Competitive Learning Approach to GIS Based Land Use Suitability Analysis

doi:10.5814/j.issn.1674-764x.2016.06.003

Journal of Resources and Ecology ›› 2016, Vol. 7 ›› Issue (6): 430-438.DOI: 10.5814/j.issn.1674-764x.2016.06.003

• Contents • Previous Articles Next Articles

Competitive Learning Approach to GIS Based Land Use Suitability Analysis

TELLEZ Ricardo Delgado^{1, 2}, WANG Shaohua^{2, 3, *}, ZHONG Ershun², CAI Wenwen³, LONG Liang²

1. University of Chinese Academy of Sciences, Beijing 100049, China;
2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences; Beijing 100101, China;
3. SuperMap Software Co. Ltd., Beijing 100015, China

Received:2015-12-15 Revised:2016-08-05 Online:2016-11-15 Published:2016-11-15
Contact: WANG Shaohua. E-mail: wangshaohua@lreis.ac.cn
Supported by:
The research has been partially supported by project 2009DFA13000 funded by the Ministry of Science and Technology of the People’s Republic of China, Beijing science and technology projects (Z151100003615012, Z151100003115007), Independent research project of State Key Laboratory of Resources and Environmental Information System (088RAC00YA), Surveying and mapping project of public welfare (201512015), Project of Beijing Excellent Talents (201500002685XG242), National Postdoctoral International Exchange Program (Grant No. 20150081), National Natural Science Foundation of China (Grant No. 41101116, 41271546), Postdoctoral Fund of Chaoyang District.

Abstract

Abstract: This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms (CLG-LUSA). It uses Kohonen’s Self Organized Maps (SOM) and Linear Vector Quantization (LVQ) among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision’s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a municipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making.

Key words: GIS, land use suitability analysis, linear vector quantization, self organized maps

TELLEZ Ricardo Delgado, WANG Shaohua, ZHONG Ershun, CAI Wenwen, LONG Liang. Competitive Learning Approach to GIS Based Land Use Suitability Analysis[J]. Journal of Resources and Ecology, 2016, 7(6): 430-438.

References

1 Bação F, V Lobo, M Painho. 2005. The self-organizing map, the Geo-SOM, and relevant variants for geosciences. Computers & Geosciences , 31(2): 155-163.
2 Bagheri M, W N Azmin. 2010. Application of GIS and AHP technique for land-use suitability analysis on coastal area in Terengganu. World Automation Congress , 184(225): 1-6.
3 Bruno G, A Genovese, A Sgalambro. 2009. An agent-based framework for modeling and solving location problems. TOP , 18(1): 81-96.
4 Céréghino R, Y-S Park. 2009. Review of the self-organizing map (SOM) approach in water resources: Commentary. Environmental Modelling & Software , 24(8): 945-947.
5 Kohonen T. 2000 (3rd.). Self-Organizing Maps. Springer: Berlin Heidelberg, 266-270.
6 Ligmann-Zielinska A, P Jankowski. 2008. A framework for sensitivity analysis in spatial multiple criteria evaluation. Geographic Information Science , 5266: 217-233.
7 Ligmann-Zielinska A, P Jankowski. 2010. Impact of proximity-adjusted preferences on rank-order stability in geographical multicriteria decision analysis. Journal of Geographical Systems , 2012, 60(14): 167-187.
8 LinW-T, P-H Huang. 2009. Using neural network classifier and the GIS technique for automatic landslide hazard assessment. The 17th International Conference on Geoinformatics , 1-6.
9 Leake C, J Malczewski. 2000. GIS and Multicriteria Decision Analysis. Journal of the Operational Research Society , 51(2): 699-708.
10 Malczewski J. 2004. GIS-based land-use suitability analysis: a critical overview. Progress in Planning , 62(1): 3-65.
11 Miller H J, J W Han. 2007 (2nd.). Geographic data mining and knowledge discovery. Taylor & Francis: Bristol Pennsylvania, 352-366.
12 Murgante B, G Las Casas. 2004. G.I.S. and fuzzy sets for the land suitability analysis. Lecture Notes in Computer Science , 3044: 1036-1045.
13 Von Neumann J, O Morgenstern. 2007 (60th-Anniversary Eds.). Theory of Games and Economic Behavior . Princeton University Press: Princeton, 1-776.
14 Openshaw S. 1992. Some suggestions concerning the development of artificial intelligence tools for spatial modelling and analysis in GIS. The Annals of Regional Science , 26(1): 35-51.
15 Rubinstein A. 1988. Similarity and decision-making under risk (is there a utility theory resolution to the allais paradox?). Journal of Economic Theory , 46(1): 145-153.
16 Sammon J W. 1969. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers , 18(5): 401-409.
17 Shanmuganathan S, P Sallis, and J Buckeridge. 2006. Self-organizing map methods in integrated modelling of environmental and economic systems. Environmental Modelling & Software , 21(9): 1247-1256.
18 Tenenbaum J B. 1998. Mapping a manifold of perceptual observations. Advances in Neural Information Processing Systems , 10: 682- 688.
19 Vesanto J. 1999. SOM-based data visualization methods. Intelligent Data Analysis , 3(2): 111-126.
20 Wezel A, S Bender. 2002. Agricultural land use in the coastal area of the alexander von Humboldt national park, Cuba and its implication for conservation and sustainability. GeoJournal , 57(4): 241-249.
21 Yin H J. 2008. The self-organizing maps: background, theories, extensions and applications. Springer: Berlin Heidelberg, 715-762.

