Next Article in Journal
Testing Different Interpolation Methods Based on Single Beam Echosounder River Surveying. Case Study: Siret River
Previous Article in Journal
Spatiotemporal Distribution of Nonseismic Landslides during the Last 22 Years in Shaanxi Province, China
Open AccessArticle

Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data

1
School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
2
Institute of Smart City, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 506; https://doi.org/10.3390/ijgi8110506
Received: 29 August 2019 / Revised: 2 November 2019 / Accepted: 6 November 2019 / Published: 10 November 2019
(This article belongs to the Special Issue GIS-Based Analysis for Quality of Life and Environmental Monitoring)
Green parks are vital public spaces and play a major role in urban living and well-being. Research on the attractiveness of green parks often relies on traditional techniques, such as questionnaires and in-situ surveys, but these methods are typically insignificant in scale, time-consuming, and expensive, with less transferable results and only site-specific outcomes. This article presents an investigative study that uses location-based social network (LBSN) data to collect spatial and temporal patterns of park visits in Shanghai metropolitan city. During the period from July 2016 to June 2017 in Shanghai, China, we analyzed the spatiotemporal behavior of park visitors for 157 green parks and conducted empirical research on the impacts of green spaces on the public’s behavior in Shanghai. Our main findings show (i) the check-in distribution of users in different green spaces; (ii) the seasonal effects on the public’s behavior toward green spaces; (iii) changes in the number of users based on the hour of the day, the intervals of the day (morning, afternoon, evening), and the day of the week; (iv) interesting user behavior variations that depend on temperature effects; and (v) gender-based differences in the number of green park visitors. These results can be used for the purpose of urban city planning for green spaces by accounting for the preferences of visitors.
Keywords: urban green parks; big data; spatiotemporal; KDE; urban studies; environment; Weibo urban green parks; big data; spatiotemporal; KDE; urban studies; environment; Weibo
MDPI and ACS Style

Ullah, H.; Wan, W.; Haidery, S.A.; Khan, N.U.; Ebrahimpour, Z.; Luo, T. Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data. ISPRS Int. J. Geo-Inf. 2019, 8, 506.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop