IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i5p4395-d1084504.html
   My bibliography  Save this article

Research on Spatial Delineation Method of Urban-Rural Fringe Combining POI and Nighttime Light Data—Taking Wuhan City as an Example

Author

Listed:
  • Jing Yu

    (Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430061, China
    These authors contributed equally to this work.)

  • Yingying Meng

    (Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430061, China
    These authors contributed equally to this work.)

  • Size Zhou

    (Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430061, China)

  • Huaiwen Zeng

    (Shenzhen Urban Space Planning and Architectural Design Co., Ltd., Shenzhen 518039, China)

  • Ming Li

    (Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430061, China)

  • Zhaoxia Chen

    (Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430061, China)

  • Yan Nie

    (Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430062, China)

Abstract

The boundary delineation of the urban-rural fringe (URF) is the basic work of fine planning and governance of cities, which plays a positive role in promoting the process of global sustainable development and urban and rural integration. In the past, the delineation of URF had shortcomings such as a single selected data source, difficulty in obtaining data, and low spatial and temporal resolution. This study combines Point of Interest (POI) and Nighttime Light (NTL) data, proposes a new spatial recognition method of URF according to the characteristics of urban and rural spatial structure, and conducts empirical analysis with Wuhan as the research object, combining the information entropy of land use structure, NDVI, and population density data to verify and compare the delineation results and field verification was conducted for typical areas. The results show that (1) the fusion of POI and NTL can maximize the use of the characteristics of the differences in facility types, light intensity, and resolution between POI and NTL, compared with the urban-rural fringe boundary identified by POI, NTL or population density data alone, and it is more accurate and time-sensitive; (2) NPP and POI (fusion data of Suomi NPP-VIIRS and POI) can quantitatively identify potential central area and multi-layer structure of the city. It fluctuates between 0.2 and 0.6 in the urban core area of Wuhan and between 0.1 and 0.3 in the new town clusters, while in the URF and rural areas drops sharply to below 0.1; (3) the urban-rural fringe area of Wuhan covers a total area of 1482.35 km 2 , accounting for 17.30% of the total area of the city. Its land use types are mainly construction land, water area, and cultivated land, accounting for 40.75%, 30.03%, and 14.60% of the URF, respectively. Its NDVI and population density are at a medium level, with values of 1.630 and 2556.28 persons/km 2, respectively; (4) the double mutation law of NPP and POI in urban and rural space confirms that the URF exists objectively as a regional entity generated in the process of urban expansion, provides empirical support for the theory of urban and rural ternary structure, and has a positive reference value for the allocation of global infrastructure, industrial division, ecological function division, and other researches.

