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Knowledge discovery from GIS in 'Natural Resources Targeting'
Nihar R. Sahoo Tata Infotech Limited, Noida Email: nihar.sahoo@tatainfotech.com Shishir K. Mahapatra Exploration Business Analyst, Tata Petrodyne Limited New Delhi. Email: shishir_m@hotmail.com Abstract With the advent of GIS Technology and its tremendous capability of handling complex spatial data, there has been ample of opportunities for an explicitly reasoned evaluation for decision making, however the extraction and comprehension of the knowledge implied by this huge amount of spatial data, poses a great challenge. Often hard real-world problems, as to their larger size, formidable structure, complex dependencies, and with a definite objective, escape classical optimization techniques. The traditional approach of reducing size of this problem attempted few ways such as: removing less significant parameters, observations, constraint, ignores analysis of relevant constraints and uncertainties in the dataset. This activity of knowledge discovery requires a thorough analysis for extraction of implicit knowledge, spatial relations, patterns and nugget effect in spatial datasets. Further the possibly obvious data inadequacy and procedure of assigning credible weights to input data need to be analyzed. This paper describes the process of knowledge discovery in exploring pattern or nature of data from Remote Sensing and GIS, and integration of data-layers in targeting natural resources. Potential application of logistic regression analysis in resource targeting has been described here. The capability of this tool in handling varied data-types, data-driven approach of factor-weighting and studying interactions of the evidences has been explained. Knowledge Discovery for Resources Assessment Studies The collation of data about the spatial distribution of several properties of the earth's surface has long been an important activity in resources assessment. It has become a complicated process to describe the spatial variations quantitatively due to reasons like, sheer data volume, inherent imprecision of geographic data, general problem of quantification of certain spatial processes, events, missing observations, nugget effect, lack of appropriate analysis tool and a few others (Cliff and Ord, 1981). A set of functionality available in a traditional GIS is not exhaustive enough to handle such a real world problem. Further the analysis tool in a GIS or RS-Image Processing System has not received much attention in commercial applications. Extraction of even a simple spatial relation requires complex spatial analysis tool (Fotheringham and Rogerson, 1994). Statistics is of much use since a decade in handling spatial data, recognition of pattern, integration and analysis. However, statistical analysis usually requires the assumption of statistical independence of spatially distributed data. Such assumptions are often unrealistic due to the influence of spatial effects. Geostatistical models with spatially lagged forms of the independent variables can be used to alleviate the problem to a greater extent. The process of knowledge discovery from spatial data requires better interaction with user and discover hidden knowledge faster (Riply, 1981).  Fig 1
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