Through the years, there has been a great deal of archaeological research focused on the earliest farming cultures of Europe (i.e. Early Neolithic). However, little effort has been expended to uncover the type and nature of daily activities performed within Early Neolithic dwellings, particularly in the Balkans. This thesis conducts a spatial analysis of the Early Neolithic pit house levels of the Foeni-Salaş site in southeast Romania, in the northern half of the Balkans, to determine the kinds and locations of activities that occurred in these pit houses. Characteristic Early Neolithic dwellings in the northern Balkans are pit houses. The data are analyzed using Geographic Information Systems (GIS) technology in an attempt to identify non-random patterns that will indicate how the pit house inhabitants used their space. Both visual and statistical (Nearest Neighbor) techniques are used to identify spatial patterns. Spreadsheet data are incorporated into the map database in order to compare and contrast the results from the two techniques of analysis. Map data provides precise artefact locations, while spreadsheet data yield more generalized quad centroid information. Unlike the mapped data, the spreadsheet data also included artefacts recovered in sieves. Utilizing both data types gave a more complexand fuller understanding of how space was used at Foeni-Salaş. The results show that different types of activity areas are present within each of the pit houses. Comparison of interior to exterior artifact distributions demonstrates that most activities take place within pit house. Some of the activities present include weaving, food preparation, butchering, hide processing, pottery making, ritual, and other activities related to the running of households. It was found that these activities are placed in specific locations relative to features within the pit house and the physical structure of the pit house itself. This research adds to the growing body of archaeological research that implements GIS to answer questions and solve problems related to the spatial dimension of human behaviour.

Invasive exotic plant species have been recognized as serious threats to ecosystems. Extensive research on invasive exotic plant species has primarily focused on the impacts, characteristics, and potential treatments. Decision tools and management models that incorporate these findings often lack input from managers and have limited use in differing invasion scenarios. Therefore, in this study, I created a scientifically-driven framework that incorporates expert input to prioritize watersheds for management within the Inner Bluegrass region of Kentucky. The widely distributed invasive exotic plant Amur honeysuckle (Lonicera maackii) was used as an example species. The framework is built around the Analytic Hierarchy Process and highlights areas in most need of invasive exotic plant management by incorporating weighted landscape variables associated with the invasion process. Results of the prioritization provide useful information for natural resource managers by aiding in the development of control strategies while also creating a valuable framework that can be adapted to various invasive exotic plant species.

South Africa is a megadiverse country, and its biodiversity is endangered by population pressure and the development needs of a developing country. In order to address the rapid decline in biological diversity, biodiversity planning has become a key focus area that aims at identifying priority areas for species and ecosystem conservation within and outside of formally protected areas. Plant conservation hotspots are identified by the quantification of indicator taxa such as plant taxa richness, rarity and endemism. But the urgent and enormous task of biodiversity assessment for conservation planning requires that we make most of what we know. Therefore, this study seeks to make a contribution by finding new ways of biodiversity pattern estimation from the extrapolation of incomplete sets of plant species distribution data at the Quarter Degree Grid level. Incomplete sampling across the grids of a study area results in false records of species absence and thus a biased biodiversity estimation. As a possible solution, plant distribution data for the western Central Bushveld Bioregion has been standardized using two profiles, namely the ‘Centroid Grid’ and ‘Integrated Grid’ profile. The former involves the strengthening of under-sampled grids by extrapolating species occurrences from three adjacent grids with the most similar vegetation units, whereas the latter integrates phyto-diversity data for the four grids intersecting at each grid reference point. Standardized data has proved to provide a means to counter the bias in plant diversity data linked to Quarter Degree Grids by a) strengthening of under-sampled grids and b) visibly smoothing out the gaps between under- and well-sampled grids, which resulted in improved biodiversity estimation for more representative spatial biodiversity modelling. Interpolation created geo-referenced polygons for more explicit use in the identification of areas of conservation concern at bioregional scale. However, well-sampled grids still dominate the outcomes of the analysis by creating spatial sampling bias. Therefore, this approach to calibrate Quarter Degree Grid resolution of spatial data is an additional attempt to achieve more representative mapping of biodiversity patterns, which is a prerequisite for strategic conservation planning for ‘living landscapes’. / Thesis (MSc (Environmental Sciences))--North-West University, Potchefstroom Campus, 2012

