Identifying Urban Neighborhood Names through User-Contributed Online Property Listings

Neighborhoods are vaguely defined, localized regions that share similar characteristics. They are most often defined, delineated and named by the citizens that inhabit them rather than municipal government or commercial agencies. The names of these neighborhoods play an important role as a basis for...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2018-10, Vol.7 (10), p.388
Main Authors: McKenzie, Grant, Liu, Zheng, Hu, Yingjie, Lee, Myeong
Format: Article
Language:English
Subjects:
Boundaries
Communication
craigslist
Data
Geography
Housing
Names
Natural language
neighborhood
neighborhood name
Neighborhoods
random forest
Rent
Social networks
spatial statistics
Statistical methods
Urban areas
Urban environments
User generated content
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Summary:Neighborhoods are vaguely defined, localized regions that share similar characteristics. They are most often defined, delineated and named by the citizens that inhabit them rather than municipal government or commercial agencies. The names of these neighborhoods play an important role as a basis for community and sociodemographic identity, geographic communication and historical context. In this work, we take a data-driven approach to identifying neighborhood names based on the geospatial properties of user-contributed rental listings. Through a random forest ensemble learning model applied to a set of spatial statistics for all n-grams in listing descriptions, we show that neighborhood names can be uniquely identified within urban settings. We train a model based on data from Washington, DC, and test it on listings in Seattle, WA, and Montréal, QC. The results indicate that a model trained on housing data from one city can successfully identify neighborhood names in another. In addition, our approach identifies less common neighborhood names and suggestions of alternative or potentially new names in each city. These findings represent a first step in the process of urban neighborhood identification and delineation.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi7100388