Using a maximum entropy model to optimize the stochastic component of urban cellular automata models-Reference-Cited by-同舟云学术

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Using a maximum entropy model to optimize the stochastic component of urban cellular automata models

Published:2019-11-11 Issue:5 Volume:34 Page:924-946
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Wang Haijun¹²,Zhang Bin¹^ORCID,Xia Chang³^ORCID,He Sanwei⁴,Zhang Wenting⁵

Affiliation:

1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

2. Key Laboratory of Geographic Information System of MOE, Wuhan University, Wuhan, China

3. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China

4. School of Public Administration, Zhongnan University of Economics and Law, Wuhan, China

5. College of Resources and Environment, Huazhong Agricultural University, Wuhan, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2019.1687898

Reference63 articles.

1. Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics

2. A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS

3. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion

4. From raster to vector cellular automata models: A new approach to simulate urban growth with the help of graph theory

5. Spatial and temporal dimensions of land use change in cross border region of Luxembourg. Development of a hybrid approach integrating GIS, cellular automata and decision learning tree models

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4. A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit;Transactions in GIS;2024-04-26

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