Space-time visualization and analysis in the Cancer Atlas Viewer

Abstract

This article describes the Cancer Atlas Viewer: free, downloadable software for the exploration of United States cancer mortality data. We demonstrate the software by exploring spatio-temporal patterns in colon cancer mortality rates for African-American and white females and males in the southeastern United States over the period 1970-1995. We compare the results of two cluster statistics: the local Moran and the local G*, through time.. Overall, the two statistics reach similar conclusions for most locations, although where they disagree reveals some interesting patterns in the data. There are only two persistent clusters of colon cancer mortality, and these are clusters of low values.

Cluster morphology analysis

“…They may detect local clusters that exist despite negative tests for global spatial autocorrelation. In a study of colon cancer in counties of the south-eastern US (Greiling et al 2005) the local G was found to give similar, but not identical results to the local Moran.…”

“…This static view hinders the mapping, representation, and analysis of dynamic health, socioeconomic, and environmental information for populations that are dispersed and mobile. Recent technological advances have resulted in Space Time Intelligence Systems (STIS) that implement constructs for representing temporal change (Avruskin et al 2004; Greiling et al 2005; Jacquez et al 2005; Meliker et al 2005). The STIS technology has the following advantages.…”

Geostatistical Analysis of County‐Level Lung Cancer Mortality Rates in the Southeastern United States. 美国东南部县级肺癌死亡率的地统计学分析

“…It exceeds zero if the kernel and neighborhood averaged rates jointly exceed the global mean m (High–High, HH cluster) or are jointly below m (Low–Low, LL cluster). Despite its widespread use, this statistic suffers from several limitations, such as the arbitrary use of the global mean to detect local clusters of low or high values, the lack of power compared to other clustering tests (Song and Kulldorff 2003), and the use of predefined neighborhoods like first- or second-order adjacencies, which makes it less sensitive to the detection of clusters of different shapes or that occur at different spatial scales (Greiling et al 2005). …”

“…Geographic Information Systems (GISs) are used increasingly for cancer control activities and resource allocation. Cancer atlases are now published by national and state health agencies and have proved useful for quantifying patterns in cancer rates such as incidence and mortality, documenting access to health care, providing tools for risk communication, and assessing disparities in cancer burdens in underserved populations (Devesa et al 1999; Pickle et al 1999; Greiling et al 2005). The major difficulty in the analysis of health outcomes is that the patterns observed reflect the influence of a complex combination of demographic, social, economic, cultural, and environmental factors that are likely to change through time and space, and that interact with the different types and scales of places where people live (Tunstall, Shaw, and Dorling 2004).…”

The emerging role and benefits of boundary analysis in spatio-temporal epidemiology and public health

“…Cancers, cardiovascular disease and other chronic diseases can differ dramatically in incidence and mortality at national, regional and local levels (Jemal, Kulldorff et al 2002; Fang, Kulldorff et al 2004; Pickle 2009; Greiling, Jacquez et al 2010). Across contiguous areas change in disease incidence and mortality may be absent, gradual or abrupt, with a good amount of geographic variability explicable wholly or in part by underlying changes in covariates and risk factors that are themselves spatially structured.…”