TY - JOUR
T1 - Machine learning for spatial analyses in urban areas
T2 - a scoping review
AU - Casali, Ylenia
AU - Aydin, Nazli Yonca
AU - Comes, Tina
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Geospatial data
KW - Machine learning
KW - Review
KW - Spatial analyses
KW - Urban areas
UR - http://www.scopus.com/inward/record.url?scp=85134704207&partnerID=8YFLogxK
U2 - 10.1016/j.scs.2022.104050
DO - 10.1016/j.scs.2022.104050
M3 - Review article
AN - SCOPUS:85134704207
SN - 2210-6707
VL - 85
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
M1 - 104050
ER -