Cluster morphology analysis

Jacquez, Geoffrey M.

doi:10.1016/j.sste.2009.08.002

Cited by 28 publications

(30 citation statements)

References 56 publications

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“…More recently, a class of techniques concerned with the shape or morphology of spatial clusters was introduced including: simulated Annealing (Duczmal and Assuncao, 2004), flexible scan (Tango and Takahashi, 2005), a multidirectional optimal ecotope algorithm (AMOEBA) (Aldstadt and Getis, 2006), Greedy Growth Scan (Yiannakoulias et al, 2007), and Cluster Morphology Analysis (CMA) (Jacquez, 2009). These methods are designed to identify contiguous, irregularly shaped clusters of events on the landscape that may often be overestimated in size, and/or shape by other methodologies such as circular scan statistics (Duczmal and Assuncao, 2004;Tango and Takahashi, 2005).…”

Section: Introductionmentioning

confidence: 99%

Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi*) and morphology cluster statistics

et al. 2012

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Abstract. We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle) and small (sheep and goats) domestic ruminants across Kazakhstan. The Getis-Ord (G i *) statistic and a multidirectional optimal ecotope algorithm (AMOEBA) were compared using 1 st , 2 nd and 3 rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and G i * tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149) and for small ruminants (n = 9). In contrast, G i * revealed fewer large ruminant clusters (n = 122) and more small ruminant clusters (n = 61). Significant environmental differences were found between groups using the Kruskall-Wallis and MannWhitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of G i * clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while G i * tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.

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Section: Introductionmentioning

confidence: 99%

Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi*) and morphology cluster statistics

et al. 2012

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“…Firstly, with regard to exploratory analysis, we recommend that researchers utilizing spatial cluster analysis as an exploratory tool consider using multiple tests to gain a greater understanding of the dataset. Recent approaches proposed in spatial epidemiology (Berke, 2005;Jacquez, 2009) focus on using multiple methods as a means to explore every possible avenue of the data to rule out false positives or spurious clusters. Second, researchers should not discount the utility of visualization as a supplement to analysis and enhancement to communication and dissemination of results.…”

Section: Future Research Recommendationsmentioning

confidence: 99%

“…As our test illustrated with the spatial scan statistic scanning windows, how the spatial neighbours are conceptualized can dramatically impact the location and extent of clusters. Recent papers have addressed both of these issues (Jacquez, 2009;Meliker and Sloan, 2011), and called for the use of tools to aid users in selecting appropriate methods and spatial weights. We would like to echo their recommendations in light of the large disparity between methods employed and methods available to the user.…”

Section: Future Research Recommendationsmentioning

confidence: 99%

A scoping review of spatial cluster analysis techniques for point-event data

et al. 2013

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Abstract. Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

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“…home addresses as used by Huang et al (2007), or a set of area centroids, e.g. the county centroids used by Jacquez (2009). Potentially it may even represent a set of continuous geospatial areas.…”

Section: A Framework For Measures Of Spatial Accuracymentioning

confidence: 99%

“…An excellent example is the a, b, c, d notation used by Jacquez (2009), where areal units into one of four types (illustrated in Figure 1 A different, and slightly more succinct, subdivision method is used in papers introducing new versions of the SSS: Huang et al (2007), Jung et al (2007) and Jung et al (2010). This is shown below, the relationship to the notation above given in brackets:…”

Section: Level 3: Sub-regionsmentioning

confidence: 99%

Measuring the spatial accuracy of the spatial scan statistic

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Willett

et al. 2011

Spatial and Spatio-temporal Epidemiology

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The spatial scan statistic is well established in spatial epidemiology. However, studies of its spatial accuracy are infrequent and vary in approach, often using multiple measures which complicate the objective ranking of different implementations of the statistic. We address this with three novel contributions. Firstly, a modular framework into which different definitions of spatial accuracy can be compared and hybridised. Secondly, we derive a new single measure, Ω, which takes account of all true and detected clusters, without the need for arbitrary weightings and irrespective of any chosen significance threshold. Thirdly, we demonstrate the new measure, alongside existing ones, in a study of the six output filter options provided by SaTScan TM . The study suggests filtering overlapping detected clusters tends to reduce spatial accuracy, and visualising overlapping clusters may be better than filtering them out. Although we only address spatial accuracy, the framework and Ω may be extendible to spatio-temporal accuracy.

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Cluster morphology analysis

Cited by 28 publications

References 56 publications

Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi*) and morphology cluster statistics

Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi*) and morphology cluster statistics

A scoping review of spatial cluster analysis techniques for point-event data

Measuring the spatial accuracy of the spatial scan statistic

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