Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data

Qi Liu,Sanam Shahla Rizvi,Hidayat Ullah,Saqib Ali Haidery,Zhangyou Peng,Tong Qu,Li Hou,Wanggen Wan,A A M Muzahid

doi:10.3390/electronics9061028

Qi Liu, Sanam Shahla Rizvi + Show 7 more

Open Access

https://doi.org/10.3390/electronics9061028

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Abstract

Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture.

Highlights

Urban green spaces, parks in particular, offer numerous advantages for people’s well-being, such as enhancing their psychological and social well-being, as well as their physical health [1,2,3].Visits to green parks provide the opportunity to directly experience the extra benefits of “natural”environments, especially for people with limited contact with nature
Data collected from social media, and other “big data” as reliable sources, grow in size every year, and can be utilized to investigate how the public interact with actual environments, and to accurately assess individual preferences through time and space [7]
122 of these green parks were chosen after processing the data collected from Weibo

Summary

Introduction

Parks in particular, offer numerous advantages for people’s well-being, such as enhancing their psychological and social well-being, as well as their physical health [1,2,3].Visits to green parks provide the opportunity to directly experience the extra benefits of “natural”environments, especially for people with limited contact with nature. Electronics 2020, 9, 1028 the number of visitors [4,5] These methods of organized observation usually pick a representative sample of urban green spaces and collect information regarding the use of green parks, for example, visitation numbers and features, as well as the activities and behavior of green park visitors. These approaches are typically time-consuming and site-specific, and have restricted spatial coverage [6]. The advent of free social media data offers new and different methods to analyze urban green park visits. Data collected from social media, and other “big data” as reliable sources, grow in size every year, and can be utilized to investigate how the public interact with actual environments, and to accurately assess individual preferences through time and space [7]

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