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Open AccessArticle

A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information

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Department of Computational and Data Sciences, George Mason University, 4400 University Drive, MS 6B2, Fairfax, VA 22030, USA
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Department of Geography and Geoinformation Science, George Mason University, 4400 University Drive, MS 6C3, Fairfax, VA 22030, USA
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 385; https://doi.org/10.3390/ijgi9060385
Received: 7 May 2020 / Revised: 5 June 2020 / Accepted: 6 June 2020 / Published: 10 June 2020
(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)
The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network approach enhances previous research involving place clustering using geo-textual information, which often simplifies relationships between places to be either in-cluster or out-of-cluster. To demonstrate our approach, we use as a case study in Manhattan (New York) that compares networks constructed from three different geo-textural data sources—TripAdvisor attraction reviews, TripAdvisor restaurant reviews, and Twitter data. The results showcase how the thematic similarity network approach enables us to conduct clustering analysis as well as node-to-node and node-to-cluster analysis, which is fruitful for understanding how places are connected through individuals’ experiences. Furthermore, by enriching the networks with geodemographic information as node attributes, we discovered that some low-income communities in Manhattan have distinctive restaurant cultures. Even though geolocated tweets are not always related to place they are posted from, our case study demonstrates that topic modeling is an efficient method to filter out the place-irrelevant tweets and therefore refining how of places can be studied. View Full-Text
Keywords: geo-textual data; volunteered geographic information; crowdsourcing; similarity network analysis; topic modeling geo-textual data; volunteered geographic information; crowdsourcing; similarity network analysis; topic modeling
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Yuan, X.; Crooks, A.; Züfle, A. A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information. ISPRS Int. J. Geo-Inf. 2020, 9, 385.

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