A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data

In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework i...

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Autores principales:	Zhicheng Shi, Xintao Liu, Jianhui Lai, Chengzhuo Tong, Anshu Zhang, Wenzhong Shi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	the elderly spatial distribution pattern connectivity smart card data data-driven method Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/b965665925124c93a6fc361776c61a2f
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id	oai:doaj.org-article:b965665925124c93a6fc361776c61a2f
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spelling	oai:doaj.org-article:b965665925124c93a6fc361776c61a2f2021-11-25T17:52:46ZA Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data10.3390/ijgi101107282220-9964https://doaj.org/article/b965665925124c93a6fc361776c61a2f2021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/728https://doaj.org/toc/2220-9964In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population.Zhicheng ShiXintao LiuJianhui LaiChengzhuo TongAnshu ZhangWenzhong ShiMDPI AGarticlethe elderlyspatial distribution patternconnectivitysmart card datadata-driven methodGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 728, p 728 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	the elderly spatial distribution pattern connectivity smart card data data-driven method Geography (General) G1-922
spellingShingle	the elderly spatial distribution pattern connectivity smart card data data-driven method Geography (General) G1-922 Zhicheng Shi Xintao Liu Jianhui Lai Chengzhuo Tong Anshu Zhang Wenzhong Shi A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data
description	In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population.
format	article
author	Zhicheng Shi Xintao Liu Jianhui Lai Chengzhuo Tong Anshu Zhang Wenzhong Shi
author_facet	Zhicheng Shi Xintao Liu Jianhui Lai Chengzhuo Tong Anshu Zhang Wenzhong Shi
author_sort	Zhicheng Shi
title	A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data
title_short	A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data
title_full	A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data
title_fullStr	A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data
title_full_unstemmed	A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data
title_sort	data-driven framework for analyzing spatial distribution of the elderly cardholders by using smart card data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b965665925124c93a6fc361776c61a2f
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A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data

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