Recently, increased attention has been given to the social and environmental context in which crimes occur (Wells & Weisheit 2004). This new interest in the human ecology of crime is largely demographic, both in terms of subject matter and increasingly in terms of the analytic methods used. Building on existing literature on the social ecology of crime, this study introduces a new approach to studying sub-county geographies of reported crime using existing census place and county definitions coupled with spatial demographic methods. Spatially decomposing counties into Census places and what Esselstyn (1953) earlier called “open country,” or non-places, allows for the development of a unique but phenomenological meaningful sub-county geography that substantively holds meaning in conceptualizing rural and urban localities in the demographic analysis of crime. This decomposition allows for the examination of core-periphery relationships at the sub-county level, which are hypothesized to act similarly to those at the national level (Agnew 1993; Lightfoot and Martinez 1995). Using 1990 to 2000 Agency-level UCR data within this approach, I propose to use spatial statistics to describe and explain patterns of crime across differing localities. Potential processes of spatial mobility in regards to the spread of criminal activity from places to non-place localities are also examined. In order to adequately understand these spatial patterns of crime while testing the usability of the new place-level geography, several of the generally accepted theories of crime and a number of explanatory factors and covariates are tested. Furthermore, using this sub-county geography, significant patterns of spatial diffusion and contagion are through the implementation and modification of the spatio-temporal model, which provides the current point of departure and put forth by Cohen and Tita (1999). The results are promising and suggest a meaningful contribution to the ecological analysis of crime and the larger sub-discipline of spatial demography.

No description available.

No description available.

The modeling of spatio-temporal and multivariate spatial random fields has been an important and growing area of research due to the increasing availability of spacetime-referenced data in a large number of scientific applications. In geostatistics, the covariance function plays a crucial role in describing the spatio-temporal dependence in the data and is key to statistical modeling, inference, stochastic simulation and prediction. Therefore, the development of flexible covariance models, which can accomodate the inherent variability of the real data, is necessary for an advantageous modeling of random fields. This thesis is composed of four significant contributions in the development and applications of new covariance models for stationary multivariate spatial processes, and nonstationary spatial and spatio-temporal processes. The first focus of the thesis is on modeling of stationary multivariate spatial random fields through flexible multivariate covariance functions. Chapter 2 proposes a semiparametric approach for multivariate covariance function estimation with flexible specification of the cross-covariance functions via their spectral representations. The proposed method is applied to model and predict the bivariate data of particulate matter concentration (PM2.5) and wind speed (WS) in the United States. Chapter 3 introduces a parametric class of multivariate covariance functions with asymmetric cross-covariance functions. The proposed covariance model is applied to analyze the asymmetry and perform prediction in a trivariate data of PM2.5, WS and relative humidity (RH) in the United States. The second focus of the thesis is on nonstationary spatial and spatio-temporal random fields. Chapter 4 presents a space deformation method which imparts nonstationarity to any stationary covariance function. The proposed method utilizes the functional data registration algorithm and classical multidimensional scaling to estimate the spatial deformation. The application of the proposed method is demonstrated on a precipitation data. Finally, chapter 5 proposes a parametric class of time-varying spatio-temporal covariance functions, which are nonstationary in time. The proposed class is a time-varying generalization of an existing nonseparable stationary class of spatio-temporal covariance functions. The proposed time-varying model is then used to study the seasonality effect and perform space-time predictions in the daily PM2.5 data from Oregon, United States.

Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas. / Master of Science / Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas.

In this dissertation, a variable selection method for spatial data is developed. It is assumed that the spatial process is non-stationary as a whole but is piece-wise stationary. The pieces where the spatial process is stationary are called regions. The variable selection approach accounts for two sources of correlation: (1) the spatial correlation of the data within the regions, and (2) the correlation of adjacent regions. The variable selection is carried out by including indicator variables that characterize the significance of the regression coefficients. The Ising distribution as prior for the vector of indicator variables, models the dependence of adjacent regions. We present a case study on brook trout data where the response of interest is the presence/absence of the fish at sites in the eastern United States. We find that the method outperforms the case of the probit regression where the spatial field is assumed stationary and isotropic. Additionally, the method outperformed the case where multiple regions are assumed independent of their neighbors. / Ph. D.

