Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and challenges

Tékouabou, Stéphane Cédric Koumétio; Diop, El Bachir; Azmi, Rida; Jaligot, Rémi; Chenal, Jérôme

doi:10.1016/j.jksuci.2021.08.007

Cited by 30 publications

(32 citation statements)

References 106 publications

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“…The Machine Learning (ML) Random Forests method, as a decision support tool, allow us to process large amount of data and extract useful information for supporting and providing decisions (Casali et al, 2022;Tekouabou et al, 2022). Here we use ML to classify residential neighbourhoods at the regional scale for the Arc region.…”

Section: Discussionmentioning

confidence: 99%

“…We integrate the natural capital value of the land in the density-scenario development, so that land that has a high value for food production, ecosystem services, or biodiversity is protected from development. The methods of analysis developed here provide a better understanding of spatial planning than traditional methods of urban densifications at regional scale (Casali et al, 2022;Eggimann et al, 2021;Tekouabou et al, 2022) and can be applied to other regions in the UK (and for other countries). Here these methods have been used to provide accurate estimates of the housing capacity for the potential land identified in Local Plans and as brownfield lands in the Arc under different density scenarios.…”

Section: Discussionmentioning

confidence: 99%

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A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

Mohajeri

Walch

Smith

et al. 2023

Sustainable Cities and Society

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

Mohajeri

Walch

Smith

et al. 2023

Sustainable Cities and Society

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“…Since previous studies required considerable human effort to be devoted to digitalizing mapping or pre-processing data, this was likely to result in lengthy analyses with high error rates when assessing large and complex data sets. Most previous statistical models are not well adapted to deal with the rapid growth of today's cities, failing to take advantage of the advent of urban Big Data [33]. The need for a robust and reliable BCR measuring tool to overcome the challenge of rapid urbanization is clear.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas

Shin

Kwon

et al. 2022

Applied Sciences

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Urban parameters, such as building density and the building coverage ratio (BCR), play a crucial role in urban analysis and measurement. Although several approaches have been proposed for BCR estimations, a quick and effective tool is still required due to the limitations of statistical-based and manual mapping methods. Since a building footprint is crucial for the BCR calculation, we hypothesize that Deep Learning (DL) models can aid in the BCR computation, due to their proven automatic building footprint extraction capability. Thus, this study applies the DL framework in the ArcGIS software to the BCR calculation task and evaluates its efficiency for a new industrial district in South Korea. Although the accuracy achieved was limited due to poor-quality input data and issues with the training process, the result indicated that the DL-based approach is applicable for BCR measuring, which is a step toward suggesting an implication of this method. Overall, the potential utility of this proposed approach for the BCR measurement promises to be considerable.

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“…In particular, as emerging urban data, POI data have been widely used in studies in areas such as urban function and land use planning, and many studies have used POI data to attempt to analyze the distribution of urban public facilities [29][30][31]. Faced with a large amount of urban data that need to be analyzed, machine learning has been widely applied to the mining of urban big data [32]. Compared with traditional linear and mathematical modeling methods, machine learning has unique advantages in the analysis of non-linear problems in big data [33].…”

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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Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and challenges

Cited by 30 publications

References 106 publications

A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas

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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