Print Email Facebook Twitter Machine learning for spatial analyses in urban areas Title Machine learning for spatial analyses in urban areas: a scoping review Author Casali, Y. (TU Delft Transport and Logistics) Aydin, N.Y. (TU Delft System Engineering) Comes, M. (TU Delft Transport and Logistics; TU Delft System Engineering) Date 2022 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. Subject Geospatial dataMachine learningReviewSpatial analysesUrban areas To reference this document use: http://resolver.tudelft.nl/uuid:37a392ea-fc9d-40b8-8902-365c270c146a DOI https://doi.org/10.1016/j.scs.2022.104050 ISSN 2210-6707 Source Sustainable Cities and Society, 85 Part of collection Institutional Repository Document type review Rights © 2022 Y. Casali, N.Y. Aydin, M. Comes Files PDF 1_s2.0_S2210670722003687_main.pdf 3.06 MB Close viewer /islandora/object/uuid:37a392ea-fc9d-40b8-8902-365c270c146a/datastream/OBJ/view