Mapping the popularity of urban restaurants using social media data

Zhai, Shixiao; Xu, Xiaolin; Yang, Lanrong; Zhou, Min; Zhang, Lu; Qiu, Bingkui

doi:10.1016/j.apgeog.2015.06.006

Cited by 45 publications

(23 citation statements)

References 42 publications

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“…Research using geotagged social media data and identifying spatial patterns of social media users is widespread [ 35 – 37 ]. Within disaster management, examples of spatial social media content are found in earthquake, wildfire, tropical cyclone, or flood events [ 20 , 38 – 40 ].…”

Section: Social Media Usage In Disastersmentioning

confidence: 99%

Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew

2017

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Hurricane Matthew was the deadliest Atlantic storm since Katrina in 2005 and prompted one of the largest recent hurricane evacuations along the Southeastern coast of the United States. The storm and its projected landfall triggered a massive social media reaction. Using Twitter data, this paper examines the spatiotemporal variability in social media response and develops a novel approach to leverage geotagged tweets to assess the evacuation responses of residents. The approach involves the retrieval of tweets from the Twitter Stream, the creation and filtering of different datasets, and the statistical and spatial processing and treatment to extract, plot and map the results. As expected, peak Twitter response was reached during the pre-impact and preparedness phase, and decreased abruptly after the passage of the storm. A comparison between two time periods—pre-evacuation (October 2th-4th) and post-evacuation (October 7th-9th)—indicates that 54% of Twitter users moved away from the coast to a safer location, with observed differences by state on the timing of the evacuation. A specific sub-state analysis of South Carolina illustrated overall compliance with evacuation orders and detailed information on the timing of departure from the coast as well as the destination location. These findings advance the use of big data and citizen-as-sensor approaches for public safety issues, providing an effective and near real-time alternative for measuring compliance with evacuation orders.

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Section: Social Media Usage In Disastersmentioning

confidence: 99%

Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew

2017

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“…Thus, posts on Twitter can contain a spatiotemporal signal along with the semantic information layer (Steiger, Albuquerque, & Zipf, ). Foursquare, Sina Weibo, and Dianping represent another category of location‐based social media platforms, which allow users to check in their spatial presence by selecting POIs provided by the social media (Jendryke, Balz, McClure, & Liao, ; Noulas, Scellato, Lambiotte, Pontil, & Mascolo, ; Zhai et al, ). Such check‐in records, although confined to predefined POI locations, also contain spatiotemporal information to infer user activities.…”

Section: Resultsmentioning

confidence: 99%

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Liao

Brown

Ding

et al. 2018

Transactions in GIS

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Over the past decade, there has been an unprecedented boom in the application of big data derived from location‐aware technology in GIScience. However, reflections on data quality lead to concerns about inaccurate or even erroneous spatial analysis results. This article contributes a meta‐analysis of big data‐enabled social sensing in spatial analysis. Big data‐enabled research has substantially enhanced our understanding of human population distribution and dynamics, land use, travel behavior, movement patterns, and spatial social networks from city to country to global levels. However, the effectiveness of big data‐enabled social sensing can be undermined by five critical issues related to research design: data volume, spatial coverage, locational information quality, and representativeness across socioeconomic and demographic groups. We suggest a framework for addressing the inherent bias in spatial big data at multiple stages of the research lifecycle and applying them to appropriate topics and scales of applications.

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“…Dianping.com scores each store in a range from 0 to 10 points: the higher the score, the greater the user's recommendation. The user rating data from Dianping.com is often used to recommend shops to users (Fang, Xu, Shamim Hossain, & Muhammad, ), and to evaluate the popularity of shops in a certain area (Zhai et al, ). Online reviews have been proven to show great potential in commercial site recommendation in the new retail era (Xiang, Du, Ma, & Fan, ; Zheng, Du, Ma, & Fan, ).…”

Section: Study Area and Datamentioning

confidence: 99%

Fine‐scale intra‐ and inter‐city commercial store site recommendations using knowledge transfer

Yao

Liu

Hong

et al. 2019

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The rapid development of urban retail companies brings new opportunities to the Chinese economy. Due to the spatiotemporal heterogeneity of different cities, selecting a business location in a new area has become a challenge. The application of multi‐source geospatial data makes it possible to describe human activities and urban functional zones at fine scale. We propose a knowledge transfer‐based model named KTSR to support citywide business location selections at the land‐parcel scale. This framework can optimize customer scores and study the pattern of business location selection for chain brands. First, we extract the features of each urban land parcel and study the similarities between them. Then, singular value decomposition was used to build a knowledge‐transfer model of similar urban land parcels between different cities. The results show that: (1) compared with the actual scores, the estimated deviation of the proposed model decreased by more than 50%, and the Pearson correlation coefficient reached 0.84 or higher; (2) the decomposed features were good at quantifying and describing high‐level commercial operation information, which has a strong relationship with urban functional structures. In general, our method can work for selecting business locations and estimating sale volumes and user evaluations.

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Mapping the popularity of urban restaurants using social media data

Cited by 45 publications

References 42 publications

Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew

Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Fine‐scale intra‐ and inter‐city commercial store site recommendations using knowledge transfer

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