Extraction of Crowdsourced Points of Interest Data

Year 2022, Volume: 3 Issue: 1, 34 - 49, 14.03.2022

Gülten Kara , Çetin Cömert , Huriye Akcan

https://doi.org/10.48123/rsgis.1057947

Abstract

Today, the amount of spatial data produced by users on the web is increasing rapidly day by day. This, of course, makes spatial data on the web into an important source of information especially for the development of spatial applications. Particularly with the open data policy, the spread and success of crowdsourced initiatives has prompted spatial data producer institutions and organizations to evaluate the possibilities of using crowdsourced geographic information in data collection. In this context, national mapping agencies either use open map resources created with a crowdsourcing approach to update their databases or develop mobile and web applications to collect data with a volunteered geographic information approach. From this point of view, in this study, POI data determined as part of the national mapping agency applications have been extracted from OpenStreetMap, Wikimapia and GoogleMaps map services and the differences between data sources have been assessed.

Keywords

Crowdsourcing, Volunteered geographic information, Points of interest, OpenStreetMap, Wikimapia, GoogleMaps

Kitle Kaynak POI verilerinin Çıkarılması

Abstract

Günümüzde web üzerinde kullanıcılar tarafından üretilen konumsal verinin miktarı her geçen gün büyük bir hızla artmaktadır. Bu da, özellikle konumsal uygulamaların geliştirilmesi için web üzerindeki konumsal verileri önemli bir bilgi kaynağı haline getirmektedir. Özellikle açık veri politikası ile birlikte kitle kaynak girişimlerinin yaygınlaşması ve başarısı, konumsal veri üreticisi kurum ve kuruluşların veri toplamada kitle kaynaklı coğrafi bilgileri kullanma olasılıklarını değerlendirmeye sevk etmiştir. Bu bağlamda ulusal harita kurumları, veri tabanlarını güncellemek için ya kitle kaynak yaklaşımıyla oluşturulan açık harita kaynaklarını kullanmakta ya da gönüllü coğrafi bilgi yaklaşımıyla veri toplamak için mobil ve web uygulamaları geliştirmektedir. Bu noktadan hareketle, Harita Genel Müdürlüğü uygulamaları kapsamında belirlenen POI verileri, OpenStreetMap, Wikimapia ve GoogleMaps harita servislerinden çıkarılarak veri kaynakları arasındaki farklılıklar irdelenmiştir.

Keywords

Kitle kaynak, Gönüllü coğrafi bilgi, İlgi çekici nokta, OpenStreetMap, Wikimapia, GoogleMaps

References

• Andrade, R., Alves, A., & Bento, C. (2020). POI mining for land use classification: A case study. ISPRS International Journal of Geo-Information, 9(9), 493, doi: 10.3390/ijgi9090493.
• Bakillah, M., Liang, S., Mobasheri, A., Jokar Arsanjani, J., & Zipf, A. (2014). Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science, 28(9), 1940-1963.
• Bao, J., Xu, C., Liu, P., & Wang, W. (2017). Exploring bikesharing travel patterns and trip purposes using smart card data and online point of interests. Networks and Spatial Economics, 17(4), 1231-1253.
• Bast, H., Brosi, P., Kalmbach, J., & Lehmann, A. (2021, November). An efficient RDF converter and SPARQL endpoint for the complete OpenStreetMap data. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems, 2021. (pp. 536-539). SIGSPATIAL'21.
• Chen, L., Zhang, D., Pan, G., Ma, X., Yang, D., Kushlev, K., Zhang, W., & Li, S. (2015, September). Bike sharing station placement leveraging heterogeneous urban open data. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015. (pp. 571-575). UbiComp'15.
• Cheng, F., Liu, S., Hou, X., Zhang, Y., Dong, S., Coxixo, A., & Liu, G. (2018). Urban land extraction using DMSP/OLS nighttime light data and OpenStreetMap datasets for cities in China at different development levels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2587-2599.
• Ciepłuch, B., Jacob, R., Mooney, P., & Winstanley, A. C. (2010, July). Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. In Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resuorces and Enviromental Sciences, 2010. (pp. 337).
• GoogleMaps API. (2021, October 17). Google maps platform, Retrieved from https://developers.google.com/maps. Klinkhardt, C., Woerle, T., Briem, L., Heilig, M., Kagerbauer, M., & Vortisch, P. (2021). Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models. Transportation Research Record, 2675(8), 294-303.
• Lamprianidis, G., Skoutas, D., Papatheodorou, G., & Pfoser, D. (2014, November). Extraction, integration and analysis of crowdsourced points of interest from multiple web sources. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, 2014. (pp. 16-23). GeoCrowd’14.
• Mummidi, L. N., & Krumm, J. (2008). Discovering points of interest from users’ map annotations. GeoJournal, 72(3), 215-227.
• OSM. (2021, January 22). Open Street Map, Retrieved from https://www.openstreetmap.org/#map=19/39.86674/ 32.75063
• OSM Sophox. (2021, May 15). OSM Sophox, Retrieved from https://sophox.org
• OSM Wiki. (2021, February 19). Map Features, Retrieved from https://wiki.openstreetmap.org/wiki/Map_features
• QLever UI. (2021, June). QLever UI, Retrieved from http://osm2ttl.cs.uni-freiburg.de:8080/osm-planet/TwJREB
• Ruta, M., Scioscia, F., De Filippis, D., Ieva, S., Binetti, M., & Di Sciascio, E. (2014). A semantic-enhanced augmented reality tool for OpenStreetMap POI discovery. Transportation Research Procedia, 3(2014), 479-488.
• Touya, G., Antoniou, V., Olteanu-Raimond, A. M., & Van Damme, M. D. (2017). Assessing crowdsourced POI quality: Combining methods based on reference data, history, and spatial relations. ISPRS International Journal of Geo-Information, 6(3), 80, doi: 10.3390/ijgi6030080.
• W3C. (2012, March 16). W3C points of interest core, Retrieved from https://www.w3.org/2010/POI/documents/Core/core-20111216.html
• Wang, Z., Ma, D., Sun, D., & Zhang, J. (2021). Identification and analysis of urban functional area in Hangzhou based on OSM and POI data. PLoS ONE, 16(5), 1-20.
• Wikimapia. (2021, February 10). Wikimapia - Let's describe the whole world!, Retrieved from https://wikimapia.org/#lang=tr&lat=40.162100&lon=29.065900&z=12&m=w
• Zhang, Y., Gao, M., Zhang, X., Yang, P., Ma, Q., Wang, C., ... & Hu, X. (2018). An automatic approach to extracting geographic information from Internet. IEEE Access, 6, 36732-36743.