Multimodality MR imaging of pathological changes in schizophrenia Aim: To prove structural changes of the neocortex and white matter of the brain indicating connectivity disorder in early phases of schizophrenia. Material and methods: A prospective monocentric study comparing a cohort of patients after the first episode of schizophrenia (on average 15.6 days after the initial hospitalization) with a control group of healthy persons. Probands were examined using a complex MRI protocol. Twenty-six patients and twenty-four healthy persons were examined in total. Three dimensional T1 and T2 data and DWI data were analyzed using TBSS FA, FBA a surface- based morphometry. Results: Large areas of dispersively decreased FA were found in patients compared to control group using TBSS. Several fixels of decreased FD metric were found using FBA in the anterior commissure of patients and one sporadic fixel of decreased FDC metric was found in frontal white matter of the brain. No statistically significant areas of cortical surface area and cortical thickness differences were found using SBM. Conclusions: Large areas of decreased microstructural integrity of the white matter of the brain were found. However, it was not possible to specify the nature of its corruption using FBA. Our findings indicate the crucial role of...

Master of Science / Department of Statistics / Juan Du / Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.

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Human impacts on ocean ecosystems are driving declines in marine biodiversity, including marine mammals. Comprehensive spatial data are vital for making informed management decisions that may aid species recovery and facilitate the sustainable use of ocean ecosystems. However, marine mammal studies are often data limited, thereby restricting possible research questions. Developing novel analytical approaches and incorporating unconventional datasets can expand the scope of analysis by increasing the information content of existing data sources. The goal of our research is to support conservation and management of marine mammals in British Columbia (BC), Canada, through the application of advanced spatial statistical methodology to characterize spatial distribution and density patterns and provide assessments of data uncertainty. Our first objective is to generate statistical models to map spatially continuous predictions of marine mammal distributions and densities within BC’s north coast and apply methodology from spatial statistics to identify hotspots of elevated use. We use species observations collected from systematic line transect surveys previously adjusted to generate estimates of density per nautical mile of transect. We predict the distribution and density patterns of nine marine mammal species by employing a species-habitat model to relate species densities to environmental covariates using a generalized additive model. We use spatial statistical hotspot analysis (Getis-Ord Gi*statistic) and an aspatial threshold approach to identify hotspots of high density. Our analysis reveals that hotspots selected using a top percentage threshold produced smaller and more conservative hotspots than those generated using the Gi*statistic. The Gi*statistic demonstrates a robust and objective technique for quantifying spatial hotspots and offers an alternative method to the commonly applied aspatial threshold measure. We find that maps show agreement with prior research and hotspots align with ecologically important areas previously identified by expert opinion. Our second objective is to apply map comparison techniques to compare cetacean density maps from disparate data collection methods (systematic surveys and citizen science) to evaluate the information content of each map product and quantify similarities and differences. Discrepancies are quantified by performing image differencing techniques on the rank order values of each map surface. We subsequently use the Gi*statistic to isolate regions where extreme differences occur. To assess similarities, a Gi*statistic is applied to both maps to locate spatially explicit areas of high cetacean density. Where clusters of high density values in both maps overlap we infer higher confidence that the datasets are representing a true ecological signal, while areas of difference we recommend as targeted locations for future sampling effort. We contextualize map similarities and differences using a dataset of human activity in the form of cumulative human effect scores. Overall, our analytical approach integrates novel spatial datasets from systematic surveys, citizen science, and remote sensing to provide updated information on cetacean distributions in BC. Our study generates geographic data products that fill knowledge gaps and results provide baseline information valuable for future decision-making. The methodology applied in this study can be generalized across species and locations to support spatial planning and conservation prioritization in both marine and terrestrial contexts. / Graduate / 2017-11-13