South Africa is a megadiverse country, and its biodiversity is endangered by population pressure and the development needs of a developing country. In order to address the rapid decline in biological diversity, biodiversity planning has become a key focus area that aims at identifying priority areas for species and ecosystem conservation within and outside of formally protected areas. Plant conservation hotspots are identified by the quantification of indicator taxa such as plant taxa richness, rarity and endemism. But the urgent and enormous task of biodiversity assessment for conservation planning requires that we make most of what we know. Therefore, this study seeks to make a contribution by finding new ways of biodiversity pattern estimation from the extrapolation of incomplete sets of plant species distribution data at the Quarter Degree Grid level. Incomplete sampling across the grids of a study area results in false records of species absence and thus a biased biodiversity estimation. As a possible solution, plant distribution data for the western Central Bushveld Bioregion has been standardized using two profiles, namely the ‘Centroid Grid’ and ‘Integrated Grid’ profile. The former involves the strengthening of under-sampled grids by extrapolating species occurrences from three adjacent grids with the most similar vegetation units, whereas the latter integrates phyto-diversity data for the four grids intersecting at each grid reference point. Standardized data has proved to provide a means to counter the bias in plant diversity data linked to Quarter Degree Grids by a) strengthening of under-sampled grids and b) visibly smoothing out the gaps between under- and well-sampled grids, which resulted in improved biodiversity estimation for more representative spatial biodiversity modelling. Interpolation created geo-referenced polygons for more explicit use in the identification of areas of conservation concern at bioregional scale. However, well-sampled grids still dominate the outcomes of the analysis by creating spatial sampling bias. Therefore, this approach to calibrate Quarter Degree Grid resolution of spatial data is an additional attempt to achieve more representative mapping of biodiversity patterns, which is a prerequisite for strategic conservation planning for ‘living landscapes’. / Thesis (MSc (Environmental Sciences))--North-West University, Potchefstroom Campus, 2012

A large portion of the freshwater in western Canada originates as snowpack from the northern Rocky Mountains. The temperature and precipitation in these areas controls the amount of snow accumulated and stored throughout the winter, and the amount and timing of melt that occurs during the spring freshet. Therefore, a better understanding of past and future changes to the extent of snowpack and timing of melt can modify the timing of peak river flow on a continental scale. Trends in temperature, precipitation, snow accumulation, and snowmelt are examined using the Mann-Kendall non-parametric test on a high resolution gridded climate dataset over western Canada for the period 1950-2010. In addition, projected changes in temperature, precipitation, snow water equivalent, and snowmelt are examined through comparison of the current (1971-2000) and future (2041-2070) time periods incorporating several regional climate models. The temporal and spatial analyses of these key hydroclimatic variables indicate that changes vary greatly over space and time. Results reveal that while both maximum and minimum temperature have increased in the past 60 years, minimum temperature has increased more than maximum temperature and is likely to continue doing so in the future. This trend is particularly evident during the colder months of the year, and at higher elevations, contributing to earlier spring melt. Between 1950 and 2010, precipitation has decreased throughout the colder months of the year and increased in the warmer months, particularly in the northern half of the study area. Future projections show increased precipitation, specifically in the north. Throughout the historical period snow accumulation has experienced decreases across the study area and through all months of the year, except for increases at high elevations. In the coldest months of the year snow accumulation is projected to increase in high elevation and northern areas while decreasing across the rest of study area in the future. Snowmelt results indicate slight increases in mid-winter melt events and an earlier onset of the spring freshet; this change is expected to continue into the future period. This study provides a summary of detected trends and potential future changes in key hydroclimatic variables across western Canada with regard to the effects these changes can have on the spring freshet and streamflow, and thus water resources, throughout the study area. / Graduate / 0368

Through the years, there has been a great deal of archaeological research focused on the earliest farming cultures of Europe (i.e. Early Neolithic). However, little effort has been expended to uncover the type and nature of daily activities performed within Early Neolithic dwellings, particularly in the Balkans. This thesis conducts a spatial analysis of the Early Neolithic pit house levels of the Foeni-Salaş site in southeast Romania, in the northern half of the Balkans, to determine the kinds and locations of activities that occurred in these pit houses. Characteristic Early Neolithic dwellings in the northern Balkans are pit houses. The data are analyzed using Geographic Information Systems (GIS) technology in an attempt to identify non-random patterns that will indicate how the pit house inhabitants used their space. Both visual and statistical (Nearest Neighbor) techniques are used to identify spatial patterns. Spreadsheet data are incorporated into the map database in order to compare and contrast the results from the two techniques of analysis. Map data provides precise artefact locations, while spreadsheet data yield more generalized quad centroid information. Unlike the mapped data, the spreadsheet data also included artefacts recovered in sieves. Utilizing both data types gave a more complexand fuller understanding of how space was used at Foeni-Salaş. The results show that different types of activity areas are present within each of the pit houses. Comparison of interior to exterior artifact distributions demonstrates that most activities take place within pit house. Some of the activities present include weaving, food preparation, butchering, hide processing, pottery making, ritual, and other activities related to the running of households. It was found that these activities are placed in specific locations relative to features within the pit house and the physical structure of the pit house itself. This research adds to the growing body of archaeological research that implements GIS to answer questions and solve problems related to the spatial dimension of human behaviour.

Sharing and inappropriate discarding of syringes and drug use equipment can lead to transmission of bloodborne pathogens and decreased sense of community safety. To reduce these risks, interventions such as syringe drop boxes, are implemented. However, little consideration has been made of the social and spatial networks of the injection drug use (IDU) populations in the placement of these drop boxes. A sample of IDU was obtained through respondent driven sampling in Winnipeg, Canada in 2009. Characteristics of the sample and distribution of these characteristics through the social network were assessed. A spatial network was constructed which focused on the connections between IDU and specific geographic locations. Measures of centrality were calculated using Pajek and the geographic network was mapped using ArcGIS. Analysis of the social network revealed variation among network components in demographic and drug use characteristics. Spatial analysis revealed geographic clustering, quantified through network centrality measures. There was congruence between locations of high degree and current drop box placement in Winnipeg. This research illustrates the benefit of combining IDU social network and spatial data to inform evidence-based municipal policies and programs.

