Optimizing the Layout of Service Facilities for Older People Based on POI Data and Machine Learning: Guangzhou City as an Example

Huicheng Feng (), Xiaoxiang Tang and Cheng Zou
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Huicheng Feng: State Key Laboratory of Subtropical Building and Urban Science, Department of Landscape Architecture, School of Architecture, South China University of Technology, Guangzhou 510641, China
Xiaoxiang Tang: State Key Laboratory of Subtropical Building and Urban Science, Department of Landscape Architecture, School of Architecture, South China University of Technology, Guangzhou 510641, China
Cheng Zou: State Key Laboratory of Subtropical Building and Urban Science, Department of Landscape Architecture, School of Architecture, South China University of Technology, Guangzhou 510641, China

Land, 2024, vol. 13, issue 5, 1-15

Abstract: Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people.

Keywords: older people; service facilities; Guangzhou; POI; machine learning; layout optimization; aging (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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