IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v10y2021i9p984-d638080.html
   My bibliography  Save this article

Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example

Author

Listed:
  • Yunzi Yang

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Yuanyuan Ma

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Hongzan Jiao

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
    Engineering Research Center of Human Settlements and Environment of Hubei Province, Wuhan 430072, China)

Abstract

Block is the basic unit for studying the urban activities of residents, and block vitality is the concrete expression of urban dynamics at the block level. The quality of the block’s residential environment is a crucial medium to satisfy the residents’ pursuit of high-quality life; good block quality is essential for fostering the block vitality and further enhancing the overall vitality of the city. This study used the distribution density of cellular signaling data to quantify block vitality and constructed a block environment index system covering four dimensions—block accessibility, block function, block development degree, and human perception of block environment—innovatively introducing the elements of block environment from the human perspective. Considering the variability of block vitality between workdays and weekends, and between downtown and suburban blocks, this study used a geographically weighted regression model to show the mechanism of the spatial and temporal influence of indicators on block vitality, as well as to suggest how to enhance block vitality at different times of the day based on the influence mechanism. This study was conducted in Wuhan, China. The findings suggest that block vitality exhibited significant spatial and temporal heterogeneity. A high-vitality block can be created by enhancing the block’s accessibility, increasing the degree of block construction, and enriching the functional density and mix of functions in the block. A pleasantly green environment with a moderate degree of openness exerted a significant impact on promoting human activity and enhancing block vitality. The creation of high-vitality blocks should also consider the differences in the impact of various elements on block vitality between weekend and workday. For example, amid the surge in travel demand for education venues on weekends, enhancing the accessibility of blocks can significantly increase the vitality of blocks on weekends. We can truly realize the people-oriented approach to build a livable and high-vitality city by adapting to local conditions and time.

