Spatial cluster detection in mobility networks: a copula approach

2018 ◽  
Vol 68 (1) ◽  
pp. 99-120 ◽  
Author(s):  
Heeyoung Kim ◽  
Rong Duan ◽  
Sungil Kim ◽  
Jaehwan Lee ◽  
Guang‐Qin Ma
Keyword(s):  
Spatial Cluster ◽  
Cluster Detection ◽  
Copula Approach

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.

Multiple source spatial cluster detection via multi-criteria analysis

2018 ◽  
Vol 25 (2) ◽  
pp. 257-275
Author(s):  
Alexandre C. L. Almeida ◽  
Luiz H. Duczmal ◽  
André L. F. Cançado ◽  
Fabio R. da Silva
Keyword(s):  
Spatial Cluster ◽  
Cluster Detection ◽  
Multiple Source ◽  
Multi Criteria Analysis

PSCluster: Differentially Private Spatial Cluster Detection for Mobile Crowdsourcing Applications

2018 ◽  
Author(s):  
Boyang Hu ◽  
Baojian Zhou ◽  
Qiben Yan ◽  
Adil Alim ◽  
Feng Chen ◽  
...  
Keyword(s):  
Spatial Cluster ◽  
Cluster Detection ◽  
Mobile Crowdsourcing ◽  
Crowdsourcing Applications

scholarly journals Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History

2009 ◽  
Vol 51 (5) ◽  
pp. 801-818 ◽  
Author(s):  
Andrea J. Cook ◽  
Diane R. Gold ◽  
Yi Li
Keyword(s):  
Spatial Cluster ◽  
Cluster Detection ◽  
Residential History

Multi-objective dynamic programming for spatial cluster detection

2014 ◽  
Vol 22 (2) ◽  
pp. 369-391 ◽  
Author(s):  
Gladston J. P. Moreira ◽  
Luís Paquete ◽  
Luiz H. Duczmal ◽  
David Menotti ◽  
Ricardo H. C. Takahashi
Keyword(s):  
Dynamic Programming ◽  
Spatial Cluster ◽  
Cluster Detection ◽  
Multi Objective

scholarly journals Arbitrarily shaped multiple spatial cluster detection for case event data

2007 ◽  
Vol 51 (8) ◽  
pp. 3931-3945 ◽  
Author(s):  
Christophe Dematteı¨ ◽  
Nicolas Molinari ◽  
Jean-Pierre Daurès
Keyword(s):  
Spatial Cluster ◽  
Cluster Detection ◽  
Event Data

Spatial cluster detection using nearest neighbor distance

2015 ◽  
Vol 14 ◽  
pp. 400-411 ◽  
Author(s):  
Avner Bar-Hen ◽  
Mathieu Emily ◽  
Nicolas Picard
Keyword(s):  
Nearest Neighbor ◽  
Spatial Cluster ◽  
Cluster Detection ◽  
Neighbor Distance ◽  
Nearest Neighbor Distance
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