Alcohol mortality: a comparison of spatial clustering methods

Hanson, Craig E; Wieczorek, William F.

doi:10.1016/s0277-9536(01)00203-9

Cited by 49 publications

(36 citation statements)

References 16 publications

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“…The results suggested that the Getis-Ord statistics are better at highlighting areas of interest for further investigation, in part, because the nearest neighbor technique in STAC tests for clustering at a single scale. Finally, Hanson and Wieczorek (2002) explored the differences and potential complementarities between LISA and Kulldorff's scan for detecting clusters of alcohol mortality in New York State. Results suggested that the LISA findings are highly dependent on the spatial weights matrix selected for analysis.…”

Section: Hybrid Comparative Workmentioning

confidence: 99%

“…Results suggested that the LISA findings are highly dependent on the spatial weights matrix selected for analysis. In their case study, LISA were quite effective at differentiating between hotspots of two or more counties, but the Kulldorff's scan was better at identifying clusters made up of only one county (Hanson and Wieczorek 2002). In part, this can be attributed to the spatial scan using the case-to-population ratio for measuring elevated risk.…”

Section: Hybrid Comparative Workmentioning

confidence: 99%

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Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense

Grubesic

Wei

Murray

2014

Annals of the Association of American Geographers

106

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Cluster analysis continues to be an important exploratory technique in scientific inquiry. It is used widely in geography, public health, criminology, ecology, and many other fields. Spatial cluster detection is driven by geographic information corresponding to the location of activities, requiring appropriate and meaningful treatment of space and spatial relationships combined with observed attributes of location and events. To date, this has meant utilizing dedicated measures and techniques to structure and account for distance, neighbors, contiguity, irregular geographic morphology, and so on. Unfortunately, all spatial clustering approaches, regardless of their theoretical underpinning, statistical foundation, or mathematical specification, have limitations in accuracy, sensitivity, and the computational effort required for identifying clusters. As a result, a major challenge in practice is determining which technique(s) will provide the most meaningful insights for a particular substantive issue or planning context. The purpose of this article is to provide an overview and evaluation of spatial clustering techniques, identifying the strengths and weaknesses of the most widely applied approaches. Results suggest that performance varies significantly in terms of accuracy, sensitivity, and computational expense. This is noteworthy because the misidentification of clusters, whether false positives or false negatives, has the potential to bias not only hypothesis formulation but also pragmatic facets of policy, process, and planning efforts within a region. Key Words: cluster analysis, hot spots, knowledge discovery, method selection, scale.El an alisis de conglomerados [an alisis de clusters] sigue siendo una importante t ecnica exploratoria en investigaci on cient ıfica. Se le emplea ampliamente en geograf ıa, salud p ublica, criminolog ıa, ecolog ıa y en muchos otros campos. La detecci on de la aglomeraci on espacial est a controlada por la informaci on geogr afica correspondiente a la localizaci on de las actividades, lo cual requiere un tratamiento apropiado y significativo del espacio y de las relaciones espaciales combinadas con atributos observados de localizaci on y eventos. Hasta el momento presente, esto implica la utilizaci on de mediciones y t ecnicas especiales para estructurar y tomar en cuenta cosas como distancia, vecinos, contig€ uidad, morfolog ıa geogr afica irregular y dem as. Infortunadamente, todos los enfoques sobre aglomeraciones espaciales, sin consideraci on a sus bases te oricas, fundamentaci on estad ıstica, o especificaci on matem atica, adolecen de limitaciones en exactitud, sensibilidad y en el esfuerzo computacional que se necesita para identificar los conglomerados. Como resultado, en la pr actica un reto mayor es determinar qu e t ecnica o t ecnicas rendir an m as para un asunto sustantivo particular o un contexto de planificaci on. El prop osito de este art ıculo es proporcionar una mirada de conjunto y evaluaci on de t ecnicas de aglomeraci on espacial, identificando las forta...

