Spatial cluster analysis using particle swarm optimization and dispersion function

Abstract

Spatial patterns studies are of great interest to the scientific community and the spatial scan statistic is a widely used technique to analyze such patterns. A key point for the construction of methods for detection of irregularly shaped clusters is that, as the geometrical shape has more degrees of freedom, some correction should be employed in order to compensate the increased flexibility. This paper proposed a multi-objective approach to cluster detection problem using the Particle Swarm Optimization technique aggregating a novel penalty function, called dispersion function, allowing only clusters which are subsets of a circular zone of moderate size. Compared to other regularity functions, the multi-objective scan with the dispersion function is faster and suited for the detection of moderately irregularly shaped clusters. An application is presented using state-wide data for Chagas’ disease in puerperal women in Minas Gerais state, Brazil.

Keywords:

• Dispersion function
• spatial scan statistic
• Arbitrarily shaped cluster
• Multi-objective optimization

Acknowledgments

The authors thank the anonymous referees and editors for their thoughtful comments.

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.