Thesis (M. Phil.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 43-45).

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Operations Research." Includes bibliographical references (p. 117-122).

Thesis (Ph.D.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on July 2, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computing Science, University of Alberta. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (M.Sc.)--York University, 2005. Graduate Programme in Mathematics and Statistics. / Typescript. Includes bibliographical references (leaves 61-62). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss &rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11874

Thesis (Ph. D.)--University of Washington, 1987. / Vita. Bibliography: leaves [192]-194.

Spatial datasets contain information relating to the locations of incidents of phenomena for example, crime and disease. Areas that contain a higher than expected incidence of the phenomena, given background population and census datasets, are of particular interest. By analysing the locations of potential influence, it may be possible to establish where a cause and effect relationship is present in the observed process. Cluster detection techniques can be applied to such datasets in order to reveal information relating to the spatial distribution of the cases. Research in these areas has mainly concentrated on either computational or statistical aspects of cluster detection. Each clustering algorithm has its own strengths and weakness. Their main weaknesses causing their unreliability can be estimating the number of clusters, testing the number of components, selecting initial seeds (centroids), running time and memory requirements. Consequently, a new cluster detection methodology has been developed in this thesis based on knowledge drawn from both statistical and computing domains. This methodology is based on a hybrid of statistical methods using properties of probability rather than distance to associate data with clusters. No previous knowledge of the dataset is required and the number of clusters is not predetermined. It performs efficiently in terms of memory requirements, running time and cluster quality. The algorithm for determining both the centre of clusters and the existence of the clusters themselves was applied and tested on simulated and real datasets. The results which were obtained from identification of hotspots were compared with results of other available algorithms such as CLAP (Cluster Location Analysis Procedure), Satscan and GAM (Geographical Analysis Machine). The outputs are very similar. XVI GIS presented in this thesis encompasses the SCS algorithm, statistics and neural networks for developing a hybrid predictive crime model, mapping, visualizing crime data and the corresponding population in the study region, visualizing the location of obtained clusters and burglary incidence concentration ‘hotspots’ which was specified by clustering algorithm SCS. Naturally the quality of results is subject to the accuracy of the used data. GIS is used in this thesis for developing a methodology for modelling data containing multiple functions. The census data used throughout this construction provided a useful source of geo-demographic information. The obtained datasets were used for predictive crime modelling. This thesis has benefited from several existing methodologies to develop a hybrid modelling approach. The methodology was applied to real data on burglary incidence distribution in the study region. Relevant principles of statistics, Geographical Information System, Neural Networks and SCS algorithm were utilized for the analysis of observed data. Regression analysis was used for building a predictive crime model and combined with Neural Networks with the aim of developing a new hierarchical neural Network approaches to generate a more reliable prediction. The promising results were compared with the non-hierarchical neural Network back-propagation network and multiple regression analysis. The average percentage accuracy achieved by the new methodology at testing stage increase 13% compared with the non-hierarchical BP performance. In general the analysis reveals a number of predictors that increase the risk of burglary in the study region. Specifically living in a household in which there is ‘one person’, ‘lone parent’, household where occupations are in elementary or intermediate and unemployed. For the influence of Household space, the results indicate that the risk of burglary rate increases within the household living in shared houses.

Spatial datasets contain information relating to the locations of incidents of a disease or other phenomena. Appropriate analysis of such datasets can reveal information about the distribution of cases of the phenomena. Areas that contain higher than expected incidence of the phenomena, given the background population, are of particular interest. Such clusters of cases may be affected by external factors. By analysing the locations of potential influences, it may be possible to establish whether a cause and effect relationship is present within the dataset. This thesis describes research that has led to the development and application of cluster detection and feature selection techniques in order to determine whether causal relationships are present within generic spatial datasets. The techniques are described and demonstrated, and their effectiveness established by testing them using synthetic datasets. The techniques are then applied to a dataset supplied by the Welsh Leukaemia Registry that details all cases of leukaemia diagnosed in Wales between 1990 and 2000. Cluster detection techniques can be used to provide information about case distribution. A novel technique, CLAP, has been developed that scans the study region and identifies the statistical significance of the levels of incidence in specific areas. Feature selection techniques can be used to identify the extent to which a selection of inputs impact upon a given output. Results from CLAP are combined with details of the locations of potential causal factors, in the form of a numerical dataset that can be analysed using feature selection techniques. Established techniques and a newly developed technique are used for the analysis. Results from such analysis allow conclusions to be drawn as to whether geographical causal relationships are apparent.

Clusters of the transition metals were generated by laser vaporisation of a sample of the metal into the throat of a pulsed supersonic expansion. This allowed clusters with internal temperatures as low as 5 K to be routinely prepared. Mass-selective detection was accomplished by multi-photon ionisation of the clusters within the ion source of a time - of - flight mass spectrometer. Use of a tunable laser to carry out electronic excitation, prior to ionisation, allowed mass - resolved resonant two - photon ionisation spectra of the clusters to be recorded. Real time control of the experiment and automated data logging was achieved using software developed to run on an IBM PC - AT microcomputer. This allowed multiple ion signals to be recorded simultaneously whilst carrying out R2PI or time-resolved studies on the metal cluster species in the beam. Resonant two - photon ionisation spectroscopic studies were carried out on the ( 0 - 0 ) and ( 1 - 0 ) bands of the J X system of Cu9 and the A X system of Ag->. The 0.04 cm-1 bandwidth of the tunable dye laser used allowed rotationally resolved spectra to be recorded. The spectra recorded for these systems showed them both to be AA = 0 ( or AS2 = 0 ) transitions. The J state of CU2 was assigned to the 1 Zj state derived from the ?P + atomic limit at Dg(X) + 45821 cm-1. Rotational analysis of the spectra yieldedl | lthe following constants for the Cu2 isotopomer: Bg = 0.1166(1) cm , ae = 0.0021(1) cm-1. This gave Rg = 2.138(1) A for the J state, shorter than the ground state bond length. Accordingly the transition was assigned to 3ditg -*?4piru, to give the above assignment. The rotational constants obtained, for the *?7Ag-, isotopomer, from analysisI _ | *of the spectra of the A X system of Ag-, were: Bg = 0.0447(3) cm , ae= 0.0004(2) cm'*, and Bq = 0.0490(18) cm"1. These gave bond lengths of Rg = 2.649(9) A and Rq = 2.530(46) A. The observed Ail = 0 transition agreed with the previous assignment of the A state as 0* arising from the 5sag -+ 5sau promotion.

This research project focuses on using a multiproxy approach to discriminate between overwash and non-hurricane marsh sediments within the bed of a coastal lake. 3 marsh cores were collected in an area of McFaddin National Wildlife Refuge just south of Clam Lake that are known to contain 4 hurricane overwash deposits, Ike, Rita, Carla, and Audrey. LOI and XRF analysis were used to determine the signature of the hurricane overwash layers. 3 more cores were collected from Clam Lake where there are no visible sand layers. The elemental signature of the overwash layers found in the marsh cores was used to run a hierarchical cluster analysis on the lake cores. This was able to determine the effectiveness of XRF's ability to distinguish between hurricane overwash and marsh sediments. The combination of cluster analysis, LOI, and XRF can tentatively identify hurricane overwash deposits in a coastal lake, however, it is more successful in the marsh cores. Results in the lake cores are somewhat inconsistent and uncertain, possibly because there may have not been enough overwash deposits to identity or that the XRF analysis needs more distinct sand layers to distinguish between overwash and marsh.