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Validating activity, time, and space diversity as essential components of urban vitality

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  • Chaogui Kang
  • Dongwan Fan
  • Hongzan Jiao

Abstract

Urbanization’s rapid progress presents an urgent challenge for developing a predictive, quantitative theory of “the death and life of cities†(a.k.a. “the essential diversity conditions for the urban built environment†). Despite the importance of activity diversity (i.e. serving different primary functions), existing works ignored that time diversity (i.e. attracting people at different times of the day) and space diversity (i.e. attracting people from different districts) also play important roles in promoting urban life in large cities. With assistance of human mobility and crowdsourcing data, this article thoroughly validates whether activity, time, and space diversity are essential and inseparable components of urban vitality in the Wuhan, China context. To achieve the goal, point of interest (POI) data are utilized to quantitatively measure activity diversity, human mobility data are adopted for building quantitative metrics of time diversity and space diversity, and a detailed urban perception map is crowdsourced as ground truth data for establishing a regression model between urban diversity indicators and urban vitality. The resultant regression model succeeds to decouple the relationship between population concentration, activity diversity, time diversity, space diversity, and urban vitality. It confirms that activity diversity together with time diversity and space diversity has stronger association with urban vitality than any single diversity indicator. Our contributions are threefold: (a) we provided a comprehensive collection of metrics for measuring urban diversity, (b) we confirmed that activity, time, and space diversity are essential components of urban vitality, and (c) our methodology can be replicated at scale to understand urban vitality under various geographic, societal, and economic contexts due to easy accessibility of similar datasets.

Suggested Citation

  • Chaogui Kang & Dongwan Fan & Hongzan Jiao, 2021. "Validating activity, time, and space diversity as essential components of urban vitality," Environment and Planning B, , vol. 48(5), pages 1180-1197, June.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:5:p:1180-1197
    DOI: 10.1177/2399808320919771
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    References listed on IDEAS

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    1. Yu Liu & Chaogui Kang & Song Gao & Yu Xiao & Yuan Tian, 2012. "Understanding intra-urban trip patterns from taxi trajectory data," Journal of Geographical Systems, Springer, vol. 14(4), pages 463-483, October.
    2. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
    3. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    4. So-Hyun Park & Jun-Hyung Kim & Yee-Myung Choi & Han-Lim Seo, 2013. "Design elements to improve pleasantness, vitality, safety, and complexity of the pedestrian environment: evidence from a Korean neighbourhood walkability case study," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 17(1), pages 142-160, March.
    5. Philip Salesses & Katja Schechtner & César A Hidalgo, 2013. "The Collaborative Image of The City: Mapping the Inequality of Urban Perception," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
    6. Marquet, Oriol & Miralles-Guasch, Carme, 2015. "Neighbourhood vitality and physical activity among the elderly: The role of walkable environments on active ageing in Barcelona, Spain," Social Science & Medicine, Elsevier, vol. 135(C), pages 24-30.
    7. Bowes, David R. & Ihlanfeldt, Keith R., 2001. "Identifying the Impacts of Rail Transit Stations on Residential Property Values," Journal of Urban Economics, Elsevier, vol. 50(1), pages 1-25, July.
    8. Adelman, M A, 1969. "Comment on the "H" Concentration Measure as a Numbers-Equivalent," The Review of Economics and Statistics, MIT Press, vol. 51(1), pages 99-101, February.
    9. Chaogui Kang & Yu Liu & Xiujun Ma & Lun Wu, 2012. "Towards Estimating Urban Population Distributions from Mobile Call Data," Journal of Urban Technology, Taylor & Francis Journals, vol. 19(4), pages 3-21, October.
    10. Wang, Xiaokun (Cara) & Kockelman, Kara M. & Lemp, Jason D., 2012. "The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data," Journal of Transport Geography, Elsevier, vol. 24(C), pages 77-88.
    11. Ben Still & David Simmonds, 2000. "Parking restraint policy and urban vitality," Transport Reviews, Taylor & Francis Journals, vol. 20(3), pages 291-316, January.
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    2. Jiangang Shi & Wei Miao & Hongyun Si & Ting Liu, 2021. "Urban Vitality Evaluation and Spatial Correlation Research: A Case Study from Shanghai, China," Land, MDPI, vol. 10(11), pages 1-15, November.
    3. Nuria Vidal Domper & Gonzalo Hoyos-Bucheli & Marta Benages Albert, 2023. "Jane Jacobs’s Criteria for Urban Vitality: A Geospatial Analysis of Morphological Conditions in Quito, Ecuador," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    4. Haize Pan & Chuan Yang & Lirong Quan & Longhui Liao, 2021. "A New Insight into Understanding Urban Vitality: A Case Study in the Chengdu-Chongqing Area Twin-City Economic Circle, China," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    5. Jing Huang & Xiao Hu & Jieqiong Wang & Andong Lu, 2023. "How Diversity and Accessibility Affect Street Vitality in Historic Districts?," Land, MDPI, vol. 12(1), pages 1-23, January.
    6. Qimeng Ren & Ming Sun, 2023. "Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao," IJERPH, MDPI, vol. 20(4), pages 1-24, February.
    7. Cefang Deng & Dailin Zhou & Yiming Wang & Jie Wu & Zhe Yin, 2024. "Association between Land Use and Urban Vitality in the Guangdong–Hong Kong–Macao Greater Bay Area: A Multiscale Study," Land, MDPI, vol. 13(10), pages 1-17, September.
    8. Ruoshi Zhang, 2023. "Evaluation of Emotional Attachment Characteristics of Small-Scale Urban Vitality Space Based on Technique for Order Preference by Similarity to Ideal Solution, Integrating Entropy Weight Method and Gr," Land, MDPI, vol. 12(3), pages 1-26, March.
    9. Xin Li & Yongsheng Qian & Junwei Zeng & Xuting Wei & Xiaoping Guang, 2021. "The Influence of Strip-City Street Network Structure on Spatial Vitality: Case Studies in Lanzhou, China," Land, MDPI, vol. 10(11), pages 1-17, October.

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