Exploring the disparities in park access through mobile phone data: Evidence from Shanghai, China

Xiao, Yang; De, Wang; Jia, Fang

doi:10.1016/j.landurbplan.2018.09.013

Cited by 200 publications

(115 citation statements)

References 83 publications

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“…The main reason for this growth is the large number of migrants and tourists, which made up 39% of the total population of Shanghai in 2010. The recent master plan greatly emphasis on providing more facilities regarding development and administrations for the betterment of tourists and residents of Shanghai (Shanghai Master Plan (2016-2035) [47]. Shanghai is a world-famous tourist destination with many renowned attractions, such as Oriental Pearl, Lujiazui, Century Park, Yu Garden, Jing'an temple, Nanjing East Road, and the Bund [48] etc., mostly situated in the city center, while there are more than 800 parks in different parts of the city.…”

Section: Study Areamentioning

confidence: 99%

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Khan

Wan

2020

IJGI

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The aim of this study is to analyze and compare the patterns of behavior of tourists and residents from Location-Based Social Network (LBSN) data in Shanghai, China using various spatiotemporal analysis techniques at different venue categories. The paper presents the applications of location-based social network’s data by exploring the patterns in check-ins over a period of six months. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data is translated into the Geographical Information Systems (GIS) format, and compared with the help of temporal statistical analysis and kernel density estimation. The venue classification is done by using information regarding the nature of physical locations. The findings reveal that the spatial activities of tourists are more concentrated as compared to those of residents, particularly in downtown, while the residents also visited suburban areas and the temporal activities of tourists varied significantly while the residents’ activities showed relatively stable behavior. These results can be applied in destination management, urban planning, and smart city development.

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Section: Study Areamentioning

confidence: 99%

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Khan

Wan

2020

IJGI

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“…First, previous studies mainly focused on developed countries in North America or Europe. There are only a few empirical studies focusing on the issue of environmental justice in developing countries like China, but many of them paid much attention to coastal metropolitan cities like Beijing, Shanghai, and Shenzhen (21)(22)(23). The case study of inland cities helps to enrich the existing empirical findings (24).…”

Section: Data Descriptionmentioning

confidence: 99%

Characterizing Horizontal and Vertical Perspectives of Spatial Equity for Various Urban Green Spaces: A Case Study of Wuhan, China

Wang

2020

Front. Public Health

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“…The main reason for this growth is the large number of migrants, who made up 39% of the total population of Shanghai in 2010. The recent master plan greatly emphasizes providing more facilities and administration for the betterment of the residents of Shanghai (Shanghai Master Plan (2016-2035) [60]. As the economic city of China, Shanghai connects China to the global economy.…”

Section: Study Areamentioning

confidence: 99%

Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

Khan

Wan

2020

IJGI

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The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.

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Exploring the disparities in park access through mobile phone data: Evidence from Shanghai, China

Cited by 200 publications

References 83 publications

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Characterizing Horizontal and Vertical Perspectives of Spatial Equity for Various Urban Green Spaces: A Case Study of Wuhan, China

Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

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