Spatial Cluster Detection Through a Dynamic Programming Approach

Gladston J. P. Moreira (), Luís Paquete (), Luiz H. Duczmal (), David Menotti () and Ricardo H. C. Takahashi ()
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Gladston J. P. Moreira: Universidade Federal de Ouro Preto, Department of Computing
Luís Paquete: University of Coimbra, CISUC, Department of Informatics Engineering
Luiz H. Duczmal: Universidade Federal de Minas Gerais, Campus Pampulha, Department of Statistics
David Menotti: Universidade Federal do Paraná, Department of Informatics
Ricardo H. C. Takahashi: Universidade Federal de Minas Gerais, Department of Mathematics

Chapter 30 in Handbook of Scan Statistics, 2024, pp 595-608 from Springer

Abstract: Abstract This chapter reviews a dynamic programming scan approach to the detection and inference of arbitrarily shaped spatial clusters in aggregated geographical area maps, which is formulated here as a classic knapsack problem. A polynomial algorithm based on constrained dynamic programming is proposed, the spatial clusters detection dynamic scan. It minimizes a bi-objective vector function, finding a collection of Pareto optimal solutions. The dynamic programming algorithm is adapted to consider geographical proximity between areas, thus allowing a disconnected subset of aggregated areas to be included in the efficient solutions set. It is shown that the collection of efficient solutions generated by this approach contains all the solutions maximizing the spatial scan statistic. The plurality of the efficient solutions set is potentially useful to analyze variations of the most likely cluster and to investigate covariates.

Keywords: Spatial scan statistic; Irregular clusters; Dynamic programming; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-1-4614-8033-4_40

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