Spatio-Temporal Cluster Modelling of Small Area Health Data

Clark, Allan; Lawson, AB

doi:10.1201/9781420035414.ch14

Cited by 15 publications

(11 citation statements)

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“…Note that we generally call any cluster over space and time a spatiotemporal cluster, so that Clusters 1, 2, and 3 in Figure 3 are special cases of spatio-temporal clusters. Our deÿnition is di erent from that in Clark and Lawson [19], where they classify Clusters 1, 2 and 3, as a temporal cluster, a spatial cluster, and a spatio-temporal cluster respectively, because Cluster 1 has an elevated risk for a period of time throughout the whole spatial study region, Cluster 2 has an elevated risk in a portion of space throughout the whole time period, and Cluster 3 covers a portion of space and persists over a period of time. In the following, we also use the term 'three-dimensional (3-D) temporal clusters' for clusters like Cluster 1 and '3-D spatial clusters' for clusters like Cluster 2.…”

Section: Spatio-temporal Clustermentioning

confidence: 89%

A cluster model for space–time disease counts

Yan

Clayton

2006

Statistics in Medicine

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Modelling disease clustering over space and time can be helpful in providing indications of possible exposures and planning corresponding public health practices. Though a considerable number of studies focus on modelling spatio-temporal patterns of disease, most of them do not directly model a spatio-temporal clustering structure and could be ineffective for detecting clusters. In this paper, we extend a purely spatial cluster model to accommodate space-time clustering. Inference is performed in a Bayesian framework using reversible jump Markov chain Monte Carlo. This idea is illustrated using data on female breast cancer mortality from Japan. A hierarchical parametric space-time model for mapping disease is used for comparison.

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Section: Spatio-temporal Clustermentioning

confidence: 89%

A cluster model for space–time disease counts

Yan

Clayton

2006

Statistics in Medicine

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“…If we choose the second one, we can aggregate the dataset by state, county, ward, census block, etc. [16].…”

Section: A Datasetmentioning

confidence: 95%

Triangular kernel nearest neighbor based clustering for pattern extraction in spatio-temporal database

Musdholifah

Hashim

2010

2010 10th International Conference on Intelligent Systems Design and Applications

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To date, various fields of applications have utilized spatio-temporal databases not only to store data, but to support decision making. For example, in traffic accident analysis; it is required to have knowledge on the pattern of accidents resulting in death. Thus, in such analysis, clustering technique is desired to implement pattern extraction. This paper presents clustering of spatio-temporal database using kernel nearest neighbor approach. It is chosen due to its ability to determine the number of clusters automatically. There are various types of kernel functions exist in the literatures, but the issue of concern is how to determine an appropriate kernel function for this application. In this study, two commonly used kernel functions, namely Gaussian and triangular, are investigated. From various experiments conducted, both functions produce reasonable clusters, but the triangular kernel nearest neighbor based clustering (TKNN) provides better performance with smaller number of iteration compared to Gaussian kernel nearest neighbor based clustering (ILGC) and K-means. Thus, TKNN is good option in clustering spatio-temporal database.

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“…We adopt the Clark and Lawson (2002) definition of spatio-temporal clusters, in the sense of persistence of clusters over space and time. We have used the 88 counties of the state of Ohio, USA, for a period of 21 years (1968–1988) to illustrate the method, as a base for simulation.…”

Section: Simulation Design: Ohio County Geographiesmentioning

confidence: 99%

Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection

Hossain

Lawson

2008

Environ Ecol Stat

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This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model's ability in relation to cluster detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. The proposed criteria are based on posterior estimates (e.g., misclassification rate) and some are based on post-hoc analysis of posterior samples (e.g., exceedance probability). In addition, we examine more conventional model fit criteria including mean square error (MSE). We illustrate the methodology with a real ST dataset, Georgia throat cancer mortality data for the years 1994-2005, and a simulated dataset where different levels and shapes of clusters are embedded. Overall, it is found that conventional SREST models fair well in ST cluster detection and in goodness-of-fit, while for extreme risk detection the local likelihood ST model does best.

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Spatio-Temporal Cluster Modelling of Small Area Health Data

Cited by 15 publications

References 0 publications

A cluster model for space–time disease counts

A cluster model for space–time disease counts

Triangular kernel nearest neighbor based clustering for pattern extraction in spatio-temporal database

Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection

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