published_or_final_version / Civil and Structural Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

Atmospheric artifacts superimposed on interferometric synthetic aperture radar (InSAR) measurements have the potential to greatly impede the accurate estimation of deformation signals. The research presented in this dissertation demonstrates a novel InSAR time series algorithm, called HiRAPS algorithm, for effectively estimating high resolution atmospheric phase screens (APS) from differential InSAR measurements. In summary, the HiRAPS algorithm utilizes short time span differential interferograms and rearranges components of existing advanced InSAR techniques to identify a higher density of scatterers used to create the APS. The improved scatterer density allows one to estimate high spatial frequency atmospheric signals not recovered from existing InSAR time series techniques. The HiRAPS algorithm was tested with simulated and actual data, which contain phase contributions from linear and nonlinear deformation, topographic height errors, and atmospheric artifacts. Simulated differential interferograms were generated to have the same spatial and temporal baselines as the actual differential interferograms formed from RADARSAT-1 data over Phoenix, Arizona. The APS superimposed on simulated differential interferograms were then estimated and compared to simulated APS. The root mean square error (RMSE) between the estimated and simulated APS was calculated to qualitatively assess the different values obtained. The RMSE was 0.26 radians when utilizing the HiRAPS algorithm, compared to an RMSE value of 0.39 radians using an implementation of the permanent scatterer (PS) algorithm. The HiRAPS algorithm also showed its applicability for estimating high spatial frequency atmospheric signals for actual data. Sixty-six SAR images, starting from October 5, 2002 and spanning 5 years, were processed for this research. The APS pixel density obtained using the HiRAPS algorithm was 253 pixels per square kilometer, compared to 14 pixels per square kilometer utilizing the PS algorithm. The APS superimposed on the differential interferograms were estimated with both the proposed and PS algorithms. High resolution APS were estimated with the HiRAPS algorithm, whereas only low resolution APS were obtained with the PS algorithm. After estimating and removing estimated APS, the phase stability of APS-free differential interferograms was examined by identifying the permanent scatterers (PS). The final density of identified PS obtained with the HiRAPS algorithm was 453 PS per square kilometer, whereas the density of detected PS using the generic PS algorithm was 381 PS per square kilometer. The maximum difference in the deformation time series between the HiRAPS algorithm and the PS algorithm was less than 6 mm. However, the HiRAPS algorithm resulted in less apparent noise in the time series than the PS algorithm due to the precise estimation of APS. / text

This dissertation is composed of three essays, each examining a unique question relating to the role of intergovernmental transfers in fiscal federalism. Using a panel of the 48 contiguous U.S. states along with recent advances in nonstationary panel and spatial econometric methods this dissertation offers a number of important insights into the workings of intergovernmental transfers and therefore a clearer understanding of the interactions among the different layers of government. The third chapter examines the relationship between intergovernmental revenues from the federal government and intergovernmental expenditures to local governments. As observed by Wildasin (2010), there remains remarkable stability in the ratio of state-tolocal transfers to federal-to-state transfers despite the disparate programs being financed by each. Therefore, the purpose of this essay is to examine the extent to which states serve as a conduit for funds from the federal government to local governments. In particular, the research question asks to what degree do federal transfers stimulate transfers to local governments. The fourth chapter explores the direction of causality between tax revenues and expenditures in answering the four hypotheses set forth in the literature: tax-spend, spend-tax, fiscal synchronization, and institutional separation. Furthermore, along with exploring the role served by intergovernmental transfers within the revenue-expenditure nexus, this essay also examines differences relating to the revenue-expenditure nexus between states with relatively higher debt levels and states with low debt levels, in order to better understand the fiscal causal links favorable for debt accumulation. The purpose of the fifth chapter is to ascertain the effect interstate fiscal interactions on the stimulative effect of grants on state level expenditures. The vast literature on fiscal competition suggests that states do not make decisions in isolation, therefore, spatial econometrics are used to capture spillovers and mimicking behavior across states. Following Boarnet and Glazer (2002), the effect of informational externalities arising from grants awarded to neighboring states are examined as well as the effect of spending spillovers from neighboring states. The results show that the flypaper anomaly (i.e. the stimulative effect of grants greater than a pure income effect) can be explained by interstate fiscal interactions.

