IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i6p879-d99424.html
   My bibliography  Save this article

Simulating Block-Level Urban Expansion for National Wide Cities

Author

Listed:
  • Ying Long

    (School of Architecture and Hang Lung Center for Real Estate, Tsinghua University, Beijing 100084, China)

  • Kang Wu

    (Beijing Key Laboratory of Megaregions Sustainable Development Modelling and School of Urban Economics and Public Affairs, Capital University of Economics and Business, Beijing 100070, China)

Abstract

Large-scale models are generally associated with large spatial modelling units, for example, counties or super grids (several to dozens of km 2 ). Few applied urban models can achieve a large spatial coverage with irregular spatial units due to data availability and computation load. The framework of automatic identification and characterization of blocks developed by Liu and Long (2016) makes such an ideal model possible by establishing the existing urban blocks using road networks and points of interest for very large areas (e.g., a country or a continent). In this study, we develop a mega-vector-blocks cellular automata model (MVB-CA) to simulate urban expansion at the block level for 654 Chinese cities. The existing urban blocks in 2012 were used for initiating the MVB-CA and are generated using multi-levelled road networks and ubiquitous points of interest. We then simulate block-based urban expansion of all the cities from 2012 to 2017. The national spatial development strategies of China are discussed extensively by academia and policy makers, while the baseline scenario and other simulated urban expansion scenarios have been tested and compared horizontally. As one of the first block-based urban expansion models at a national scale, its academic contributions, practical applications, and potential biases are also discussed in this paper. The developed MVB-CA using general approaches is also applicable for other counties.

Suggested Citation

  • Ying Long & Kang Wu, 2017. "Simulating Block-Level Urban Expansion for National Wide Cities," Sustainability, MDPI, vol. 9(6), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:879-:d:99424
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/6/879/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/6/879/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gong, Jian-zhou & Liu, Yan-sui & Xia, Bei-cheng & Zhao, Guan-wei, 2009. "Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, China," Ecological Modelling, Elsevier, vol. 220(24), pages 3612-3620.
    2. Kang Wu & Ying Long & Qizhi Mao & Xingjian Liu, 2015. "Featured Graphic. Mushrooming Jiedaos, Growing Cities: An Alternative Perspective on Urbanizing China," Environment and Planning A, , vol. 47(1), pages 1-2, January.
    3. ChuangLin Fang & XingLiang Guan & ShaSha Lu & Min Zhou & Yu Deng, 2013. "Input–Output Efficiency of Urban Agglomerations in China: An Application of Data Envelopment Analysis (DEA)," Urban Studies, Urban Studies Journal Limited, vol. 50(13), pages 2766-2790, October.
    4. Karen C. Seto & Robert K. Kaufmann, 2003. "Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data," Land Economics, University of Wisconsin Press, vol. 79(1), pages 106-121.
    5. Xiangzheng Deng & Jikun Huang & Scott Rozelle & Emi Uchida, 2010. "Economic Growth and the Expansion of Urban Land in China," Urban Studies, Urban Studies Journal Limited, vol. 47(4), pages 813-843, April.
    6. Daniel Stevens & Suzana Dragićević, 2007. "A GIS-Based Irregular Cellular Automata Model of Land-Use Change," Environment and Planning B, , vol. 34(4), pages 708-724, August.
    7. Xingjian Liu & Ying Long, 2016. "Automated identification and characterization of parcels with OpenStreetMap and points of interest," Environment and Planning B, , vol. 43(2), pages 341-360, March.
    8. Zhenjiang Shen & Mitsuhiko Kawakami & Ippei Kawamura, 2009. "Geosimulation Model Using Geographic Automata for Simulating Land-Use Patterns in Urban Partitions," Environment and Planning B, , vol. 36(5), pages 802-823, 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. Yi Lu & Shawn Laffan & Chris Pettit & Min Cao, 2020. "Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia," Environment and Planning B, , vol. 47(9), pages 1605-1621, November.
    2. Zimu Jia & Long Chen & Jingjia Chen & Guowei Lyu & Ding Zhou & Ying Long, 2020. "Urban modeling for streets using vector cellular automata: Framework and its application in Beijing," Environment and Planning B, , vol. 47(8), pages 1418-1439, October.
    3. Chun Yang, 2020. "The transformation of foreign investment-induced ‘exo(genous)-urbanisation’ amidst industrial restructuring in the Pearl River Delta, China," Urban Studies, Urban Studies Journal Limited, vol. 57(3), pages 618-635, February.

