Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China
The lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cy...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.883177/full |
| _version_ | 1811302890261708800 |
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| author | Linchuan Yang Bingjie Yu Pengpeng Liang Xianglong Tang Ji Li |
| author_facet | Linchuan Yang Bingjie Yu Pengpeng Liang Xianglong Tang Ji Li |
| author_sort | Linchuan Yang |
| collection | DOAJ |
| description | The lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cycling and running tracks in central Chengdu in December 2021 (during the COVID-19 pandemic) and develops spatial regression models for numerous 500 × 500 m grids (N = 2,788) to assess the impacts of the built environment on the cycling and running intensity indices. The findings are summarized as follows. First, land-use mix has insignificant effects on the physical activity of residents, which largely contrasts with the evidence gathered from previous studies. Second, road density, water area, green space area, number of stadiums, and number of enterprises significantly facilitate cycling and running. Third, river line length and the light index have positive associations with running but not with cycling. Fourth, housing price is positively correlated with cycling and running. Fifth, schools seem to discourage these two types of physical activities during the COVID-19 pandemic. This study provides practical implications (e.g., green space planning and public space management) for urban planners, practitioners, and policymakers. |
| first_indexed | 2024-04-13T07:37:31Z |
| format | Article |
| id | doaj.art-f83434c454224cd9a19a2d6859f0de64 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-13T07:37:31Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-f83434c454224cd9a19a2d6859f0de642022-12-22T02:56:05ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-04-011010.3389/fpubh.2022.883177883177Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, ChinaLinchuan YangBingjie YuPengpeng LiangXianglong TangJi LiThe lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cycling and running tracks in central Chengdu in December 2021 (during the COVID-19 pandemic) and develops spatial regression models for numerous 500 × 500 m grids (N = 2,788) to assess the impacts of the built environment on the cycling and running intensity indices. The findings are summarized as follows. First, land-use mix has insignificant effects on the physical activity of residents, which largely contrasts with the evidence gathered from previous studies. Second, road density, water area, green space area, number of stadiums, and number of enterprises significantly facilitate cycling and running. Third, river line length and the light index have positive associations with running but not with cycling. Fourth, housing price is positively correlated with cycling and running. Fifth, schools seem to discourage these two types of physical activities during the COVID-19 pandemic. This study provides practical implications (e.g., green space planning and public space management) for urban planners, practitioners, and policymakers.https://www.frontiersin.org/articles/10.3389/fpubh.2022.883177/fullphysical environmentcyclingrunningspatial inequalityStravahealth |
| spellingShingle | Linchuan Yang Bingjie Yu Pengpeng Liang Xianglong Tang Ji Li Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China Frontiers in Public Health physical environment cycling running spatial inequality Strava health |
| title | Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China |
| title_full | Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China |
| title_fullStr | Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China |
| title_full_unstemmed | Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China |
| title_short | Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China |
| title_sort | crowdsourced data for physical activity built environment research applying strava data in chengdu china |
| topic | physical environment cycling running spatial inequality Strava health |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.883177/full |
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