TY - JOUR
T1 - Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography
T2 - An extensive systematic review
AU - Wang, Siqin
AU - Huang, Xiao
AU - Liu, Pengyuan
AU - Zhang, Mengxi
AU - Biljecki, Filip
AU - Hu, Tao
AU - Fu, Xiaokang
AU - Liu, Lingbo
AU - Liu, Xintao
AU - Wang, Ruomei
AU - Huang, Yuanyuan
AU - Yan, Jingjing
AU - Jiang, Jinghan
AU - Chukwu, Michaelmary
AU - Reza Naghedi, Seyed
AU - Hemmati, Moein
AU - Shao, Yaxiong
AU - Jia, Nan
AU - Xiao, Zhiyang
AU - Tian, Tian
AU - Hu, Yaxin
AU - Yu, Lixiaona
AU - Yap, Winston
AU - Macatulad, Edgardo
AU - Chen, Zhuo
AU - Cui, Yunhe
AU - Ito, Koichi
AU - Ye, Mengbi
AU - Fan, Zicheng
AU - Lei, Binyu
AU - Bao, Shuming
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/4
Y1 - 2024/4
N2 - This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the Web of Science in the relevant fields and select 1516 papers that we identify as human geography studies using GeoAI via human scanning conducted by several research groups around the world. We outline the GeoAI applications in human geography by systematically summarising the number of publications over the years, empirical studies across countries, the categories of data sources used in GeoAI applications, and their modelling tasks across different subdomains. We find out that existing human geography studies have limited capacity to monitor complex human behaviour and examine the non-linear relationship between human behaviour and its potential drivers—such limits can be overcome by GeoAI models with the capacity to handle complexity. We elaborate on the current progress and status of GeoAI applications within each subdomain of human geography, point out the issues and challenges, as well as propose the directions and research opportunities for using GeoAI in future human geography studies in the context of sustainable and open science, generative AI, and quantum revolution.
AB - This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the Web of Science in the relevant fields and select 1516 papers that we identify as human geography studies using GeoAI via human scanning conducted by several research groups around the world. We outline the GeoAI applications in human geography by systematically summarising the number of publications over the years, empirical studies across countries, the categories of data sources used in GeoAI applications, and their modelling tasks across different subdomains. We find out that existing human geography studies have limited capacity to monitor complex human behaviour and examine the non-linear relationship between human behaviour and its potential drivers—such limits can be overcome by GeoAI models with the capacity to handle complexity. We elaborate on the current progress and status of GeoAI applications within each subdomain of human geography, point out the issues and challenges, as well as propose the directions and research opportunities for using GeoAI in future human geography studies in the context of sustainable and open science, generative AI, and quantum revolution.
KW - GeoAI
KW - Geographic subdomains
KW - Geospatial artificial intelligence
KW - Human geography
KW - Systematic review
UR - http://www.scopus.com/inward/record.url?scp=85187354023&partnerID=8YFLogxK
U2 - 10.1016/j.jag.2024.103734
DO - 10.1016/j.jag.2024.103734
M3 - Review article
AN - SCOPUS:85187354023
SN - 1569-8432
VL - 128
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
M1 - 103734
ER -