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

Hu, Yingjie; Mao, Huina; Mckenzie, Grant

doi:10.1080/13658816.2018.1458986

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

Journal Article · 13 April 2018 · International Journal of Geographical Information Science

DOI: https://doi.org/10.1080/13658816.2018.1458986 · OSTI ID:1435186

Hu, Yingjie ^[1];

^[2]; Mckenzie, Grant ^[3]

Univ. of Tennessee, Knoxville, TN (United States). Department of Geography
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Geographic Information Science and Technology Group
Univ. of Maryland, College Park, MD (United States). Department of Geographical Sciences

We report that 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 multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. Finally, we also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1435186

Journal Information:: International Journal of Geographical Information Science, Vol. 33, Issue 4; ISSN 1365-8816

Country of Publication:: United States

Language:: English

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Cited by: 35 works

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

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