Machine learning for spatial analyses in urban areas

a scoping review

Review (2022)
Author(s)

Ylenia Casali (TU Delft - Transport and Logistics)

Nazli Yonca Aydin (TU Delft - System Engineering)

Martina Comes (TU Delft - System Engineering, TU Delft - Transport and Logistics)

Research Group
Transport and Logistics
Copyright
© 2022 Y. Casali, N.Y. Aydin, M. Comes
DOI related publication
https://doi.org/10.1016/j.scs.2022.104050
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Y. Casali, N.Y. Aydin, M. Comes
Research Group
Transport and Logistics
Volume number
85
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Abstract

The challenges for sustainable cities to protect the environment, ensure economic growth, and maintain social justice have been widely recognized. Along with the digitization, availability of large datasets, Machine Learning (ML) and Artificial Intelligence (AI) are promising to revolutionize the way we analyze and plan urban areas, opening new opportunities for the sustainable city agenda. Especially urban spatial planning problems can benefit from ML approaches, leading to an increasing number of ML publications across different domains. What is missing is an overview of the most prominent domains in spatial urban ML along with a mapping of specific applied approaches. This paper aims to address this gap and guide researchers in the field of urban science and spatial data analysis to the most used methods and unexplored research gaps. We present a scoping review of ML studies that used geospatial data to analyze urban areas. Our review focuses on revealing the most prominent topics, data sources, ML methods and approaches to parameter selection. Furthermore, we determine the most prominent patterns and challenges in the use of ML. Through our analysis, we identify knowledge gaps in ML methods for spatial data science and data specifications to guide future research.