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International Journal of Health Geographics

, 7:57

First Online: 07 November 2008Received: 29 July 2008Accepted: 07 November 2008DOI: 10.1186-1476-072X-7-57

Cite this article as: Chen, J., Roth, R.E., Naito, A.T. et al. Int J Health Geogr 2008 7: 57. doi:10.1186-1476-072X-7-57

Abstract

BackgroundKulldorff-s spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: 1 the method lacks cartographic support for understanding the clusters in geographic context and 2 results from the method are sensitive to parameter choices related to cluster scaling abbreviated as scaling parameters, but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S.

ResultsWe address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: 1 the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and 2 the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset e.g., U.S. data aggregated by county, demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results.

ConclusionThe geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales.

MethodWe analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.

AbbreviationsFIPS codeFederal Information Processing Standards code

HPVhuman papillomavirus

LLRlogarithm of the likelihood ratio

maximum-sizethe maximum radius size of a scan circle

Popthe population at risk of a cluster

Pct NHthe percentage of the counties in a cluster that are not in high risk

Pct Popthe percentage of the total population at risk

SaTScanKulldorff-s spatial scan statistic and its software implementation

SMRStandardized Mortality Ratio

VITVisual Inquiry Toolkit.

Electronic supplementary materialThe online version of this article doi:10.1186-1476-072X-7-57 contains supplementary material, which is available to authorized users.

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Author: Jin Chen - Robert E Roth - Adam T Naito - Eugene J Lengerich - Alan M MacEachren

Source: https://link.springer.com/



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