Studying the spatial distribution and redistribution of population has long been a major concern of demography, because population changes can reflect deep and massive social changes. For decades, the major population change was the moving of people from rural to urban regions. However, with the advancement of transportation and information technology, many new regions have become more attractive to people, such as small and new metropolitan, nonmetropolitan, suburban, and rural areas. Traditional migration and population redistribution studies emphasize economic and social factors. Relatively little attention is paid to how natural amenities and transportation affect changes of population size and net migration. Using data from various sources, such as the U.S. Census Bureau, National Land Cover Database, United States Department of Agriculture, National Transportation Atlas Database, and Air Carrier Activity Information System, this dissertation examines the roles of natural amenities and transportation in explaining population change and the net migration rate from 2000 to 2010 in the United States at the county level. Spatial regression models are used to treat spatial dependence and investigate relationships between variables and their neighboring values. Results show that population growth is higher in counties with higher natural-amenity-ranking values, regardless of whether those counties are in metropolitan or nonmetropolitan areas. However, natural-amenity-ranking values only positively affect net migration rates in nonmetropolitan counties. Forest coverage only positively affects population change and the net migration rate in nonmetropolitan counties. Land developability is negatively associated with population change in nonmetropolitan counties. Man-made amenities are negatively associated with population change and the net migration rate in both metropolitan and nonmetropolitan counties. Population growth and the net migration rate are higher in counties characterized by greater airport accessibility. Highway density is positively associated with population change in nonmetropolitan counties only. This dissertation illustrates the importance of natural amenities, forest coverage, land developability, highway density, and airport accessibility as correlates of population growth in America, especially in nonmetropolitan counties.

Proponents of educational reform often call for policies to increase competition between schools. It is argued that market forces naturally lead to greater efficiencies, including improved student learning, when schools face competition. In many parts of the country, public schools experience significant competition from private schools; however, the literature is not clear as to whether public versus private competition generates significant improvements in technical efficiency. A major hurdle for researchers examining this issue is determining a workable definition of competition by which they can measure the degree of competition within local markets. I address this challenge by developing a School Competition Index (SCI) for Mississippi through implementation of several Geographical Information System (GIS) tools. The SCI reveals the degree of competition for each public school based on their spatial location relative to peer private schools operating within their service area. GIS is a unique way to measure the degree of competition among public schools and private schools. Including components of market structure is not sufficient to measure the effects of competition in a market; market characteristics, which vary between locations, are also important. Market characteristics such as, religiosity, school location, and social capital are used in this dissertation as exogenous variables. Two stage stochastic frontier analysis and single equation stochastic frontier analysis are both employed to evaluate school efficiency. This dissertation finds that higher degrees of competition from private schools significantly increase public elementary school efficiency, as measured by the proficiency rates in different examinations. At the same time, competition from private schools does not improve public high schools efficiency. The results suggest that a rural-urban student academic achievement gap persists, and that community social capital stock is also important to some extent. Regardless of model or estimation procedure, students’ race and socio-economic status significantly reduce public school efficiency. It is anticipated that the current results will inform policymakers regarding the viability of competition-based reforms after considering all these factors.

Jamaican farmers are experiencing constraints to hot pepper (Capsicum chinense) export production due to a quarantine pest -- the gall midge (Contarinia lycopersici; Prodiplosis longifila). There is a threat of gall midge introduction into the United States, where the insect pest is not known to occur. This research tests the significance of a range of variables to gall midge incidence. The purpose was to explain the spatial patterns that result from the relationships between gall midge incidence in hot pepper production and production methods and/or environmental conditions. There were three components to the sample of 47 farm visits: the interview, the hot pepper sampling, and the measurements of physical and locational attributes. Producers responded to questions about production methods, marketing, and quarantine issues. The percent of infested fruits per plot was calculated. GPS was used to record farm location. Using ArcView, environmental and climatic datasets were overlaid with farm locations and their attributes. Multiple regression was used to measure significance of variables to gall midge incidence. Cluster analyses were used to demonstrate the spatial patterns of the variability of gall midge incidence and its associated variables. There was significant effect on incidence by farm elevation, observance of pesticide-use recommendations, producer awareness of pre-clearance fumigation, and the use of intercropping in hot pepper production. / Master of Science

