An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance

Authors Yingjie Hu, Krzysztof Janowicz

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Yingjie Hu
  • GSDA Lab, Department of Geography, University of Tennessee, Knoxville, USA
Krzysztof Janowicz
  • STKO Lab, Department of Geography, University of California, Santa Barbara, USA

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Yingjie Hu and Krzysztof Janowicz. An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


While Points Of Interest (POIs), such as restaurants, hotels, and barber shops, are part of urban areas irrespective of their specific locations, the names of these POIs often reveal valuable information related to local culture, landmarks, influential families, figures, events, and so on. Place names have long been studied by geographers, e.g., to understand their origins and relations to family names. However, there is a lack of large-scale empirical studies that examine the localness of place names and their changes with geographic distance. In addition to enhancing our understanding of the coherence of geographic regions, such empirical studies are also significant for geographic information retrieval where they can inform computational models and improve the accuracy of place name disambiguation. In this work, we conduct an empirical study based on 112,071 POIs in seven US metropolitan areas extracted from an open Yelp dataset. We propose to adopt term frequency and inverse document frequency in geographic contexts to identify local terms used in POI names and to analyze their usages across different POI types. Our results show an uneven usage of local terms across POI types, which is highly consistent among different geographic regions. We also examine the decaying effect of POI name similarity with the increase of distance among POIs. While our analysis focuses on urban POI names, the presented methods can be generalized to other place types as well, such as mountain peaks and streets.

Subject Classification

ACM Subject Classification
  • Information systems → Language models
  • Place names
  • points of interest
  • geographic information retrieval
  • semantic similarity
  • geospatial semantics


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