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Open AccessArticle

Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai

1
School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
2
School of Information Engineering, Huangshan University, Huangshan 245041, China
3
Institute of Smart City, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 360; https://doi.org/10.3390/ijgi9060360
Received: 1 April 2020 / Revised: 20 May 2020 / Accepted: 30 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Geovisualization and Geo Visual Knowledge Discovery)
Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors. View Full-Text
Keywords: geospatial visualization; big data; kernel density estimation; green parks; geospatial knowledge; smart cities geospatial visualization; big data; kernel density estimation; green parks; geospatial knowledge; smart cities
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Liu, Q.; Ullah, H.; Wan, W.; Peng, Z.; Hou, L.; Qu, T.; Ali Haidery, S. Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai. ISPRS Int. J. Geo-Inf. 2020, 9, 360.

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