IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i8p2632-d160187.html
   My bibliography  Save this article

Multi-Disciplinary Determination of the Rural/Urban Boundary: A Case Study in Xi’an, China

Author

Listed:
  • Lei Fang

    (State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
    University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China)

  • Yingjie Wang

    (State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
    University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China)

Abstract

Rapid urbanization in China has blurred the boundaries between rural and urban areas in both geographic and conceptual terms. Accurately identifying this boundary in a given area is an important prerequisite for studies of these areas, but previous research has used fairly simplistic factors to distinguish the two areas (such as population density). In this study, we built a model combining multi-layer conditions and cumulative percentage methods based on five indicators linking spatial, economic, and demographic factors to produce a more comprehensive and quantitative method for identifying rural and urban areas. Using Xi’an, China as a case study, our methods produced a more accurate determination of the rural-urban divide when compared to data from the National Bureau of Statistics of the People’s Republic of China. Specifically, the urbanization level was 3.24% lower in the new model, with a total urban area that was 621.87 km 2 lower. These results were checked by field survey and satellite imagery for accuracy. This new model thus provides local governments and other interested parties a theoretical and technological foundation for more accurate rural/urban planning and management in the future.

Suggested Citation

  • Lei Fang & Yingjie Wang, 2018. "Multi-Disciplinary Determination of the Rural/Urban Boundary: A Case Study in Xi’an, China," Sustainability, MDPI, vol. 10(8), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2632-:d:160187
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/8/2632/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/8/2632/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. F Wu & C J Webster, 1998. "Simulation of Land Development through the Integration of Cellular Automata and Multicriteria Evaluation," Environment and Planning B, , vol. 25(1), pages 103-126, February.
    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. Yi-Ya Hsu & Scott Hawken & Samad Sepasgozar & Zih-Hong Lin, 2022. "Beyond the Backyard: GIS Analysis of Public Green Space Accessibility in Australian Metropolitan Areas," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    2. Xuewei Chen & Hongliang Chen, 2020. "Differences in Preventive Behaviors of COVID-19 between Urban and Rural Residents: Lessons Learned from A Cross-Sectional Study in China," IJERPH, MDPI, vol. 17(12), pages 1-14, June.
    3. Mortoja, Md. Golam & Yigitcanlar, Tan & Mayere, Severine, 2020. "What is the most suitable methodological approach to demarcate peri-urban areas? A systematic review of the literature," Land Use Policy, Elsevier, vol. 95(C).
    4. Chong Zhao & Yu Li & Min Weng, 2021. "A Fractal Approach to Urban Boundary Delineation Based on Raster Land Use Maps: A Case of Shanghai, China," Land, MDPI, vol. 10(9), pages 1-21, September.

    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. Di Traglia, Mario & Attorre, Fabio & Francesconi, Fabio & Valenti, Roberto & Vitale, Marcello, 2011. "Is cellular automata algorithm able to predict the future dynamical shifts of tree species in Italy under climate change scenarios? A methodological approach," Ecological Modelling, Elsevier, vol. 222(4), pages 925-934.
    2. Han, Yu & Jia, Haifeng, 2017. "Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China," Ecological Modelling, Elsevier, vol. 353(C), pages 107-116.
    3. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    4. Liu, Dongya & Zheng, Xinqi & Zhang, Chunxiao & Wang, Hongbin, 2017. "A new temporal–spatial dynamics method of simulating land-use change," Ecological Modelling, Elsevier, vol. 350(C), pages 1-10.
    5. Deal, Brian & Schunk, Daniel, 2004. "Spatial dynamic modeling and urban land use transformation: a simulation approach to assessing the costs of urban sprawl," Ecological Economics, Elsevier, vol. 51(1-2), pages 79-95, November.
    6. Yan Liu & Yongjiu Feng, 2016. "Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    7. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    8. repec:asg:wpaper:1038 is not listed on IDEAS
    9. Qing Zheng & Xuan Yang & Ke Wang & Lingyan Huang & Amir Reza Shahtahmassebi & Muye Gan & Melanie Valerie Weston, 2017. "Delimiting Urban Growth Boundary through Combining Land Suitability Evaluation and Cellular Automata," Sustainability, MDPI, vol. 9(12), pages 1-22, November.
    10. Sorel, Luc & Viaud, Valérie & Durand, Patrick & Walter, Christian, 2010. "Modeling spatio-temporal crop allocation patterns by a stochastic decision tree method, considering agronomic driving factors," Agricultural Systems, Elsevier, vol. 103(9), pages 647-655, November.
    11. Xuesong Gao & Yu Liu & Lun Liu & Qiquan Li & Ouping Deng & Yali Wei & Jing Ling & Min Zeng, 2018. "Is Big Good or Bad?: Testing the Performance of Urban Growth Cellular Automata Simulation at Different Spatial Extents," Sustainability, MDPI, vol. 10(12), pages 1-10, December.
    12. Jun Ren & Wei Zhou & Xuelu Liu & Liang Zhou & Jing Guo & Yonghao Wang & Yanjun Guan & Jingtian Mao & Yuhan Huang & Rongrong Ma, 2019. "Urban Expansion and Growth Boundaries in an Oasis City in an Arid Region: A Case Study of Jiayuguan City, China," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    13. Thekdi, Shital A. & Lambert, James H., 2015. "Integrated risk management of safety and development on transportation corridors," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 1-12.
    14. Jiangfeng She & Zhongqing Guan & Fangfang Cai & Lijie Pu & Junzhong Tan & Tao Chen, 2017. "Simulation of Land Use Changes in a Coastal Reclaimed Area with Dynamic Shorelines," Sustainability, MDPI, vol. 9(3), pages 1-19, March.
    15. Tian, Guangjin & Ouyang, Yun & Quan, Quan & Wu, Jianguo, 2011. "Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA," Ecological Modelling, Elsevier, vol. 222(5), pages 1129-1138.
    16. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.
    17. Liu, Xiaoping & Li, Xia & Shi, Xun & Wu, Shaokun & Liu, Tao, 2008. "Simulating complex urban development using kernel-based non-linear cellular automata," Ecological Modelling, Elsevier, vol. 211(1), pages 169-181.
    18. Cong Cao & Suzana Dragićević & Songnian Li, 2019. "Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    19. Dagmar Haase, 2005. "Derivation of robust predictor variables for modelling urban shrinkage and its effects at different scales," ERSA conference papers ersa05p322, European Regional Science Association.
    20. Cheng Li & Jie Zhao & Nguyen Xuan Thinh & Yantao Xi, 2018. "Assessment of the Effects of Urban Expansion on Terrestrial Carbon Storage: A Case Study in Xuzhou City, China," Sustainability, MDPI, vol. 10(3), pages 1-17, February.
    21. Ye Zhou & Feng Zhang & Zhenhong Du & Xinyue Ye & Renyi Liu, 2017. "Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth," Sustainability, MDPI, vol. 9(10), pages 1-19, October.

    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:jsusta:v:10:y:2018:i:8:p:2632-:d:160187. 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.