNN based string matching, Github, n.d. https://github.com/LouisTsiattalou/tfidf_matcher (accessed April 21, 2023).", "h3: Hexagonal hierarchical geospatial indexing system, Github, n.d. https://github.com/uber/h3 (accessed December 14, 2022).", "tobler: Spatial interpolation, Dasymetric Mapping, & Change of Support, Github, n.d. https://github.com/pysal/tobler (accessed April 21, 2023)."]}