A Statistical Toolbox For Mining And Modeling Spatial Data

Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis

Application of data mining techniques using SAS software

Data mining has captured the hearts and minds of business analysts seeking a solution forexploring and modeling vastly larger, more complex and less well-behaved datasets. Exploratorydata analysis, typically consisting of activities like statistical visualization, hypothesis generation,and introductory model fitting is a vital first step in any successful data mining venture.Exploratory data analysis produces direct benefits for data miners in enhanced understanding ofdata, improved clarity and confidence of the modeling results, and avoidance of pitfalls early inthe process. By using data mining techniques to analyze the data that is accumulating and fillingvast data warehouses, organizations can harness more insight from their large data stores to driveproactive decision making. SAS data mining software can surface patterns and trends in yourdata that you may never have thought to look for. This paper will review the usefulness of SAS Tsoftware for exploratory data analysis, interactive regression modeling, and advancedmultidimensional data visualizatio