Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data
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 us...
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doaj-3645d145630c4fde96480050a02ba7b72020-11-25T02:25:37ZengMDPI AGElectronics2079-92922020-06-0191028102810.3390/electronics9061028Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big DataQi Liu0Hidayat Ullah1Wanggen Wan2Zhangyou Peng3Li Hou4Sanam Shahla Rizvi5Saqib Ali Haidery6Tong Qu7A. A. M. Muzahid8School of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Information Engineering, Huangshan University, Huangshan 245041, ChinaRaptor Interactive (Pty) Ltd., Eco Boulevard, Witch Hazel Ave, Centurion 0157, South AfricaSchool of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaUrban 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.https://www.mdpi.com/2079-9292/9/6/1028spatiotemporal analysissmart cityenvironmentKDEgreen parksquality of life |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qi Liu Hidayat Ullah Wanggen Wan Zhangyou Peng Li Hou Sanam Shahla Rizvi Saqib Ali Haidery Tong Qu A. A. M. Muzahid |
spellingShingle |
Qi Liu Hidayat Ullah Wanggen Wan Zhangyou Peng Li Hou Sanam Shahla Rizvi Saqib Ali Haidery Tong Qu A. A. M. Muzahid Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data Electronics spatiotemporal analysis smart city environment KDE green parks quality of life |
author_facet |
Qi Liu Hidayat Ullah Wanggen Wan Zhangyou Peng Li Hou Sanam Shahla Rizvi Saqib Ali Haidery Tong Qu A. A. M. Muzahid |
author_sort |
Qi Liu |
title |
Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data |
title_short |
Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data |
title_full |
Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data |
title_fullStr |
Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data |
title_full_unstemmed |
Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data |
title_sort |
categorization of green spaces for a sustainable environment and smart city architecture by utilizing big data |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-06-01 |
description |
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. |
topic |
spatiotemporal analysis smart city environment KDE green parks quality of life |
url |
https://www.mdpi.com/2079-9292/9/6/1028 |
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