A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

TL;DR

This paper introduces a framework combining NLP and geospatial clustering to extract and identify local place names from geotagged housing ads, aiding urban planning and disaster response.

Contribution

It presents a novel two-stage framework that leverages textual and spatial data to discover unrecorded local place names from online housing advertisements.

Findings

Successfully extracted local place names not in existing gazetteers.

Outperformed six baseline methods in accuracy.

Discovered new place names relevant for urban applications.

Abstract

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g., the entire world), recent establishment (e.g., the name of a newly-opened shopping center), or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements, and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates, and performs multi-scale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.