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Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai

Author

Listed:
  • Jiatian Bu

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

  • Jie Yin

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

  • Yifan Yu

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

  • Ye Zhan

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

Abstract

The characteristics of the built environment and the configuration of public facilities can affect the health and well-being of older adults. Recognizing the range of daily activities and understanding the utilization of public facilities among older adults has become essential in planning age-friendly communities. However, traditional methods are unable to provide large-scale objective measures of older adults’ travel behaviors. To address this issue, we used the smartphone-based global positioning system (GPS) trajectory to explore the activity spaces of 76 older adults in a high-density urban community in Shanghai for 102 consecutive days. We found that activity spaces are centered around older adults’ living communities, with 46.3% within a 1.5 km distance. The older adults’ daily activities are within a 15 min walking distance, and accessibility is the most important factor when making a travel choice to parks and public facilities. We also found that the travel range and spatial distribution of points of interest are different between age and gender groups. In addition, we found that using a concave hull with Alpha shape algorithm is more applicable and robust than the traditional convex hull algorithm. This is a unique case study in a high-density urban area with objective measures for assessing the activity spaces of older adults, thus providing empirical evidence for promoting healthy aging in cities.

Suggested Citation

  • Jiatian Bu & Jie Yin & Yifan Yu & Ye Zhan, 2021. "Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5003-:d:546223
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    References listed on IDEAS

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    1. Zachary Patterson & Steven Farber, 2015. "Potential Path Areas and Activity Spaces in Application: A Review," Transport Reviews, Taylor & Francis Journals, vol. 35(6), pages 679-700, November.
    2. Calastri, Chiara & Hess, Stephane & Daly, Andrew & Carrasco, Juan Antonio, 2017. "Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 1-20.
    3. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    4. Wenzhi Liu & Huapu Lu & Zhiyuan Sun & Jing Liu, 2017. "Elderly’s Travel Patterns and Trends: The Empirical Analysis of Beijing," Sustainability, MDPI, vol. 9(6), pages 1-11, June.
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    Cited by:

    1. Minhui Lin & Xinyun Lin, 2023. "A Qualitative Study on Leisure Benefits, Constraints, and Negotiations in Urban Parks Based on Perception of Chinese Older Adults," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    2. Jie Chang & Qiuju Deng & Piaopiao Hu & Zhao Yang & Moning Guo & Feng Lu & Yuwei Su & Jiayi Sun & Yue Qi & Ying Long & Jing Liu, 2023. "Driving Time to the Nearest Percutaneous Coronary Intervention-Capable Hospital and the Risk of Case Fatality in Patients with Acute Myocardial Infarction in Beijing," IJERPH, MDPI, vol. 20(4), pages 1-12, February.
    3. Hongxu Guo & Zhuoqiao Luo & Mengtian Li & Shumin Kong & Haiyan Jiang, 2022. "A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension," Land, MDPI, vol. 11(6), pages 1-17, June.

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