Browsing by Subject "spatial statistics"
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Item Geographies of identity theft in the u.s.: understanding spatial and demographic patterns, 2002-2006(2009-05-15) Lane, Gina W.Criminal justice researchers and crime geographers have long recognized the importance of understanding where crimes happen as well as to whom and by whom. Although past research often focused on violent crimes, calls for research into non-lethal white-collar crimes emerged in the 1970s. Today, identity theft is among the fastest growing white-collar crimes in the United States, although official recognition of it as a criminal act is a relatively recent development. Remaining largely unmet, the need for white-collar crime research has greatly intensified considering the escalating identity theft problem. Furthermore, many studies conclude that identity theft will continue to rise due to increasing technology-driven offenses via the Internet and widespread use of digital consumer databases. Utilizing theoretical framework established in crime geography, GIS mapping and spatial statistics are employed to produce a spatial analysis of identity theft in the U.S. from 2002-2006. Distinct regional variations, such as high rates in the western and southwestern states, and low rates in New England and the central plains states, are identified for identity theft as reported by the FTC. Significant spatial patterns of identity theft victims alongside social demographic variables are also revealed in order to better understand the regional patterns that may indicate underlying social indicators contributing to identity theft. Potential social variables, such as race/ethnicity and urban-rural populations, are shown to have similar patterns that may be directly associated with U.S. identity theft victims. To date, no in-depth geographic studies exist on the geographic patterns of identity theft, although numerous existing studies attempt basic spatial pattern recognition and propose the need for better spatial interpretation. This thesis is the first empirical study on the geographies of identity theft. It fills in a void in the literature by revealing significant geographical patterns of identity theft in the digital age, attempts at understanding the social factors driving the patterns, and examines some of the social implications of identity theft.Item Nonparametric Methods for Point Processes and Geostatistical Data(2011-10-21) Kolodziej, Elizabeth YoungIn this dissertation, we explore the properties of correlation structure for spatio-temporal point processes and a quantitative spatial process. Spatio-temporal point processes are often assumed to be separable; we propose a formal approach for testing whether a particular data set is indeed separable. Because of the resampling methodology, the approach requires minimal conditions on the underlying spatio-temporal process to perform the hypothesis test, and thus is appropriate for a wide class of models. Africanized Honey Bees (AHBs, Apis mellifera scutellata) abscond more frequently and defend more quickly than colonies of European origin. That they also utilize smaller cavities for building colonies expands their range of suitable hive locations to common objects in urban environments. The aim of the AHB study is to create a model of this quantitative spatial process to predict where AHBs were more likely to build a colony, and to explore what variables might be related to the occurrences of colonies. We constructed two generalized linear models to predict the habitation of water meter boxes, based on surrounding landscape classifications, whether there were colonies in surrounding areas, and other variables. The presence of colonies in the area was a strong predictor of whether AHBs occupied a water meter box, suggesting that AHBs tend to form aggregations, and that the removal of a colony from a water meter box may make other nearby boxes less attractive to the bees.Item Resampling Methodology in Spatial Prediction and Repeated Measures Time Series(2012-02-14) Rister, Krista DianneIn recent years, the application of resampling methods to dependent data, such as time series or spatial data, has been a growing field in the study of statistics. In this dissertation, we discuss two such applications. In spatial statistics, the reliability of Kriging prediction methods relies on the observations coming from an underlying Gaussian process. When the observed data set is not from a multivariate Gaussian distribution, but rather is a transformation of Gaussian data, Kriging methods can produce biased predictions. Bootstrap resampling methods present a potential bias correction. We propose a parametric bootstrap methodology for the calculation of either a multiplicative or additive bias correction factor when dealing with Trans-Gaussian data. Furthermore, we investigate the asymptotic properties of the new bootstrap based predictors. Finally, we present the results for both simulated and real world data. In time series analysis, the estimation of covariance parameters is often of utmost importance. Furthermore, the understanding of the distributional behavior of parameter estimates, particularly the variance, is useful but often difficult. Block bootstrap methods have been particularly useful in such analyses. We introduce a new procedure for the estimation of covariance parameters for replicated time series data.