Suggested Citation

  • Yunzi Yang & Yuanyuan Ma & Hongzan Jiao, 2021. "Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example," Land, MDPI, vol. 10(9), pages 1-23, September.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:9:p:984-:d:638080
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/10/9/984/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/10/9/984/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    2. Kopczewska, Katarzyna & Ćwiakowski, Piotr, 2021. "Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants," Land Use Policy, Elsevier, vol. 103(C).
    3. Jiwei Li & Qingqing Ye & Xuankai Deng & Yaolin Liu & Yanfang Liu, 2016. "Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
    4. Chris Jacobs-Crisioni & Piet Rietveld & Eric Koomen & Emmanouil Tranos, 2014. "Evaluating the Impact of Land-Use Density and Mix on Spatiotemporal Urban Activity Patterns: An Exploratory Study Using Mobile Phone Data," Environment and Planning A, , vol. 46(11), pages 2769-2785, November.
    5. Radosław Klimek, 2019. "Towards Recognising Individual Behaviours from Pervasive Mobile Datasets in Urban Spaces," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    6. Seema Dave, 2011. "Neighbourhood density and social sustainability in cities of developing countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 19(3), pages 189-205, May/June.
    7. Mei Liu & Ying Jiang & Junliang He, 2021. "Quantitative Evaluation on Street Vitality: A Case Study of Zhoujiadu Community in Shanghai," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    8. Gao, Jie & Kamphuis, Carlijn B.M. & Helbich, Marco & Ettema, Dick, 2020. "What is ‘neighborhood walkability’? How the built environment differently correlates with walking for different purposes and with walking on weekdays and weekends," Journal of Transport Geography, Elsevier, vol. 88(C).
    9. Jane M Jacobs & Susan J Smith, 2008. "Living Room: Rematerialising Home," Environment and Planning A, , vol. 40(3), pages 515-519, March.
    10. Zhaoya Gong & Qiwei Ma & Changcheng Kan & Qianyun Qi, 2019. "Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
    11. Feng Hu & Wei Liu & Junyu Lu & Chengpeng Song & Yuan Meng & Jun Wang & Hanfa Xing, 2020. "Urban Function as a New Perspective for Adaptive Street Quality Assessment," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    12. 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.
    13. Yanyan Chen & Zheng Zhang & Tianwen Liang, 2019. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He Liu & Xueming Li, 2022. "Understanding the Driving Factors for Urban Human Settlement Vitality at Street Level: A Case Study of Dalian, China," Land, MDPI, vol. 11(5), pages 1-20, April.
    2. 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.
    3. Yan Long & Siyu Hu & Zhengyuan Lu & Lianghao Cheng & Cairui Zhen & Jingmei Shao & Yuqiao Zheng & Xuehui Zhou & Jin Li & Yudi Lin & Shu Zhang & Yue Wang & Tianyue Luo & Haijuan Zhao & Xuejun Liu, 2024. "A Study on the Influence Mechanisms of Neighborhood Vitality and the Characteristics of Spatial and Temporal Differentiation in the Urban Fringe Areas of Wuhan City," Land, MDPI, vol. 13(11), pages 1-36, October.
    4. Yiming Liu & Xiangxiang Pan & Qing Liu & Guicai Li, 2023. "Establishing a Reliable Assessment of the Green View Index Based on Image Classification Techniques, Estimation, and a Hypothesis Testing Route," Land, MDPI, vol. 12(5), pages 1-14, May.
    5. 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.
    6. Jing Zhao & Wanyue Suo, 2024. "Research on the Construction and Application of a SVM-Based Quantification Model for Streetscape Visual Complexity," Land, MDPI, vol. 13(11), pages 1-27, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuanyuan Ma & Hongzan Jiao, 2023. "Quantitative Evaluation of Friendliness in Streets’ Pedestrian Networks Based on Complete Streets: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 15(13), pages 1-28, June.
    2. Junyue Yang & Xiaomei Li & Jia Du & Canhui Cheng, 2023. "Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    3. Tongwen Wang & Ya Li & Haidong Li & Shuaijun Chen & Hongkai Li & Yunxing Zhang, 2022. "Research on the Vitality Evaluation of Parks and Squares in Medium-Sized Chinese Cities from the Perspective of Urban Functional Areas," IJERPH, MDPI, vol. 19(22), pages 1-23, November.
    4. Fernando Fonseca & Elisa Conticelli & George Papageorgiou & Paulo Ribeiro & Mona Jabbari & Simona Tondelli & Rui Ramos, 2021. "Levels and Characteristics of Utilitarian Walking in the Central Areas of the Cities of Bologna and Porto," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    5. Ruoxin Zhu & Diao Lin & Yujing Wang & Michael Jendryke & Rui Xin & Jian Yang & Jianzhong Guo & Liqiu Meng, 2020. "Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    6. Lixin Liu & Yanjun Dong & Wei Lang & Huiyu Yang & Bin Wang, 2024. "The Impact of Commercial-Industry Development of Urban Vitality: A Study on the Central Urban Area of Guangzhou Using Multisource Data," Land, MDPI, vol. 13(2), pages 1-19, February.
    7. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    8. Liao, Cong & Scheuer, Bronte, 2022. "Evaluating the performance of transit-oriented development in Beijing metro station areas: Integrating morphology and demand into the node-place model," Journal of Transport Geography, Elsevier, vol. 100(C).
    9. Yating Fan & Da Kuang & Wei Tu & Yu Ye, 2023. "Which Spatial Elements Influence Waterfront Space Vitality the Most?—A Comparative Tracking Study of the Maozhou River Renewal Project in Shenzhen, China," Land, MDPI, vol. 12(6), pages 1-18, June.
    10. Mehzabin Tuli, Farzana & Mitra, Suman & Crews, Mariah B., 2021. "Factors influencing the usage of shared E-scooters in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 164-185.
    11. John Stanley & Janet Stanley, 2023. "Improving Appraisal Methodology for Land Use Transport Measures to Reduce Risk of Social Exclusion," Sustainability, MDPI, vol. 15(15), pages 1-18, August.
    12. Marie Geraldine Herrmann-Lunecke & Cristhian Figueroa-Martínez & Francisca Parra Huerta & Rodrigo Mora, 2022. "The Disabling City: Older Persons Walking in Central Neighbourhoods of Santiago de Chile," Sustainability, MDPI, vol. 14(17), pages 1-19, September.
    13. Li, Jingjing & Kim, Changjoo & Sang, Sunhee, 2018. "Exploring impacts of land use characteristics in residential neighborhood and activity space on non-work travel behaviors," Journal of Transport Geography, Elsevier, vol. 70(C), pages 141-147.
    14. Ding, Chuan & Wang, Donggen & Liu, Chao & Zhang, Yi & Yang, Jiawen, 2017. "Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 65-80.
    15. Van Acker, Veronique & Ho, Loan & Stevens, Larissa & Mulley, Corinne, 2020. "Quantifying the effects of childhood and previous residential experiences on the use of public transport," Journal of Transport Geography, Elsevier, vol. 86(C).
    16. Ding, Yu & Lu, Huapu, 2016. "Activity participation as a mediating variable to analyze the effect of land use on travel behavior: A structural equation modeling approach," Journal of Transport Geography, Elsevier, vol. 52(C), pages 23-28.
    17. Singleton, Patrick A. & Park, Keunhyun & Lee, Doo Hong, 2021. "Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data," Journal of Transport Geography, Elsevier, vol. 93(C).
    18. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.
    19. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    20. Regine Gerike & Caroline Koszowski & Bettina Schröter & Ralph Buehler & Paul Schepers & Johannes Weber & Rico Wittwer & Peter Jones, 2021. "Built Environment Determinants of Pedestrian Activities and Their Consideration in Urban Street Design," Sustainability, MDPI, vol. 13(16), pages 1-21, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:10:y:2021:i:9:p:984-:d:638080. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.