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

Abstract

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 themore » not-yet-recorded local place names discovered by our framework.« less

Authors:
 [1]; ORCiD logo [2];  [3]
  1. Univ. of Tennessee, Knoxville, TN (United States). Department of Geography
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Geographic Information Science and Technology Group
  3. Univ. of Maryland, College Park, MD (United States). Department of Geographical Sciences
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1435186
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Geographical Information Science
Additional Journal Information:
Journal Volume: 33; Journal Issue: 4; Journal ID: ISSN 1365-8816
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Local place name; gazetteer; natural language processing; named entity recognition; geospatial clustering; geospatial semantics

Citation Formats

Hu, Yingjie, Mao, Huina, and Mckenzie, Grant. A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements. United States: N. p., 2018. Web. doi:10.1080/13658816.2018.1458986.
Hu, Yingjie, Mao, Huina, & Mckenzie, Grant. A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements. United States. doi:10.1080/13658816.2018.1458986.
Hu, Yingjie, Mao, Huina, and Mckenzie, Grant. Fri . "A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements". United States. doi:10.1080/13658816.2018.1458986. https://www.osti.gov/servlets/purl/1435186.
@article{osti_1435186,
title = {A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements},
author = {Hu, Yingjie and Mao, Huina and Mckenzie, Grant},
abstractNote = {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.},
doi = {10.1080/13658816.2018.1458986},
journal = {International Journal of Geographical Information Science},
number = 4,
volume = 33,
place = {United States},
year = {2018},
month = {4}
}

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