An Urban Growth Study Based on Controllable Neighbor-effect CA

doi:10.6046/gtzyyg.2011.02.20

Abstract
References
Related Articles
Metrics

Download: PDF(664 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Celluar Automata (CA) featured by self-organizing evolvement is used to establish the urban expansion model. This paper introduces a CA model and adopts a neighbor factor to implement controllable neighbor-effect on the basis of exploring the influence of several location feature variables on cell status conversion in historical data. Obtaining simulated results with different spatial patterns is possible through changing the value of the neighbor factor. Taking the urban district of Changsha and it vicinities as the study area, the authors conducted an experiment to simulate and predict the process of urbanization, and then evaluated and analyzed the results.

Keywords Saline-alkalized land Spatial analysis Western Jilin Province

TP 79:F 291

Issue Date: 17 June 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

LIU Xing-Quan, WU Tao, GAN Xi-Qing. An Urban Growth Study Based on Controllable Neighbor-effect CA[J]. REMOTE SENSING FOR LAND & RESOURCES,2011, 23(2): 110-114.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.02.20 OR https://www.gtzyyg.com/EN/Y2011/V23/I2/110

［1］Couclelis H.Cellular Worlds:a Framework for Modeling Micro-Macro Dynamics［J］.Enviroment and Planning A,1985,17(5):585-596.

［2］Batty M.Cellular Automata and Urban Form:A Primer［J］.Journal of the American Planning Association,1997,63(2):266-274.

［3］Clarke K C,Gaydos L J.Loose-coupling a Cellular Automata Model and GIS:Long-term Urban Growth Prediction for San Francisco and Washington/Baltimore［J］.International Journal of Geographical Information Science,1998,12(7):699-714.

［4］White R,Engelen G,Uijee I.The Use of Constrained Cellular Automata for High-resolution Modeling of Urban Land-use Dynamics［J］.Environment and Planning B,1997,24(3):323-343.

［5］黎夏，叶嘉安.主成分分析与Cellular Automata在空间决策与城市模拟钟的应用［J］.中国科学，2001，31(8):683-690.

［6］黎夏，叶嘉安.遗传算法GIS结合进行空间优化决策的研究［J］.地理学报，2004，59(5):745-753.

［7］黎夏，叶嘉安.基于神经网络的单元自动机CA及真实和优化的城市模拟［J］.地理学报，2002,57(2):159-166.

［8］王济川,郭志刚.Logistic回归模型——方法与应用［M］.北京：高等教育出版社，2007.

［9］罗平.地理特征元胞自动机及城市土地利用演化研究［D］.武汉：武汉大学,2004.

［10］黎夏，刘小平.基于案例推理的元胞自动机及大区域城市演变模拟［J］.地理学报，2007，62（10）：1097-1109.

[1]	LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
[2]	Renbo SONG, Yuxin ZHU, Shangshan DING, Qiaoning HE, Xiyuan WANG, Yuexiang WANG. An automatic method for extracting skeleton lines from arbitrary polygons based on GIS spatial analysis[J]. Remote Sensing for Land & Resources, 2020, 32(1): 51-59.
[3]	Weidong ZHAO, Yong ZHENG, Haonan ZHANG, Qiong JIANG, Jiajia WEI. Remote sensing interpretation and spatial distribution characteristics of the Anhui segment of Tanlu fault zone based on multi-source data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 79-87.
[4]	Jingpeng GUO, Yinghui ZHAO, Huiqian CHEN, Ya’nan XIANG, Xumeng ZHAO, Chang QIAO. Remote sensing monitoring for temporal and spatial expansion of construction land of industrial cities in Heilongjiang[J]. Remote Sensing for Land & Resources, 2018, 30(3): 204-212.
[5]	Yuanwen ZENG, Yi DI, Yan HU, Jing CHEN, Songjiang DUAN. An analysis of spatial distribution and optimization of rural settlements:A case study of Niejia Village in Shitan Town,Hechuan District,Chongqing[J]. Remote Sensing for Land & Resources, 2018, 30(3): 113-119.
[6]	Xiaoping ZHANG, Ying LYU, Huaguo ZHANG, Chaokui LI. Remote sensing analysis of impervious surface changes in Zhoushan Islands during 1990—2011[J]. Remote Sensing for Land & Resources, 2018, 30(2): 178-185.
[7]	GUO Liqin, ZHAO Zhifang, DAI Qixue, LIANG Mingyue, FU Yixun, CHEN Bailian. Temporal and spatial evolution and genesis of rocky desertification based on RS and GIS in Wenshan Prefecture[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 106-113.
[8]	CHENG Tao. Exploring management and service mode for remote sensing data in big data era[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 202-206.
[9]	CHEN Yingyue, GAN Shu, TIAN Yudong, ZHOU Xibing. GIS spatial modeling in mountainous land evaluation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 196-200.
[10]	LI Xueyuan, ZHAO Bo, CHEN Shilei, ZHAO Yingwang, BIAN Kai. Spatial-temporal evolution analysis of mining geological environment based on RS and GIS: A case study of the Dongsheng ore district[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 167-173.
[11]	JI Cui-Cui, LI Xiao-Song, ZENG Yuan, YAN Na-Na, WU Wen-Bo, WU Bing-Fang. The Ration Spatial Distribution of Soil Loss Based on Remote Sensing and GIS in Xuanhua County[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 107-112.
[12]	HUANG Chu-Dong, SHAO Yun, LI Jing. A TEMPORAL AND SPATIAL ANALYSIS OF LAND SURFACE TEMPERATURE [JP]IN BEIJING UTILIZING REMOTE SENSING TECHNIQUES[J]. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(3): 64-68.
[13]	LIN Xian-Cheng, YANG Wu-Nian. THE APPLICATION OF RASTER DATA SPATIALANALYSIS TO URBAN LAND GRADING[J]. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(2): 91-101.
[14]	PANG Zhi-guo, LI Ji-ren, LI Qu-sheng . SPATIAL VARIATION OF SALINE-ALKALIZED LAND IN WESTERN JILIN PROVINCE AND COUNTERMEASURES FOR ITS PREVENTION AND TREATMENT[J]. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(2): 56-60.
[15]	Liu Xiang-nan, Xu Hong-mei . STUDY ON LAND USE CHANGE OF SONGNEN SANDY PLAIN BASED ON RS AND GIS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2001, 13(1): 54-59.

Viewed

Full text

Abstract

Cited

Shared

Discussed