Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China

Studying the spatiotemporal pattern of urban leisure activities helps us to understand the development and utilization of urban public space, people’s quality of life, and the happiness index. It has outstanding value for improving rational resource allocation, stimulating urban vitality, and promot...

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Autores principales:	Shaojun Liu, Yao Long, Ling Zhang, Hao Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	big data spatiotemporal pattern urban leisure mobile sensing human activity Agriculture S
Acceso en línea:	https://doaj.org/article/8a4457f776914445b92bb68804916f43
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id	oai:doaj.org-article:8a4457f776914445b92bb68804916f43
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spelling	oai:doaj.org-article:8a4457f776914445b92bb68804916f432021-11-25T18:09:43ZQuantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China10.3390/land101112142073-445Xhttps://doaj.org/article/8a4457f776914445b92bb68804916f432021-11-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1214https://doaj.org/toc/2073-445XStudying the spatiotemporal pattern of urban leisure activities helps us to understand the development and utilization of urban public space, people’s quality of life, and the happiness index. It has outstanding value for improving rational resource allocation, stimulating urban vitality, and promoting sustainable urban development. This study aims at discovering the spatiotemporal distribution patterns and people’s behavioral preferences of urban leisure activities using quantitative technology merging ubiquitous sensing big data. On the basis of modeling individual activity traces using mobile signaling data (MSD), we developed a space-time constrained dasymetric interpolation method to refine the urban leisure activity spatiotemporal distribution. We conducted an empirical study in Nanjing, China. The results indicate that leisure plays an essential role in daily human life, both on weekdays and weekends. Significant differences exist in spatiotemporal and type selection in urban leisure. The weekend afternoon is the breakout period of leisure, and entertainment is the most popular leisure activity. Furthermore, the correlation between leisure resource allocation and leisure activity participation was argued. Our findings confirm that data-driven approaches would be a promising method for analyzing human behavior patterns; therefore, assisting in land planning decisions and promoting social justice and sustainability.Shaojun LiuYao LongLing ZhangHao LiuMDPI AGarticlebig dataspatiotemporal patternurban leisuremobile sensinghuman activityAgricultureSENLand, Vol 10, Iss 1214, p 1214 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	big data spatiotemporal pattern urban leisure mobile sensing human activity Agriculture S
spellingShingle	big data spatiotemporal pattern urban leisure mobile sensing human activity Agriculture S Shaojun Liu Yao Long Ling Zhang Hao Liu Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China
description	Studying the spatiotemporal pattern of urban leisure activities helps us to understand the development and utilization of urban public space, people’s quality of life, and the happiness index. It has outstanding value for improving rational resource allocation, stimulating urban vitality, and promoting sustainable urban development. This study aims at discovering the spatiotemporal distribution patterns and people’s behavioral preferences of urban leisure activities using quantitative technology merging ubiquitous sensing big data. On the basis of modeling individual activity traces using mobile signaling data (MSD), we developed a space-time constrained dasymetric interpolation method to refine the urban leisure activity spatiotemporal distribution. We conducted an empirical study in Nanjing, China. The results indicate that leisure plays an essential role in daily human life, both on weekdays and weekends. Significant differences exist in spatiotemporal and type selection in urban leisure. The weekend afternoon is the breakout period of leisure, and entertainment is the most popular leisure activity. Furthermore, the correlation between leisure resource allocation and leisure activity participation was argued. Our findings confirm that data-driven approaches would be a promising method for analyzing human behavior patterns; therefore, assisting in land planning decisions and promoting social justice and sustainability.
format	article
author	Shaojun Liu Yao Long Ling Zhang Hao Liu
author_facet	Shaojun Liu Yao Long Ling Zhang Hao Liu
author_sort	Shaojun Liu
title	Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China
title_short	Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China
title_full	Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China
title_fullStr	Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China
title_full_unstemmed	Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China
title_sort	quantifying and characterizing urban leisure activities by merging multiple sensing big data: a case study of nanjing, china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8a4457f776914445b92bb68804916f43
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Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China

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