Spatial analysis of users-generated ratings of yelp venues

Abstract

Background: With popular location-based services on smart phones, users are willing to leave comments on the business venues (e.g., restaurants, shops, bars, etc.) that they visited. Reviews of users on Yelp venues somewhat indicate satisfaction of customers with services of those venues. Those reviews could be used to reflect service quality of business venues. Geo-localized venues could tell researchers where and how good a business venue is. Methods: In terms of a spatial analysis of venues' ratings, this paper explored geographic patterns of ratings of Yelp business venues in a city-wide region. Specifically, we identified clusters of high and low ratings and explored spatial patterns of clusters of high ratings for different venue categories (i.e., restaurants, fast foods and bars). Results: In this study, we undertook an analysis of Yelp ratings in Phoenix, USA. The empirical results indicate that spatial clusters of high ratings tend to be differently distributed between different categories of Yelp venues. More specifically, bars within or near the city centre are likely to get high ratings. Moreover, although hot spots and cold spots of restaurants and fast foods both tend to be randomly distributed over space, spatial distribution of restaurants' ratings tends to be more similar to that of bars' ratings. Conclusion: Mapping Yelp's business venues with ratings provides a new way to understand spatial patterns of service quality of business or public venues at a large spatial scale.

Measuring sustainable tourism with online platform data

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Big data and sustainable cities: applications of new and emerging forms of geospatial data in urban studies

“…Yelp has a crowdsourced rating system, which facilitates the reviews of users on business owners. Sun et al [9] map restaurants, fast foods and bars with ratings across a city, and afterwards identify clusters of high and low ratings in the city. On the other hand, inhabitants can express their real-time emotions that are associated with perceptions of the built environments or activities undertaken (work, rest, dine, etc.).…”

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