scholarly journals Can we trust earthquake cluster detection tests?

2012 ◽  
Vol 39 (17) ◽  
pp. n/a-n/a ◽  
Author(s):  
F. Dimer de Oliveira
Keyword(s):  
Cluster Detection ◽  
Earthquake Cluster

Spectral analysis for earthquake cluster detection

2012 ◽  
Author(s):  
Deborah Fagan ◽  
Kasper van Wijk ◽  
James Rutledge
Keyword(s):  
Spectral Analysis ◽  
Cluster Detection ◽  
Earthquake Cluster

Statistical methods for spatial cluster detection in childhood cancer incidence: A simulation study

2021 ◽  
Vol 70 ◽  
pp. 101873
Author(s):  
Michael M. Schündeln ◽  
Toni Lange ◽  
Maximilian Knoll ◽  
Claudia Spix ◽  
Hermann Brenner ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Statistical Methods ◽  
Cancer Incidence ◽  
Simulation Study ◽  
Spatial Cluster ◽  
Cluster Detection

scholarly journals sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ian D. Buller ◽  
Derek W. Brown ◽  
Timothy A. Myers ◽  
Rena R. Jones ◽  
Mitchell J. Machiela
Keyword(s):  
Relative Risk ◽  
Statistical Power ◽  
Risk Function ◽  
R Package ◽  
Cancer Epidemiology ◽  
Spatial Cluster ◽  
Cluster Detection ◽  
Detection Technique ◽  
Power Calculation ◽  
Spatial Clusters

Abstract Background Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

Scene segmentation by cluster detection in color spaces

1976 ◽  
pp. 16-17 ◽  
Author(s):  
B. J. Schacter ◽  
L. S. Davis ◽  
A. Rosenfeld
Keyword(s):  
Cluster Detection ◽  
Scene Segmentation ◽  
Color Spaces

Modeling and Anomalous Cluster Detection for Point Processes Using Process Convolutions

2014 ◽  
Vol 23 (1) ◽  
pp. 129-150
Author(s):  
Waley W. J. Liang ◽  
Jacob B. Colvin ◽  
Bruno Sansó ◽  
Herbert K. H. Lee
Keyword(s):  
Point Processes ◽  
Cluster Detection ◽  
Process Convolutions

scholarly journals Developments in disease cluster detection

2006 ◽  
Vol 25 (5) ◽  
pp. 721-721 ◽  
Author(s):  
Andrew Lawson ◽  
Ronald Gangnon ◽  
Dan Wartenberg
Keyword(s):  
Cluster Detection ◽  
Disease Cluster ◽  
Disease Cluster Detection
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