Suggested Citation

  • Jing Yu & Yingying Meng & Size Zhou & Huaiwen Zeng & Ming Li & Zhaoxia Chen & Yan Nie, 2023. "Research on Spatial Delineation Method of Urban-Rural Fringe Combining POI and Nighttime Light Data—Taking Wuhan City as an Example," IJERPH, MDPI, vol. 20(5), pages 1-22, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4395-:d:1084504
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/5/4395/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/5/4395/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shiwei Lu & Chaoyang Shi & Xiping Yang, 2019. "Impacts of Built Environment on Urban Vitality: Regression Analyses of Beijing and Chengdu, China," IJERPH, MDPI, vol. 16(23), pages 1-16, November.
    2. Jun Zhang & Xiaodie Yuan & Xueping Tan & Xue Zhang, 2021. "Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development," IJERPH, MDPI, vol. 18(13), pages 1-19, July.
    3. Zhongqiang Bai & Juanle Wang & Mingming Wang & Mengxu Gao & Jiulin Sun, 2018. "Accuracy Assessment of Multi-Source Gridded Population Distribution Datasets in China," Sustainability, MDPI, vol. 10(5), pages 1-15, April.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    2. Yan Yan & Hui Liu & Ningcheng Wang & Shenjun Yao, 2021. "How Does Low-Density Urbanization Reduce the Financial Sustainability of Chinese Cities? A Debt Perspective," Land, MDPI, vol. 10(9), pages 1-18, September.
    3. Jinyao Lin & Yaye Zhuang & Yang Zhao & Hua Li & Xiaoyu He & Siyan Lu, 2022. "Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
    4. Ziyu Wang & Nan Xia & Xin Zhao & Xing Gao & Sudan Zhuang & Manchun Li, 2023. "Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    5. Zhao, Chuyun & Tang, Jinjun & Zeng, Yu & Li, Zhitao & Gao, Fan, 2023. "Understanding the spatio-temporally heterogeneous effects of built environment on urban travel emissions," Journal of Transport Geography, Elsevier, vol. 112(C).
    6. Gao, Fan & Yang, Linchuan & Han, Chunyang & Tang, Jinjun & Li, Zhitao, 2022. "A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership," Journal of Transport Geography, Elsevier, vol. 105(C).
    7. Xuanting Li & Xiaohong Wang & Shaopeng Zhang, 2022. "Impacts of Urban Spatial Development Patterns on Carbon Emissions: Evidence from Chinese Cities," Land, MDPI, vol. 11(11), pages 1-16, November.
    8. Diandian Hao & Ziyi Yan & Yanan Wang & Bowen Wang, 2022. "Effect of Village Informal Institutions and Cadre-Mass Relationship for Farmers’ Participation in Rural Residential Environment Governance in China," IJERPH, MDPI, vol. 20(1), pages 1-16, December.
    9. Yuan Lai & Jiatong Li & Jiachen Zhang & Lan Yan & Yifeng Liu, 2022. "Do Vibrant Places Promote Active Living? Analyzing Local Vibrancy, Running Activity, and Real Estate Prices in Beijing," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    10. Kai Zhao & Jinhan Guo & Ziying Ma & Wanshu Wu, 2023. "Exploring the Spatiotemporal Heterogeneity and Stationarity in the Relationship between Street Vitality and Built Environment," SAGE Open, , vol. 13(1), pages 21582440231, February.
    11. Daniela Palacios-Lopez & Felix Bachofer & Thomas Esch & Wieke Heldens & Andreas Hirner & Mattia Marconcini & Alessandro Sorichetta & Julian Zeidler & Claudia Kuenzer & Stefan Dech & Andrew J. Tatem & , 2019. "New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products," Sustainability, MDPI, vol. 11(21), pages 1-24, October.
    12. Hua Zhang & Chen Liang & Yuxuan Pan, 2022. "Spatial Expansion of Built-Up Areas in the Beijing–Tianjin–Hebei Urban Agglomeration Based on Nighttime Light Data: 1992–2020," IJERPH, MDPI, vol. 19(7), pages 1-16, March.
    13. Fan Gao & Jinjun Tang & Zhitao Li, 2022. "Effects of spatial units and travel modes on urban commuting demand modeling," Transportation, Springer, vol. 49(6), pages 1549-1575, December.
    14. Zhenwei Wang & Xiaochun Wang & Zijin Dong & Lisan Li & Wangjun Li & Shicheng Li, 2023. "More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China," Land, MDPI, vol. 12(1), pages 1-13, January.
    15. Yihao Jiang & Zhaojin Chen & Pingjun Sun, 2022. "Urban Shrinkage and Urban Vitality Correlation Research in the Three Northeastern Provinces of China," IJERPH, MDPI, vol. 19(17), pages 1-22, August.
    16. Bahram Zikirya & Xiong He & Ming Li & Chunshan Zhou, 2021. "Urban Food Takeaway Vitality: A New Technique to Assess Urban Vitality," IJERPH, MDPI, vol. 18(7), pages 1-18, March.
    17. Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu & Jie Zhao, 2018. "Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy," Sustainability, MDPI, vol. 10(8), pages 1-16, July.
    18. Franz Schug & David Frantz & Sebastian van der Linden & Patrick Hostert, 2021. "Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    19. Gao, Fan & Han, Chunyang & Yang, Linchuan & Liang, Jian & He, Xuan & Li, Fan, 2024. "Analyzing spatiotemporal distribution patterns of metro ridership: Comparison between common-class and business-class carriage service," Journal of Transport Geography, Elsevier, vol. 115(C).
    20. Qilun Li & Lin Li & Jun Zhang & Xiong He, 2024. "Spatio-Temporal Characteristics and Driving Mechanisms of Urban Expansion in the Central Yunnan Urban Agglomeration," Land, MDPI, vol. 13(9), pages 1-24, September.

    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:jijerp:v:20:y:2023:i:5:p:4395-:d:1084504. 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.