Research on Site Selection Planning of Urban Parks Based on POI and Machine Learning—Taking Guangzhou City as an Example

Xiaoxiang Tang, Cheng Zou, Chang Shu (), Mengqing Zhang and Huicheng Feng
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Xiaoxiang Tang: School of Architecture, South China University of Technology, Guangzhou 510641, China
Cheng Zou: School of Architecture, South China University of Technology, Guangzhou 510641, China
Chang Shu: College of Water Resources and Civil Engineering, South China Agricultural University, Guangzhou 510642, China
Mengqing Zhang: School of Architecture, South China University of Technology, Guangzhou 510641, China
Huicheng Feng: School of Architecture, South China University of Technology, Guangzhou 510641, China

Land, 2024, vol. 13, issue 9, 1-18

Abstract: Against the background of smart city construction and the increasing application of big data in the field of planning, a method is proposed to effectively improve the objectivity, scientificity, and global nature of urban park siting, taking Guangzhou and its current urban park layout as an example. The proposed approach entails integrating POI data and innovatively applying machine learning algorithms to construct a decision tree model to make predictions for urban park siting. The results show that (1) the current layout of urban parks in Guangzhou is significantly imbalanced and has blind zones, and with an expansion of the search radius, the distribution becomes more concentrated; high-density areas decrease from the center outward in a circle, which manifests as a pattern of high aggregation at the core and low dispersion at the edge. (2) Urban park areas with a service pressure of level 3 have the largest coverage and should be prioritized for construction as much as possible; there are fewer areas at levels 4 and 5, which are mainly concentrated in the central city, and unreasonable resource allocation is a problem that needs to be solved urgently. (3) There was a preliminary prediction of 6825 sites suitable for planning, and the fit with existing city parks was 93.7%. The prediction results were reasonable, and the method was feasible. After further screening through the coupling and superposition of the service pressure and the layout status quo, 1537 locations for priority planning were finally obtained. (4) Using the ID3 machine learning algorithm to predict urban park sites is conducive to the development of an overall optimal layout, and subjectivity in site selection can be avoided, providing a methodological reference for the planning and construction of other infrastructure or the optimization of layouts.

Keywords: smart cities; big data; machine learning; decision trees; urban parks; site planning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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