Finding regional co-location patterns for sets of continuous variables in spatial datasets

This paper proposes a novel framework for mining regional colocation patterns with respect to sets of continuous variables in spatial datasets. The goal is to identify regions in which multiple continuous variables with values from the wings of their statistical distribution are co-located. A co-location mining framework is introduced that operates in the continuous domain without the need for discretization and which views regional co-location mining as a clustering problem in which an externally given fitness function has to be maximized. Interestingness of co-location patterns is assessed using products of z-scores of the relevant continuous variables. The proposed framework is evaluated by a domain expert in a case study that analyzes Arsenic contamination in Texas water wells centering on regional co-location patterns. Our approach is able to identify known and unknown regional colocation patterns, and different sets of algorithm parameters lead to the characterization of Arsenic...

• Type: Conference
• Year: 2008
• Where: GIS
• Authors: Christoph F. Eick, Jean-Philippe Nicot, Rachana Parmar, Tomasz F. Stepinski, Wei Ding 0003
• Keywords: Co-location Mining Framework, Database, GIS 2008, Regional Co-location Mining, Regional Co-location Patterns