Analyzing Domestic Violence with Topographic Maps: A Comparative Study

Authors:
Jonas Poelmans;Paul Elzinga;Stijn Viaene;Guido Dedene;Marc M. Hulle
Affiliations:
K.U.Leuven, Faculty of Business and Economics, Leuven, Belgium 3000;Police Amsterdam-Amstelland, Amsterdam, The Netherlands 1000 CG;K.U.Leuven, Faculty of Business and Economics, Leuven, Belgium 3000 and Vlerick Leuven Gent Management School, Leuven, Belgium 3000;K.U.Leuven, Faculty of Business and Economics, Leuven, Belgium 3000 and Universiteit van Amsterdam Business School, Amsterdam, The Netherlands 1018 WB;K.U.Leuven, Laboratorium voor Neuro- en Psychofysiologie, Leuven, Belgium 3000
Venue:
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Year:
2009

Citing 2
Cited 0

Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization

Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the recent years, several variations on the Self Organizing Maps (SOM) were introduced in the literature. In this paper, the toroidal Emergent SOM tool and the spherical SOM are used to analyze a text corpus consisting of police reports of all violent incidents that occurred during the first quarter of 2006 in the police region Amsterdam-Amstelland (The Netherlands). It is demonstrated that spherical topographic maps provide a powerful instrument for analyzing this dataset. In addition, the performance of the toroidal Emergent SOM is compared to that of the spherical SOM, and it turned out to be superior to that of an ordinary classifier, applied directly to the data.