Acute Myocardial Infarction (AMI), commonly known as heart attack, is a leading cause of death for adult men and women in the world. Studying mortality after AMI is therefore an important problem in epidemiology. This thesis develops statistical methodology for examining geographic patterns in mortality following AMI. Specifically, we develop parametric Accelerated Failure Time (AFT) models for censored survival data, where space-varying regression is used to investigate spatial patterns of mortality after AMI. In addition to important covariates such as age and gender, the regression models proposed here also incorporate spatial random e ects that describe the residual heterogeneity associated with di erent local health geographical units. We conduct model inference under a hierarchical Bayesian modeling framework using Markov Chain Monte Carlo algorithms for implementation. We compare an array of models and address the goodness-of- t of the parametric AFT model through simulation studies and an application to a longitudinal AMI study in Quebec. The application of our AFT model to the Quebec AMI data yields interesting ndings concerning aspects of AMI, including spatial variability. This example serves as a strong case for considering the parametric AFT model developed here as a useful tool for the analysis of spatially correlated survival data.

This research develops spatial-explicit methods to characterize the relationship between wildlife and habitat use and selection. Both home range analysis and resource selection function (RSF) models, two common methods of representing wildlife-habitat associations, are often summarized aspatially. I apply a novel method to home range analysis which quantifies the spatial-temporal patterns of site fidelity and range drift. As a result, the spatial structure of home ranges is described, thus building on current methods which summarize ranges as aspatial metrics, often mean area. Furthermore, I develop a new method to spatially assess the ability of RSF models to predict wildlife occurrence using conditional randomization. As opposed to summarizing RSF model accuracy as a single value, I produce spatially-explicit and mappable outputs. I also demonstrate how this spatial method may be used to improve RSF model results. I apply these two spatial-temporal methods to a case study on adult female grizzly bears (Ursus arctos) in the Northeastern slopes of the Canadian Rockies. Through describing the spatial-temporal pattern of grizzly bear home range change, I determine that offspring status and season impact the size and spatial configuration of a bear’s home range. By spatially evaluating the predictive success of a RSF model, I locate and quantify the spatial pattern of areas where the model is under-predicting bear occurrence using Local Moran’s I. Further, I evaluate landscape characteristics at these locations and suggest additions to the model which may increase accuracy. Both home range analysis methods and RSF evaluation techniques could assist in conservation by aiding in the delineation of critical grizzly bear habitat areas in both space and time.

Scientists are now able to collect ubiquitous data on individual-level movement at increasingly fine spatial and temporal resolutions. Despite this surge in data availability, methods for extracting relevant information about spatial-temporal movement patterns remain limited in scope and sophistication. The objective of this PhD research is to develop novel quantitative approaches for analyzing spatial-temporal patterns in modern movement datasets. A review of the state-of-the-art in quantitative movement analysis identifies the current breadth of available methods, while highlighting key limitations and fragmentation in the literature across multiple disciplines. Existing theory from the geographical literature, namely time geography is applied to a novel application – wildlife movement ecology (termed the PPA home range), in an attempt to expose these ideas to wildlife researchers. The PPA home range method has several advantages over existing methods, most notably its ability to identify omission and commission error in existing home range techniques. Next, an advance to time geography theory is proposed for incorporating object kinetics (i.e., velocity and acceleration) into a probabilistic movement model termed kinetic-based probabilistic time geography. Kinetic-based probabilistic time geography provides a more accurate model for predicting object movement when object kinetics are relevant (e.g., with fast moving vehicles, or athletes). A novel method (termed the DI index) for quantifying dynamic interactions between moving objects is presented, focusing specifically on examining cohesive movement behaviour. The DI index is advantageous over existing dynamic interaction measures in that it is computed at the local level, facilitating a finer treatment of interactive movement behaviour. The DI index is then contrasted with seven alternative measures of dynamic interaction to examine the effectiveness of each at identifying expected and unexpected interactive behaviour, at a range of sampling resolutions, in the context of wildlife movement ecology. The results highlight the value of the DI index, especially as a local level index, capable of identifying variable and infrequent interactions in pairs of moving objects. In summary, this dissertation contributes to the rapidly expanding body of quantitative movement research by providing: 1) a cross-disciplinary methodological review, 2) expanding the application of core time geography theory to wildlife ecology, 3) advancing time geographic theory in development of kinetic-based probabilistic time geography, 4) developing a novel index (the DI index) for measuring inter-object interactions, and 5) examining the effectiveness of available dynamic interaction measures, and their sensitivity across sampling resolutions, in the context of wildlife ecology. / Graduate / 0366 / 0463 / 0329