An empirical study on the names of points of interest and their changes with geographic distance

TL;DR

This study analyzes how POI names reflect local culture and how their similarity decreases with geographic distance, using large-scale data from US cities to inform geographic information retrieval models.

Contribution

It introduces a large-scale empirical analysis of POI name localness and their variation with distance, utilizing term frequency and inverse document frequency in geographic contexts.

Findings

Uneven usage of local terms across POI types

High consistency of local term usage among regions

POI name similarity decays with increasing geographic distance

Abstract

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.