Article Identifying Urban Neighborhood Names through User-Contributed Online Property Listings Grant McKenzie 1,* ID , Zheng Liu 2, Yingjie Hu 3 ID and Myeong Lee 4 1 Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada 2 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;
[email protected] 3 Department of Geography, University at Buffalo, Buffalo, NY 14260, USA;
[email protected] 4 College of Information Studies, University of Maryland, College Park, MD 20742, USA;
[email protected] * Correspondence:
[email protected] Received: 24 August 2018; Accepted: 22 September 2018; Published: 26 September 2018 Abstract: 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.