Air pollution is highly variable in both space and time, which presents many challenges to researchers when they wish to model concentrations. The modelling of air pollution is necessary for a number of reasons, which include the determination of human health effects, providing warning of health risks, and to understand general ecosystem health. In this thesis, modelling of air pollution through both space and time has been explored, with a focus on improving models that can be used to assign air pollution exposure. The techniques presented in this thesis have leveraged the ability of mobile monitoring units to collect air pollution concentration data multiple locations throughout a study period. First, we explore the use of combining mobile air pollution monitoring data with traditional fixed location monitoring. We find that the mobile data is able to provide insight into changes in spatial pattern between two temporal periods that could not be identified solely with the fixed location monitors, which demonstrates value in this monitoring approach that can be built upon with refinement of techniques. Second, we present a method to determine the amount of classical error that will be introduced when a long-term mean concentration is calculated from a discontinuous time-series dataset, which are the type of datasets collected by mobile air pollution monitoring. Third, we merge mobile and stationary air pollution monitoring data, along with meteorological, transportation, and land use information to model the hourly air pollution field using neural network models. The models developed allowed for the assignment of air pollution exposure incorporating human activity patterns. Also, they can be used to provide a spatially refined air quality health index. Lastly, we demonstrate exposure assignment that incorporates human activity patterns to calculate the dose exposure for students during their trips to school. This work commences with a demonstration of the basic utility of mobile air pollution monitoring data, which is to increase the number of monitored locations. Building on that utility of mobile technology, a technique was developed to estimate the error when mobile units are used for long-term estimates, similar to stationary monitoring units; and we were able to provide guiding principles for mobile monitoring data collection. Furthering our objective, to better understand the value of mobile data in a fully integrated monitoring network, we utilized both mobile and stationary data collection techniques together, in a single model, to produce accurate estimates of an air pollution field on an hourly basis. Finally, the research culminates with the demonstration of how mobile monitoring can be used for activity based air pollution exposure estimates, which was shown with a case-study of students’ trips between home and school. Overall, the chapters in this thesis work toward a better understanding of how to incorporate mobile monitoring data into air pollution assessment studies. / Thesis / Doctor of Philosophy (PhD)

Paramedic services are developing a new model of service delivery known as community paramedicine (CP). This service delivery model seeks to build on existing paramedic skills, establish collaboration with non-traditional health care partners, and create alternative pathways for accessing care. Frequent users of paramedic services represent patients that are of particular interest to CP programs. Chapters 2 and 3 of this thesis address questions of effective delivery of these programs. The second chapter is a spatial-temporal analysis of frequent users in Hamilton, ON. Drawing on concepts of time-geography and dynamic ambulance deployment, this analysis identifies space-time patterns in paramedic service utilization by frequent users. Data were aggregated to represent daily demand in terms of space and time. Analysis employed generalized linear mixed models that included a random slope effect for time intervals for each geographic unit. Fixed effects included distance to emergency department, proportion of residential addresses, and proportion of older adult population. Locations and times that had greater or less than expected daily demand from frequent users were identified. The findings can be used to tailor deployment of community paramedics in dual-capacity roles to address the system demand of frequent users. The third chapter analyzes the geographic influence of CP service delivery in Renfrew County, ON. This research draws on concepts of spatial accessibility and geographic profiling to estimate spatially defined probabilities of paramedic service use by frequent users. Due to ongoing CP programs within the county, the resultant community health profiles serve as an evaluation of the benefit of these programs. The community health profiles can also be used to assess community level probabilities of patient needs for future interventions. This analysis can serve as a new way to assess spatial accessibility to health care services and identify locations with increased risk of frequent use of paramedic services. / Thesis / Master of Arts (MA)

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Wireless sensor networks (WSNs) allow us to instrument the physical world in novel ways, providing detailed insight that has not been possible hitherto. Since WSNs provide an interface to the physical world, each sensor node has a location in physical space, thereby enabling us to associate spatial properties with data. Since WSNs can perform periodic sensing tasks, we can also associate temporal markers with data. In the environmental sciences, in particular, WSNs are on the way to becoming an important tool for the modelling of spatially and temporally extended physical phenomena. However, support for high-level and expressive spatial-analytic tasks that can be executed inside WSNs is still incipient. By spatial analysis we mean the ability to explore relationships between spatially-referenced entities (e.g., a vineyard, or a weather front) and to derive representations grounded on such relationships (e.g., the geometrical extent of that part of a vineyard that is covered by mist as the intersection of the geometries that characterize the vineyard and the weather front, respectively). The motivation for this endeavour stems primarily from applications where important decisions hinge on the detection of an event of interest (e.g., the presence, and spatio-temporal progression, of mist over a cultivated field may trigger a particular action) that can be characterized by an event-defining predicate (e.g., humidity greater than 98 and temperature less than 10). At present, in-network spatial analysis in WSN is not catered for by a comprehensive, expressive, well-founded framework. While there has been work on WSN event boundary detection and, in particular, on detecting topological change of WSN-represented spatial entities, this work has tended to be comparatively narrow in scope and aims. The contributions made in this research are constrained to WSNs where every node is tethered to one location in physical space. The research contributions reported here include (a) the definition of a framework for representing geometries; (b) the detailed characterization of an algebra of spatial operators closely inspired, in its scope and structure, by the Schneider-Guting ROSE algebra (i.e., one that is based on a discrete underlying geometry) over the geometries representable by the framework above; (c) distributed in-network algorithms for the operations in the spatial algebra over the representable geometries, thereby enabling (i) new geometries to be derived from induced and asserted ones, and (ii)topological relationships between geometries to be identified; (d) an algorithmic strategy for the evaluation of complex algebraic expressions that is divided into logically-cohesive components; (e) the development of a task processing system that each node is equipped with, thereby with allowing users to evaluate tasks on nodes; and (f) an empirical performance study of the resulting system.