Central Places in Wikipedia
Central Place Theory explains the number and locations of cities, towns, and villages based on principles of market areas, transportation, and socio-political interactions between settlements. It assumes a hexagonal segmentation of space, where every central place is surrounded by six lower-order settlements in its range, to which it caters its goods and services. In reality, this ideal hexagonal model is often skewed based on varying population densities, locations of natural features and resources, and other factors. In this paper, we propose an approach that extracts the structure around a central place and its range from the link structure on the Web. Using a corpus of georeferenced documents from the English language edition of Wikipedia, we combine weighted links between places and semantic annotations to compute the convex hull of a central place, marking its range. We compare the results obtained to the structures predicted by Central Place Theory, demonstrating that the Web and its hyperlink structure can indeed be used to infer spatial structures in the real world. We demonstrate our approach for the four largest metropolitan areas in the United States, namely New York City, Los Angeles, Chicago, and Houston.
KeywordsYork City Central Place Weighted Distance Link Structure Volunteer Geographic Information
- Adams, B., & McKenzie, G. (2012). Frankenplace: An application for similarity-based place search. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media.Google Scholar
- Baskin, C. W., (1966). Central places in Southern Germany. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
- Berners-Lee, T., (2009). Linked data—design issues. Online: http://www.w3.org/DesignIssues/LinkedData.html.
- Christaller, W. (1933). Die zentralen Orte in Süddeutschland. Jena: Gustav Fischer.Google Scholar
- Gao, S., Li, L., Li, W., Janowicz, K., & Zhang, Y. (2014). Constructing gazetteers from volunteered big geo-data based on Hadoop. Computers, Environment and Urban Systems. doi: 10.1016/j.compenvurbsys.2014.02.004.
- Harris, S., Seaborne, A. (2013). SPARQL 1.1 query language. W3C Recommendation. Available from http://www.w3.org/TR/sparql11-query/.
- Hecht, B., & Moxley, E. (2009). Terabytes of tobler: Evaluating the first law in a massive, domain-neutral representation of world knowledge. In: K. Hornsby, C. Claramunt, M. Denis, & G. Ligozat (Eds.), Spatial information theory . Lecture Notes in Computer Science (Vol. 5756, pp. 88–105). Heidelberg: Springer. doi: 10.1007/978-3-642-03832-7_6.
- Hecht, B. J., & Gergle, D. (2010). On the localness of user-generated content. In Proceedings of The 2010 ACM Conference on Computer Supported Cooperative Work (pp. 229–232).Google Scholar
- Keßler, C., Maué, P., Heuer, J. T., & Bartoschek, T. (2009). Bottom-Up gazetteers: Learning from the implicit semantics of geotags. In: K. Janowicz , M. Raubal, S. Levashkin (Eds.), Third International Conference on Geospatial Semantics (GeoS 2009), Springer Lecture Notes in Computer Science 5892, pp. 83–102.Google Scholar
- Lieberman, M. D., & Lin, J. (2009). You are where you edit: Locating Wikipedia contributors through edit histories. In Proceedings of the Third International Conference on Weblogs and Social Media, (ICWSM 2009), San Jose, California, USA, 17–20 May 2009.Google Scholar
- Lösch, A. (1954). The Economics of Location. New Haven, CT: Yale University Press.Google Scholar
- Salvini, M. M. (2012). Spatialization von nutzergenerierten Inhalten für die explorative Analyse des globalen Städtenetzes. PhD thesis, University of Zurich.Google Scholar
- Takahashi, Y., Ohshima, H., Yamamoto, M., Iwasaki. H., Oyama, S., & Tanaka, K. (2011). Evaluating significance of historical entities based on tempo-spatial impacts analysis using wikipedia link structure. In Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, (pp 83–92).Google Scholar
- W3C Semantic Web Interest Group. (2004). Basic geo (WGS84 lat/long) vocabulary. Online: http://www.w3.org/2003/01/geo/.