Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China

Abstract

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.

Spatially varying impacts of the built environment on physical activity from a human-scale view: Using street view data

“…Fifth, contrary to the results of some studies that enterprises promote running activities (Yang et al, 2022b), in most areas, such as Shuhan Road and Balizhuang, enterprises harm running activities. It may be attributed to the job-housing imbalances among Chengdu residents.…”

“…With the development of information and communication technology, crowdsourcing data such as Strava data emerged, which provides technical support for measuring the spatial distribution of PA on large sample sizes (Yang et al, 2022b). Moreover, many researchers have found that streets play an important role in the promotion of physical activities such as running and cycling (Lu et al, 2018b;Yang et al, 2021a).…”

Intergenerational differences in the urban vibrancy of TOD: Impacts of the built environment on the activities of different age groups

“…Second, to address the adverse effects of “Transit-Induced Gentrification” on urban vibrancy, we call for efforts to develop and improve the multi-level housing support system (e.g., government-subsidized and affordable rental housing) in the MSA to achieve a systematic spatial match for the “housing-transportation” system. This will help reduce the “Spatial Deprivation” suffered by low-and middle-income residents due to high housing prices ( 71 ), thus contributing to the development of equitable, accessible, and vibrant TOD communities. Finally, to accommodate the daily activities of young adults, urban planners need to improve the quality of public space in the high employment density MSA by providing adequate leisure facilities like small community parks.…”

Optimization of low-carbon land use in Chengdu based on multi-objective linear programming and the future land use simulation model

“…This article takes Chengdu, the largest metropolis in western China and the capital of Sichuan province, as a case study. The city is in a period of fast industrialization and urbanization, and the land use pattern is changing substantially (Wei, X., Duan, L. 2021;Yang et al, 2022a;Yang et al, 2022b). As the first proposed park city and a low-carbon pilot city approved by the state, Chengdu is taking the lead in laying out low-carbon industries and carrying out carbon reduction actions in order to achieve the goal of carbon peak and carbon neutrality.…”