Int J Health Geogr - A binary-based approach for detecting irregularly shaped clusters.

Tópicos

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Resumo

CKGROUND: There are many applications for spatial cluster detection and more detection methods have been proposed in recent years. Most cluster detection methods are efficient in detecting circular (or circular-like) clusters, but the methods which can detect irregular-shaped clusters usually require a lot of computing time.METHODS: We propose a new spatial detection algorithm for lattice data. The proposed method can be separated into two stages: the first stage determines the significant cells with unusual occurrences (i.e., individual clustering) by applying the Choynowski's test, and the second stage determines if there are clusters based on the information of the first stage by a binomial approximate method. We first use computer simulation to evaluate the performance of the proposed method and compare it with the scan statistics. Furthermore, we take the Taiwan Cancer data in 2000 to illustrate the detection results of the scan statistics and the proposed method.RESULTS: The simulation results support using the proposed method when the population sizes are large and the study regions are irregular. However, in general, the scan statistics still have better power in detecting clusters, especially when the population sizes are not large. For the analysis of cancer data, the scan statistics tend to spot more clusters, and the clusters' shapes are close to circular (or elliptic). On the other hand, the proposed methods only find one cluster and cannot detect small-sized clusters.CONCLUSIONS: In brief, the proposed methods can detect both circular and non-circular clusters well when the significant cells are correctly detected by the Choynowski's method. In addition, the binomial-based method can handle the problem of multiple testing and save the computing time. On the other hand, both the circular and elliptical scan statistics have good power in detecting clusters, but tend to detect more clusters and have lower accuracy in detecting non-circular clusters.

Resumo Limpo

ckground mani applic spatial cluster detect detect method propos recent year cluster detect method effici detect circular circularlik cluster method can detect irregularshap cluster usual requir lot comput timemethod propos new spatial detect algorithm lattic data propos method can separ two stage first stage determin signific cell unusu occurr ie individu cluster appli choynowski test second stage determin cluster base inform first stage binomi approxim method first use comput simul evalu perform propos method compar scan statist furthermor take taiwan cancer data illustr detect result scan statist propos methodresult simul result support use propos method popul size larg studi region irregular howev general scan statist still better power detect cluster especi popul size larg analysi cancer data scan statist tend spot cluster cluster shape close circular ellipt hand propos method find one cluster detect smallsiz clustersconclus brief propos method can detect circular noncircular cluster well signific cell correct detect choynowski method addit binomialbas method can handl problem multipl test save comput time hand circular ellipt scan statist good power detect cluster tend detect cluster lower accuraci detect noncircular cluster

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