[1]	ZHAO Yuluan, REN Hongyu, LI Xiubin. Forest Transition and Its Driving Forces in the Qian-Gui Karst Mountainous Areas [J]. Journal of Resources and Ecology, 2020, 11(1): 59-68.
[2]	Masoud MASOUDI, Parviz JOKAR. Risk Assessment of Vegetation Degradation Using Geographic Information System: A Case Study of Qareh Aghaj Basin, Iran [J]. Journal of Resources and Ecology, 2018, 9(5): 477-483.
[3]	WANG Mingkuan, MO Hongwei. The Impact of Spatial Heterogeneity on Ecosystem Service Value in a Case Study in Liuyang River Basin, China [J]. Journal of Resources and Ecology, 2018, 9(2): 209-217.
[4]	DONG Guanglong, XU Erqi, ZHANG Hongqi. Urban Expansion and Spatiotemporal Relationships with Driving Factors Revealed by Geographically Weighted Logistic Regression [J]. Journal of Resources and Ecology, 2017, 8(3): 277-286.
[5]	XIE Xue, XIE Hualin, Fan Yuanhua. Spatiotemporal Patterns and Drivers of Forest Change from 1985-2000 in the Beijing-Tianjin-Hebei Region of China [J]. Journal of Resources and Ecology, 2016, 7(4): 301-308.
[6]	LI Qiuying, FANG Chuanglin, LI Guangdong, REN Zhoupeng. Quantitative Measurement of Urban Expansion and Its Driving Factors in Qingdao: An Empirical Analysis Based on County Unit Data [J]. Journal of Resources and Ecology, 2015, 6(3): 172-179.
[7]	PAN Jinghu, LI Junfeng, CONG Yibo. Quantitative Geography Analysis on Spatial Structure of A-grade Tourist Attractions in China [J]. Journal of Resources and Ecology, 2015, 6(1): 12-20.
[8]	YU Haibin, ZHANG Yili, GAO Jungang, QI Wei. Visualizing Patterns of Genetic Landscapes and Species Distribution of Taxus wallichiana (Taxaceae), Based on GIS and Ecological Niche Models [J]. Journal of Resources and Ecology, 2014, 5(3): 193-202.
[9]	ZHENG Defeng, ZHANG Yu, ZANG Zheng, SUN Caizhi. The Driving Forces and Synergistic Effect between Regional Economic Growth, Resources and the Environment in the Yangtze River Economic Zone [J]. Journal of Resources and Ecology, 2014, 5(3): 203-210.
[10]	FAN Jie, HU Wangshu, CHEN Dong, SUN Wei. Aesthetic Perception of Residential Landscapes in Nujiang Lisu Autonomous Prefecture, Yunnan Province [J]. Journal of Resources and Ecology, 2013, 4(2): 157-164.
[11]	QI Xiaopeng, WEI Liang, Laurie BARKER, Akaki LEKIACHVILI, ZHANG Xingyou. Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties [J]. Journal of Resources and Ecology, 2012, 3(3): 220-229.
[12]	LI Xiaowei, FU Guobin, Melanie J. B. ZEPPEL, YU Xiubo, ZHAO Gang, Derek EAMUS, YU Qiang. Probability Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China [J]. Journal of Resources and Ecology, 2012, 3(2): 105-117.
[13]	LUO Shiming. Ecoagriculture in China:A Systems Approach [J]. Journal of Resources and Ecology, 2011, 2(1): 8-14.
[14]	FENG Zhi-Ming, YANG Yan-Zhao, YOU Zhen, ZHAO Yan-De. A GIS-based Study on Sustainable Human Settlements Functional Division in China [J]. Journal of Resources and Ecology, 2010, 1(4): 331-.
[15]	XIA Jun, XUE Jin-Feng. A Distributed Soil Erosion and Sediment Transport Sub-model in Non-point Source Pollution and Its Application in Guishui Watershed [J]. Journal of Resources and Ecology, 2010, 1(3): 231-237.

Competitive Learning Approach to GIS Based Land Use Suitability Analysis

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments