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Quantitative Identification of Rural Functions Based on Big Data: A Case Study of Dujiangyan Irrigation District in Chengdu

Author

Listed:
  • Qidi Dong

    (College of Landscape Architecture, Sichuan Agricultural University, Chengdu 611130, China)

  • Jun Cai

    (College of Landscape Architecture, Sichuan Agricultural University, Chengdu 611130, China)

  • Linjia Wu

    (College of Landscape Architecture, Sichuan Agricultural University, Chengdu 611130, China)

  • Di Li

    (College of Landscape Architecture, Sichuan Agricultural University, Chengdu 611130, China)

  • Qibing Chen

    (College of Landscape Architecture, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

Urbanization increases the scales of urban spaces and the sizes of their populations, causing the functions in cities and towns to be in short supply. This study carries out functional space identification on the Dujiangyan elite irrigation area based on remote sensing data and point of interest (POI) data from Open Street Map (OSM), enabling the use of POI data to analyze rural functional spaces. Research and development and big data can greatly improve the accuracy of spatial function recognition, but research on rural spaces has limitations regarding the amount of available data. The Dujiangyan Irrigation District has low spatial aggregation levels for functions, scattered functions and linear distributions along roads. The mixing degrees of regional functions are low, the connections between functional elements are insufficient, and the comprehensive functional quality is low. The features of various functional elements in the region are significant, mostly in the discrete distribution mode, and functional compounding has become a trend. Therefore, it is necessary to integrate spatial resources and improve the centrality of cities and towns to realize the optimal allocation of resources and enable the development of surrounding cities and towns.

Suggested Citation

  • Qidi Dong & Jun Cai & Linjia Wu & Di Li & Qibing Chen, 2022. "Quantitative Identification of Rural Functions Based on Big Data: A Case Study of Dujiangyan Irrigation District in Chengdu," Land, MDPI, vol. 11(3), pages 1-17, March.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:3:p:386-:d:764926
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    References listed on IDEAS

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    1. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
    2. Teng Zhong & Guonian Lü & Xiuming Zhong & Haoming Tang & Yu Ye, 2020. "Measuring Human-Scale Living Convenience through Multi-Sourced Urban Data and a Geodesign Approach: Buildings as Analytical Units," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
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    Cited by:

    1. Haoying Li & Jonas Østergaard Nielsen & Rui Chen, 2023. "Rural Entrepreneurship Development in Southwest China: A Spatiotemporal Analysis," Land, MDPI, vol. 12(4), pages 1-22, March.
    2. Qidi Dong & Jun Cai & Shuo Chen & Pengman He & Xuli Chen, 2022. "Spatiotemporal Analysis of Urban Green Spatial Vitality and the Corresponding Influencing Factors: A Case Study of Chengdu, China," Land, MDPI, vol. 11(10), pages 1-17, October.

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