Abstract
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.
Highlights
Urban areas are generally marked by downgraded ecosystems, rising environmental damage, higher temperature, and a decreasing number of green parks
Data collected from social media and other “big data” reliable sources grow in size every year and can be utilized to research how the public interact with actual environments and to accurately assess individual preferences through time and space [11]
The findings revealed that women are much more likely to use social media throughout the weekdays compared to men in almost all districts
Summary
Urban areas are generally marked by downgraded ecosystems, rising environmental damage, higher temperature, and a decreasing number of green parks. Recent developments in social media analysis based on geographic information systems (GIS) provide an opportunity to examine spatial and temporal, and even affective dimensions of the behavior of users, including public spaces and visits to parks [14,15]. Some of these analyses still have drawbacks, because only a relatively small number of social media posts are analyzed manually. We investigated the gender difference among districts and days by applying the relative difference formula By examining these factors, we could analyze the use of urban green parks and its variance across a range of temporal scales. This method paves the way for socioecological research using crowdsourced and social network data instead of relying on the results of subjective coverage and observational data
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