Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques
He, Jialv ; Li, Xia LU ; Yao, Yao ; Hong, Ye LU
and Jinbao, Zhang
(2018)
In International Journal of Geographical Information Science
32(10).
p.2076-2097
- Abstract
Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has... (More)
Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.
(Less)
- author
- He, Jialv
; Li, Xia
LU
; Yao, Yao
; Hong, Ye
LU
and Jinbao, Zhang
- publishing date
- 2018-10-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- cellular automata, deep learning, large scale, Markov chain, Urban expansion
- in
- International Journal of Geographical Information Science
- volume
- 32
- issue
- 10
- pages
- 22 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85048030536
- ISSN
- 1365-8816
- DOI
- 10.1080/13658816.2018.1480783
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
- id
- ea12447e-ad22-42b3-b71d-da8a8c6985f8
- date added to LUP
- 2026-06-08 19:12:12
- date last changed
- 2026-06-11 03:36:47
@article{ea12447e-ad22-42b3-b71d-da8a8c6985f8,
abstract = {{<p>Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.</p>}},
author = {{He, Jialv and Li, Xia and Yao, Yao and Hong, Ye and Jinbao, Zhang}},
issn = {{1365-8816}},
keywords = {{cellular automata; deep learning; large scale; Markov chain; Urban expansion}},
language = {{eng}},
month = {{10}},
number = {{10}},
pages = {{2076--2097}},
publisher = {{Taylor & Francis}},
series = {{International Journal of Geographical Information Science}},
title = {{Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques}},
url = {{http://dx.doi.org/10.1080/13658816.2018.1480783}},
doi = {{10.1080/13658816.2018.1480783}},
volume = {{32}},
year = {{2018}},
}