Public health planning for influenza is based on morbidity and mortality estimates derived from statistical models. Lower than anticipated 2009 H1N1 pandemic death estimates have raised questions about the method. Examining the statistical method is important for future policy and program development. We compared the main methods of estimating influenza burden through a systematic literature review and by comparing statistical estimates of influenza-attributable burden at the Ottawa Hospital (TOH) to clinical estimates validated through chart review. We identified heterogeneity in methods used to estimate influenza-attributable mortality in the literature which resulted in within-season estimate variation by study. We found statistical estimates of influenza burden at TOH to be 4-8 times greater than clinically validated data. We also found no significant association between the outcomes examined and epidemic periods at TOH. The findings of this study suggest discordance between model estimates by model approach and between model estimates and validated findings. Examining reasons for these discordances should be pursued.

Globally, the tourism industry is recognised as one of the fastest growing industries, generating high revenues and creating a vast number of job opportunities. In South Africa, this is no different and, in recent years, the tourism industry has outshone the country's gold exports therefore claiming its position as the fourth highest earner of foreign exchange to date. Yet the industry is still to receive the attention it deserves from conventional economics. This research aimed to fill this gap in South Africa by providing an understanding on the determinants of international tourism demand for South Africa. The first objective of the study was to provide a broad overview of the tourism industry of South Africa. The discussion focused on the supply and demand sides of tourism which, in turn, are divided into the domestic and international tourism markets. There has been a high growth, especially in the international market since 1994 and, while domestic and international markets continue to grow, seasonality remains an issue. Tourism has a significant impact on economic activity, employment, and the balance of payments and therefore the industry has great potential. The second objective was to create a theoretical understanding on the different factors that could determine the international demand for the tourism product. From this discussion it was found that there are various economic and non-economic factors that are believed to have an influence on tourism demand. Income, prices, transport cost, and the exchange rate are amongst the favourite economic variables with travel time, population, marketing expenditure, climate, and capacity being the more popular noneconomic factors. Among these, certain threats were also identified that could have harmful impacts on tourism growth. The third objective and main aim of the study was to determine which of the factors identified earlier determine the demand for international tourism to South Africa. This was done through an empirical investigation. Data from all the continents were used to attain an international perspective on tourist arrivals (tourism demand). The results indicated that capacity and climate factors determine tourism demand in the short term with income and transport cost influencing South Africa as a tourism destination in the long term. The last objective was to determine whether certain events or disasters that take place globally have a negative influence on tourism demand to South Africa. The event that was looked as was the terror attacks on the United States in September 2001. It was found that although the overall tourism activity of the world became stagnant during this period, the effect was not that considerable in South Africa's tourism arrivals. Tourism in countries such as the United Sates, on the other hand, has still not recovered fully after this event. / Thesis (M.Com. (International Commerce))--North-West University, Potchefstroom Campus, 2007.

The problem of estimating latent or unobserved states of a dynamical system from observed data is studied in this thesis. Approximate filtering methods for discrete time series for a class of nonlinear systems are considered, which, in turn, require sampling from a partially specified discrete distribution. A new algorithm is proposed to sample from partially specified discrete distribution, where the specification is in terms of the first few moments of the distribution. This algorithm generates deterministic sigma points and corresponding probability weights, which match exactly a specified mean vector, a specified covariance matrix, the average of specified marginal skewness and the average of specified marginal kurtosis. Both the deterministic particles and the probability weights are given in closed form and no numerical optimization is required. This algorithm is then used in approximate Bayesian filtering for generation of particles and the associated probability weights which propagate higher order moment information about latent states. This method is extended to generate random sigma points (or particles) and corresponding probability weights that match the same moments. The algorithm is also shown to be useful in scenario generation for financial optimization. For a variety of important distributions, the proposed moment-matching algorithm for generating particles is shown to lead to approximation which is very close to maximum entropy approximation. In a separate, but related contribution to the field of nonlinear state estimation, a closed-form linear minimum variance filter is derived for the systems with stochastic parameter uncertainties. The expressions for eigenvalues of the perturbed filter are derived for comparison with eigenvalues of the unperturbed Kalman filter. Moment-matching approximation is proposed for the nonlinear systems with multiplicative stochastic noise.