This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 428
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #308930
Title: Using GAM_K to Detect Clustering in MI Wood Products Companies
Author(s): Juan Du*+
Companies: Kansas State University
Address: 108D Dickens Hall, the Department of Statistics, Manhattan, KS, 66506-0802,
Keywords: Geographic analysis machine algorithm (GAM) ; Kernel method ; Poisson process ; Spatial clustering
Abstract:

The area-based geographic analysis machine algorithm along with kernel method (GAM_K) is proposed and compared with case-based GAM_K [Openshaw (1996)] to identify the presence of spatial clustering and the location of clusters in wood products companies in Northern Lower Peninsula of Michigan. Some rules of thumbs are provided for some factor determinations in the case-based GAM_K when a heterogeneous underlying population is accounted for. However the area-based GAM_K is preferable if the underlying spatial variation can be ignored. It will produce results better than those from pure kernel method by indicating statistical test, significance level and clustering location scales. The implementation and programming of this algorithm are illustrated by using R.


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