Applications and New Trends of Machine Learning in Urban Simulation Research

CHEN Yimin; LI Xia

doi:10.13203/j.whugis20200423

Volume 45 Issue 12

Dec. 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(12): 1884-1889. > DOI: 10.13203/j.whugis20200423

CHEN Yimin, LI Xia. Applications and New Trends of Machine Learning in Urban Simulation Research[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1884-1889. DOI: 10.13203/j.whugis20200423

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Applications and New Trends of Machine Learning in Urban Simulation Research

CHEN Yimin^{1, 2,},
LI Xia^{3, 4, ,}

1.
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2.
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
3.
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
4.
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China

Funds:

The National Key Research and Development Program of China 2019YFA0607201

the National Natural Science Foundation of China 41871306

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Author Bio:
CHEN Yimin, PhD, associate professor, specializes in urban computation and scenario simulation. E-mail:chenym49@mail.sysu.edu.cn
Corresponding author:
LI Xia, PhD, professor. E-mail:lixia@geo.ecnu.edu.cn
Received Date: August 16, 2020
Published Date: December 04, 2020

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Abstract

Abstract

Urban simulation research originated between the 1980s and 1990s. Today urban simulation has become a new paradigm of urban research, which is an important outcome of computational thinking in urban research. Urban simulation methods are usually based on cellular automata (CA) and machine learning. A series of urban CA models have been developed to simulate complex urban evolution processes and associated multi-scenario analysis. This paper reviews the origin and progress of urban simulation research. With the discussion of urban CA's general structure, we explains the necessity and feasibility of machine learning methods to support urban simulation. Furthermore, we reviews the integration of machine learning and CA in urban research, and also discusses its new trends and emerging challenges.
- cellular automata (CA),
- machine learning,
- urban simulation

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References

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