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Section: Hybrid Comparative Workmentioning

confidence: 99%

Section: Hybrid Comparative Workmentioning

confidence: 99%

Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense

Grubesic

Wei

Murray

2014

Annals of the Association of American Geographers

106

View full text Add to dashboard Cite

show abstract

“…Esses mapas possibilitaram a identificação de conglomerados de áreas com altas taxas do indicador e vizinhos com taxas relativamente elevadas (alto-alto) e áreas com taxas baixas do indicador e vizinhos com taxas relativamente baixas (baixo-baixo); além de áreas de transição, com taxas baixas e vizinhos com taxas altas (baixo-alto) ou taxas altas e vizinhos com taxas baixas (alto-baixo) 26,28 .…”

Section: Análise Dos Dadosunclassified

Indicadores de saúde materno infantil em Belo Horizonte, Minas Gerais, Brasil, 2001: análise dos diferenciais intra-urbanos

et al. 2006

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A análise espacial de indicadores de saúde tem sido um importante instrumento na detecção de diferenciais intra-urbanos. O estudo objetivou traçar um perfil dos nascimentos em Belo Horizonte, Minas Gerais, Brasil, em 2001, analisando a presença de conglomerados espaciais de indicadores de saúde do recém-nascido e suas mães, a partir de dados do Sistema de Informações sobre Nascidos Vivos. Para cada área de abrangência das Unidades Básicas de Saúde, foram calculadas as proporções desses indicadores, utilizando-se o método Bayesiano empírico. Para análise espacial, foram utilizados os índices de Moran global e local (LISA). Áreas com índices de autocorrelação espacial significantes (p < 0,05) foram visualizadas através de mapas. Foram encontrados conglomerados de áreas com índices de autocorrelação espacial significativos para os indicadores adolescentes, menos de oito anos de estudo, filhos mortos em gestações anteriores, cesárea e menos de quatro consultas no pré-natal, guardando relação com as características sócio-demográficas das áreas. A metodologia utilizada configurou-se como um ótimo instrumento de detecção de conglomerados de áreas de risco à saúde materno infantil, podendo facilmente ser incorporada como mecanismo de monitoramento dos eventos relacionados aos nascimentos em Belo Horizonte.

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“…38 SaTScan cluster detection has been published widely in public health literature, but, the utilization of space-time scan statistics in epidemiologic research on drug abuse has been limited. [39][40][41] SaTScan has been reported to demonstrate more precision in identifying outliers (e.g., local clusters that have small geographic or population sizes) as compared to other methods. 42 Briefly, the SaTScan statistical technique detects spatial clusters of areal units by imposing an infinite number of overlapping circular (or elliptical) scanning windows of various sizes across a defined geographic area.…”

Section: Space-time Cluster Detectionmentioning

confidence: 99%

Application of Space-Time Scan Statistics to Describe Geographic and Temporal Clustering of Visible Drug Activity

et al. 2014

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Knowledge of the geographic and temporal clustering of drug activity can inform where health and social services are needed and can provide insight on the potential impact of local policies on drug activity. This ecologic study assessed the spatial and temporal distribution of drug activity in Baltimore, Maryland, prior to and following the implementation of a large urban redevelopment project in East Baltimore, which began in 2003. Drug activity was measured by narcotic calls for service at the neighborhood level. A space-time scan statistic approach was used to identify statistically significant clusters of narcotic calls for service across space and time, using a discrete Poisson model. After adjusting for economic deprivation and housing vacancy, clusters of narcotic calls for service were identified among neighborhoods located in Southeast, Northeast, Northwest, and West Baltimore from 2001 to 2010. Clusters of narcotic calls for service were identified among neighborhoods located in East Baltimore from 2001 to 2003, indicating a decrease in narcotic calls thereafter. A large proportion of clusters occurred among neighborhoods located in North and Northeast Baltimore after 2003, which indicated a potential spike during this time frame. These findings suggest potential displacement of drug activity coinciding with the initiation of urban redevelopment in East Baltimore. Space-time scan statistics should be used in future research to describe the potential implications of local policies on drug activity.

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Alcohol mortality: a comparison of spatial clustering methods

Cited by 49 publications

References 16 publications

Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense

Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense

Indicadores de saúde materno infantil em Belo Horizonte, Minas Gerais, Brasil, 2001: análise dos diferenciais intra-urbanos

Application of Space-Time Scan Statistics to Describe Geographic and Temporal Clustering of Visible Drug Activity

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