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| MULTI-ELEMENT SPATIAL DISCRETIZATION METHOD BASED ON FUZZY RELATIONS RECOGNITION——A Case Study of Funing County, Jiangsu Province |
| YU Zhao-yuan, ZONG Zhen, LU Yu-qi, YUAN Lin-wang |
| Geo-Science Department of Nanjing Normal University, Nanjing 210046, China |
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Abstract Traditional social-economic data acquisitions are primarily based on the administrative divisions, and taking into account the expert knowledge to build a new multi-element spatial data affect the discrete method is an important way to enhance the county-level divisions of the main function planning. However, lots of modern main functional zoning planning requires high resolution and precision spatial data for model analysis and decision support. Hence, spatial discretization of social and economic indexes is an important requirement for the main functional zoning planning. Lots of existing methods such as linear regression, kernel smoothing etc. have already been developed. Existing spatial discretization methods are usually affected by the diversity, complexity, and spatial heterogeneity of observed data, which cannot achieve well balance among multiple factor integration, the accuracy and precision and the integration of expert empirical knowledge. To fulfill the gap between the spatial discretization method and the main functional zoning planning needs. This paper presents a multi-element spatial discretization method based on fuzzy relations recognition. Five subsystems, such as land usage, terrain conditions, road traffics, ecological, spatial factors etc. are considered to construct the affection factor index system. The weight of the affect factors are determined by the expert choice with AHP. By defining and applying a generalized weight distance, the expert's knowledge is integrated. Through the establishment of discrete elements to be indicators of the fuzzy relation and by building its influence identification model, we can access to space discretization weights. The results show that the proposed space discretization method has better accuracy and reliability, and can better reveal the spatial impact of the discrete elements and their affect factors.
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Received: 11 June 2011
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2012, Vol. 27 
