IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p10764-d1189908.html
   My bibliography  Save this article

Spatial Distribution Characteristics and Influential Factors of Major Towns in Guizhou Province Analyzed with ArcGIS

Author

Listed:
  • Caiqing Liu

    (College of Tourism and Culture Industry, Guizhou University, Guiyang 550025, China)

  • Huifeng Pan

    (College of History and Ethnic Culture, Guizhou University, Guiyang 550025, China)

  • Yurong Wei

    (College of History and Ethnic Culture, Guizhou University, Guiyang 550025, China)

Abstract

The spatial arrangement of towns and cities reflects comprehensively on their economic, social, and cultural aspects, constituting the foundation of regional economic and social development and exerting a significant driving effect on the surrounding rural areas. In light of consolidating and expanding the achievements of poverty eradication and rural revitalization in Guizhou Province, it is crucial to clarify the spatial distribution and influencing factors of major towns in the province to effectively realize rural revitalization. Using the ArcGIS tool for spatial analysis combined with mathematical statistics, this article explores the spatial distribution characteristics and influencing factors of 97 major towns identified in the Guizhou Provincial Urban System Plan (2015–2030). The geographical concentration index of these major towns is first calculated in this study, followed by the kernel density method used to visualize their physical distribution and the usage of the closest index to reflect the spatial concentration of the studied elements. This study concludes that the major towns in Guizhou Province are concentrated yet unevenly distributed in various states and cities, forming a spatial pattern of towns with “one core, one group, two circles, six groups, and multiple points” as the main body. Additionally, the spatial structure of major towns in Guizhou Province follows a point-axis distribution highly correlated with the traffic road network. Endowment and distribution of natural environmental conditions and human tourism resources, as well as policy support, also significantly affect the distribution and development of major towns in Guizhou Province. This study on the spatial distribution characteristics and influencing factors of major towns in the province provides valuable insights for optimizing future urban planning and achieving rural revitalization in Guizhou Province.

Suggested Citation

  • Caiqing Liu & Huifeng Pan & Yurong Wei, 2023. "Spatial Distribution Characteristics and Influential Factors of Major Towns in Guizhou Province Analyzed with ArcGIS," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10764-:d:1189908
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/10764/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/14/10764/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zirui Zhan & Jeremy Cenci & Jiazhen Zhang, 2022. "Frontier of Rural Revitalization in China: A Spatial Analysis of National Rural Tourist Towns," Land, MDPI, vol. 11(6), pages 1-21, May.
    2. Marek Zagroba & Katarzyna Pawlewicz & Adam Senetra, 2021. "Analysis and Evaluation of the Spatial Structure of Cittaslow Towns on the Example of Selected Regions in Central Italy and North-Eastern Poland," Land, MDPI, vol. 10(8), pages 1-28, July.
    3. Hui Tao & Zhihui Huang & Feixiao Ran, 2018. "Rural Tourism Spatial Reconstruction Model from the Perspective of ATV: A Case Study of Mufu Township, Hubei Province, China," Sustainability, MDPI, vol. 10(8), pages 1-16, July.
    4. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    5. Yang, Chunyu & Huang, Jue & Lin, Zhibin & Zhang, Danxia & Zhu, Ying & Xu, Xinghua & Chen, Mei, 2018. "Evaluating the symbiosis status of tourist towns: The case of Guizhou Province, China," Annals of Tourism Research, Elsevier, vol. 72(C), pages 109-125.
    6. Shunqian Gao & Liu Yang & Hongzan Jiao, 2022. "Changes in and Patterns of the Tradeoffs and Synergies of Production-Living-Ecological Space: A Case Study of Longli County, Guizhou Province, China," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    7. Jie Li & Rebecca Lai Har Chiu, 2020. "State rescaling and large-scale urban development projects in China: The case of Lingang New Town, Shanghai," Urban Studies, Urban Studies Journal Limited, vol. 57(12), pages 2564-2581, September.
    8. Alperovich, Gershon, 1984. "The size distribution of cities: On the empirical validity of the rank-size rule," Journal of Urban Economics, Elsevier, vol. 16(2), pages 232-239, September.
    9. Zhang, Na & Jing, Yong-Cai & Liu, Cheng-Yu & Li, Yao & Shen, Jing, 2016. "A cellular automaton model for grasshopper population dynamics in Inner Mongolia steppe habitats," Ecological Modelling, Elsevier, vol. 329(C), pages 5-17.
    10. Hong Geng & Jing Qiao, 2018. "Assessment of Small Towns’ Fitness around China’s Major Cities: A Case Study in Wuhan City," Sustainability, MDPI, vol. 10(7), pages 1-20, June.
    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. Runchun Guo & Yanmei Xu, 2025. "Spatial Differentiation and Driving Mechanisms of Revolutionary Cultural Tourism Resources in Xinjiang," Sustainability, MDPI, vol. 17(21), pages 1-16, October.

    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. repec:ers:journl:v:xxiv:y:2021:i:4b:p:659-667 is not listed on IDEAS
    2. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. David G Mets & Michael S Brainard, 2018. "An automated approach to the quantitation of vocalizations and vocal learning in the songbird," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-29, August.
    4. Noah E. Friedkin, 1984. "Structural Cohesion and Equivalence Explanations of Social Homogeneity," Sociological Methods & Research, , vol. 12(3), pages 235-261, February.
    5. David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2011. "Measuring globalization: A hierarchical network approach," CREMA Working Paper Series 2011-11, Center for Research in Economics, Management and the Arts (CREMA).
    6. repec:cdl:itsrrp:qt6cb1f85c is not listed on IDEAS
    7. Lisa Price, 2001. "Demystifying farmers' entomological and pest management knowledge: A methodology for assessing the impacts on knowledge from IPM-FFS and NES interventions," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 18(2), pages 153-176, June.
    8. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    9. Geert Soete & Wayne DeSarbo & J. Carroll, 1985. "Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 173-192, December.
    10. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
    11. Jianwei Qi & Yayan Lu & Fang Han & Xuankai Ma & Zhaoping Yang, 2022. "Spatial Distribution Characteristics of the Rural Tourism Villages in the Qinghai-Tibetan Plateau and Its Influencing Factors," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    12. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    13. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
    14. Jiejing Wang & Yanguang Chen, 2021. "Economic Transition and the Evolution of City-Size Distribution of China’s Urban System," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    15. Hung Chau & Sarah H Bana & Baptiste Bouvier & Morgan R Frank, 2023. "Connecting higher education to workplace activities and earnings," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-18, March.
    16. Soo, Kwok Tong, 2005. "Zipf's Law for cities: a cross-country investigation," Regional Science and Urban Economics, Elsevier, vol. 35(3), pages 239-263, May.
    17. Taggart, J. H., 1999. "MNC subsidiary performance, risk, and corporate expectations," International Business Review, Elsevier, vol. 8(2), pages 233-255, April.
    18. Sorin Alexandru Ungureanu & Diana Andreea Mandricel & Bogdan Ioan Coculescu & Ionica Oncioiu, 2020. "Prevention in Dental Medicine. Case Studies and Explanations Regarding the Cost-Benefit Ratio," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 6(2), pages 135-147, June.
    19. Fang, Yixin & Wang, Junhui, 2011. "Penalized cluster analysis with applications to family data," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2128-2136, June.
    20. Xingyin Duan & Xiaobo Wu & Jie Ge & Li Deng & Liang Shen & Jingwen Xu & Xiaoying Xu & Qin He & Yixin Chen & Xuesong Gao & Bing Li, 2024. "A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images," Agriculture, MDPI, vol. 14(9), pages 1-20, August.
    21. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    22. Satoru Yokoyama & Atsuho Nakayama & Akinori Okada, 2009. "One-mode three-way overlapping cluster analysis," Computational Statistics, Springer, vol. 24(1), pages 165-179, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:15:y:2023:i:14:p:10764-:d:1189908. 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.