Multi-Source Data-Based Evaluation of Suitability of Land for Elderly Care and Layout Optimization: A Case Study of Changsha, China

Yang, Jun; Lou, Zhifei; Tang, Xinglong; Sun, Yu

doi:10.3390/su15032034

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…The article 'Multi-Source Data-Based Evaluation of Suitability of Land for Elderly Care and Layout Optimization: A Case Study of Changsha, China' [8] utilizes multiple data sources and constructs suitability evaluation indicator systems for community-based residential land and institutional elderly care land development. The results reveal significant spatial variations in the suitability of elderly care facility land.…”

Section: Index Measurement and Assessmentmentioning

confidence: 99%

Urban Planning and Sustainable Land Use

2023

Sustainability

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Section: Index Measurement and Assessmentmentioning

confidence: 99%

Urban Planning and Sustainable Land Use

2023

Sustainability

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“…Many factors affect the site selection of elderly care facilities, and it is not only necessary to consider economic benefits, but also social benefits. [20] The influencing factors in machine learning models are independent variables, also known as predictive variables. According to previous research, the main factors affecting the location of elderly care facilities include population factors, [21] economic level, [22] natural factors, [23] medical conditions, [24] convenience of life, and transportation accessibility.…”

Section: Feature Constructionmentioning

confidence: 99%

A multi-source heterogeneous data-driven method for the site selection of elderly care facilities

Wang

2023

Preprint

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In the site selection of elderly care facilities era, the traditional methods of site selection for elderly care facilities are subjective, the data sources of methods are single, and the research objects of the methods are not realistic, this paper presents a multi-source heterogeneous data-driven method for the site selection of elderly care facilities. This method takes the Xicheng and Dongcheng districts of Beijing as the research objects. On the grid scale of 500m*500m, six features of population factors, economic level, traffic accessibility, medical conditions, the convenience of life, and natural factors are constructed through multi-source data. Using an integrated algorithm, the location of elderly care facilities is predicted quantitatively, and the priority of features is sorted. The research found that: (1) The model in this paper predicts more accurately than traditional models (2) the number of education facilities, proportion of green areas, medical conditions, traffic accessibility, and the number of shopping facilities are the main features for the site selection of elderly care facilities in Xicheng and Dongcheng districts. This method can also provide references for the site selection of other facilities.

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“…Machine learning methods are widely used to analyze correlations between urban elements, and machine learning algorithms are applied according to different urban data and problems [35][36][37]. In addition, studies using POI data and machine learning to assess the current locations of urban public services and to make predictions about future locations are beginning to emerge [38,39]. These studies are an important foundation for this research.…”

Section: Introductionmentioning

confidence: 99%

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

Feng,

Tang,

Zou

2024

Land

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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.

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Multi-Source Data-Based Evaluation of Suitability of Land for Elderly Care and Layout Optimization: A Case Study of Changsha, China

Cited by 5 publications

References 13 publications

Urban Planning and Sustainable Land Use

Urban Planning and Sustainable Land Use

A multi-source heterogeneous data-driven method for the site selection of elderly care facilities

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

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