Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2013, 5(9):372-380 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 372 Analysis of GIS-based spatial variability and risk assessment 1 Zhu Qingjie and 2 Beata Hejmanowska 1 School of Petroleum Engineering, Changzhou University, Changzhou, Jiangsu Province, China 2 Department of Geoinformation, Photogrammetry and Remote Sensing of Environment, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland _____________________________________________________________________________________________ ABSTRACT Spatial variability is the focus of geostatistics in GIS analysis. It is an important tool to analyze the spatial data values and their locations. Spatial variability present in the sample data is assessed in terms of distance and direction, and can be described as surfaces. Methods of GIS-based spatial variability calculation and risk assessment are introduced. As an example application, spatial variability’s of four factors for oil production in petroleum engineering are analyzed. With kriging techniques for interpolating surfaces, risk images of four factors are obtained. Order weights based on rank-order approximation and criterion weights based on analytical hierarchy program are calculated. Therefore, the comprehensive risk assessment results are worked out. Finally, the calculating results are analyzed, and some advice is proposed. Key words: Spatial Variability, Risk Assessment, Geostatistics, GIS analysis, IDRISI. _____________________________________________________________________________________________ INTRODUCTION In GIS analysis, spatial variability is the core and focus of Geostatistics (geostatistical analysis) that is a main part of GIS. There are lots of tools to explore the nature of a data set in Geostatistics. Geostatistical techniques have a high degree of flexibility, this maybe produce many surfaces with the same data set. Those surfaces are very different but all represent reality. Therefore, an understanding of these techniques is essential in order to provide meaningful analysis results. For many continuously varying spatial data, close locations are more likely to have similar values than further apart locations. This can be quantified in geography through measures of spatial autocorrelation and variability. As a statistical characterization of spatial sample point data, spatial variability is an important tool to analyze the spatial data values and their locations. Also, it can be combined with some techniques for interpolating surfaces, such as ordinary kriging [1]. Since the 20th century, GIS has been applied in many fields, such as city planning, risk assessment, engineering safety, etc. For example, combine GIS with Geostatistics to analyze the spatial variability [2,3]. Spatial variability analysis is the foundation of risk assessment. Ordinary, there are several influence factors for the risk; therefore, risk assessment is combined with multi-criteria evaluation (MCE). Multi-criteria evaluation based on geographical distributions is called GIS-based multi-criteria evaluation. There are two fundamental classes of multi-criteria evaluation in GIS, Boolean overlay operation and weighted combination. Weighted combination methods include weighted linear combination (WLC) method and ordered weighted averaging (OWA) method that is a relatively new method. Ronald R. Yager introduces OWA operators and quantifier guided aggregation [4,5]. About OWA method,