    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. Shasha Lu & Xingliang Guan & Chao He & Jiali Zhang, 2014. "Spatio-Temporal Patterns and Policy Implications of Urban Land Expansion in Metropolitan Areas: A Case Study of Wuhan Urban Agglomeration, Central China," Sustainability, MDPI, vol. 6(8), pages 1-26, July.
    2. Taiyang Zhong & Xianjin Huang & Lifang Ye & Steffanie Scott, 2014. "The Impacts on Illegal Farmland Conversion of Adopting Remote Sensing Technology for Land Inspection in China," Sustainability, MDPI, vol. 6(7), pages 1-26, July.
    3. Shu, Cheng & Xie, Hualin & Jiang, Jinfa & Chen, Qianru, 2018. "Is Urban Land Development Driven by Economic Development or Fiscal Revenue Stimuli in China?," Land Use Policy, Elsevier, vol. 77(C), pages 107-115.
    4. He, Qingsong & Xu, Min & Xu, Zike & Ye, Yanmei & Shu, Xianfan & Xie, Peng & Wu, Jiayu, 2019. "Promotion incentives, infrastructure construction, and industrial landscapes in China," Land Use Policy, Elsevier, vol. 87(C).
    5. Wang, Chenglong & Liu, Hui & Zhang, Mengtian & Wei, Zongcai, 2018. "The border effect on urban land expansion in China: The case of Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 78(C), pages 287-294.
    6. Li Jiang & Yonghui Zhang, 2016. "Modeling Urban Expansion and Agricultural Land Conversion in Henan Province, China: An Integration of Land Use and Socioeconomic Data," Sustainability, MDPI, vol. 8(9), pages 1-12, September.
    7. Ronghui Tan & Kehao Zhou & Qingsong He & Hengzhou Xu, 2016. "Analyzing the Effects of Spatial Interaction among City Clusters on Urban Growth—Case of Wuhan Urban Agglomeration," Sustainability, MDPI, vol. 8(8), pages 1-14, August.
    8. Chau, Nancy H. & Qin, Yu & Zhang, Weiwen, 2015. "Networked Leaders in the Shadow of the Market – A Chinese Experiment in Allocating Land Conversion Rights," Working Papers 250022, Cornell University, Department of Applied Economics and Management.
    9. Han, Wenjing & Zhang, Xiaoling & Zheng, Xian, 2020. "Land use regulation and urban land value: Evidence from China," Land Use Policy, Elsevier, vol. 92(C).
    10. David Hidalgo García & Julián Arco Díaz & Adelaida Martín Martín & Emilio Gómez Cobos, 2022. "Spatiotemporal Analysis of Urban Thermal Effects Caused by Heat Waves through Remote Sensing," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    11. Bingkui Qiu & Shasha Lu & Min Zhou & Lu Zhang & Yu Deng & Ci Song & Zuo Zhang, 2015. "A Hybrid Inexact Optimization Method for Land-Use Allocation in Association with Environmental/Ecological Requirements at a Watershed Level," Sustainability, MDPI, vol. 7(4), pages 1-25, April.
    12. Tang, Peng & Feng, Yue & Li, Min & Zhang, Yanyan, 2021. "Can the performance evaluation change from central government suppress illegal land use in local governments? A new interpretation of Chinese decentralisation," Land Use Policy, Elsevier, vol. 108(C).
    13. ChuangLin Fang & XingLiang Guan & ShaSha Lu & Min Zhou & Yu Deng, 2013. "Input–Output Efficiency of Urban Agglomerations in China: An Application of Data Envelopment Analysis (DEA)," Urban Studies, Urban Studies Journal Limited, vol. 50(13), pages 2766-2790, October.
    14. Tommaso Orusa & Annalisa Viani & Enrico Borgogno-Mondino, 2024. "Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)," Land, MDPI, vol. 13(1), pages 1-21, January.
    15. Siyi Chen & Zhigang Chen & Yan Shen, 2021. "Can improving law enforcement effectively curb illegal land use in China?," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-17, February.
    16. Su, Baozhong & Li, Yuheng & Li, Lequn & Wang, Yue, 2018. "How does nonfarm employment stability influence farmers' farmland transfer decisions? Implications for China’s land use policy," Land Use Policy, Elsevier, vol. 74(C), pages 66-72.
    17. Jianglong Chen & Jinlong Gao & Feng Yuan, 2016. "Growth Type and Functional Trajectories: An Empirical Study of Urban Expansion in Nanjing, China," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
    18. Weiguo Liu & Karen C Seto, 2008. "Using the ART-MMAP Neural Network to Model and Predict Urban Growth: A Spatiotemporal Data Mining Approach," Environment and Planning B, , vol. 35(2), pages 296-317, April.
    19. Wu, Rong & Li, Yingcheng & Wang, Shaojian, 2022. "Will the construction of high-speed rail accelerate urban land expansion? Evidences from Chinese cities," Land Use Policy, Elsevier, vol. 114(C).
    20. Jiangsu Li & Weihua Li & Bo Li & Liangrong Duan & Tianjiao Zhang & Qi Jia, 2022. "Construction Land Expansion of Resource-Based Cities in China: Spatiotemporal Characteristics and Driving Factors," IJERPH, MDPI, vol. 19(23), pages 1-20, December.

    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:9:y:2017:i:6:p:879-:d:99424. 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.