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Title: Methods for the improved implementation of the spatial scan statistic when applied to binary labelled point data
Author: Read, Simon
ISNI:       0000 0004 2719 9187
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2011
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This thesis investigates means of improving, applying, and measuring the success of, the Spatial Scan Statistic (SSS) when applied to binary labelled point data (BLPD). As the SSS is an established means of detecting anomalies in spatial data (also known as clusters), this work has potential application in many fields, notably epidemiology. Firstly, the thesis considers the capacity of the SSS to correctly identify the presence of anomalies, irrespective of location. The most important contribution is the identification that p-values produced by the standard algorithm for implementing of the SSS are sometimes conservative, and thus may lead to lower-than-expected statistical power. A novel means of rectifying this is presented, along with a study of how this can be used in conjunction with an existing technique (Gumbel smoothing) for reducing the computational expense of the SSS. A novel version of the SSS for BLPD is also derived and tested, together with an alternative algorithm for selecting circular scan windows. Secondly, the thesis considers the capacity of the SSS to correctly identify the location of anomalies. This is an under-researched area, and this work is relevant to all forms of data to which the SSS is applied, not just BLPD. A synthesis of current research is presented as a five level framework, facilitating the comparison and hy- bridisation of existing spatial accuracy measures for the SSS. Two novel measures of spatial accuracy ('l!, 0) are derived, both compatible with this framework. 'l! works in conjunction with power; D is independent of power. Both use a single parameter to encapsulate complex information about spatial accuracy performance. This pre- viously required two or more parameters, or an arbitrarily weighted combination of two or more parameters. All novel techniques are benchmark tested against established software, and the statistical significance of performance improvements is measured.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available