The role of the spatial representation in the relation between chronic exposure to NO2 and respiratory health outcomes is studied through a spatial approach encompassing three conceptual components: the geography of susceptibility, the geography of exposure and the geography of risk. A spatially explicit methodology that defined natural neighbourhoods for the city of Ottawa is presented; it became the geography of analysis in this research. A LUR model for Ottawa is developed to study the geography of exposure. Model sensitivity to the spatial representation of population showed that dasymetric population mapping did not provide significant improvements to the LUR model over population at the dissemination block level. However, both the former were significantly better than population represented at the dissemination area. Spatial representation in the geography of exposure was also evaluated by comparing four kriging and cokriging interpolation models to the LUR. Geostatistically derived NO2 concentration maps were weakly correlated with LUR model results. The relationship between mean NO2 concentrations and respiratory health outcomes was assessed within the natural neighbourhoods. We find a statistically significant association between NO2 concentrations and respiratory health outcomes as measured by global bivariate Moran’s I. However, for regression model building, NO2 had to be forced into the model, demonstrating that NO2 is not one of the main contributing variables to respiratory health outcomes in Ottawa. The results point toward the importance of the socioeconomic status on the health condition of individuals. Finally, the role of spatial representation was assessed using three different spatial structures, which also permitted to better understand the role of the modifiable areal unit problem (MAUP) in the study of the relationship between exposure to NO2 and health. The results confirm that NO2 concentration is not a major contributing factor to the respiratory health in Ottawa but clearly demonstrate the implications that the use of opportunistic administrative boundaries can have on results of exposure studies. The effects of the MAUP, the scale effect and the zoning effect, were observed indicating that a spatial structure that embodies the scale of major social processes behind the health condition of individuals should be used when possible.

Time-since-fire data are used to describe wildfire disturbances, the major disturbance type in the Boreal forest, over a landscape. These data can be used to calculate various parameters about wildfire disturbances, such as size, shape and severity. Collecting time-since-fire data is expensive and time consuming; the ability to derive it from existing forest inventory data would result in availability of fire data over larger areas. The objective of this thesis was to explore the use of forest inventory information for the prediction of time-since-fire data in the mixedwood boreal forests of Saskatchewan. Regression models were used to predict time-since-fire from forest inventory variables for each inventory polygon with a stand age. Non-water polygons with no stand age value were assigned values from neighbouring polygons, after splitting long polygons that potentially crossed many historic fire boundaries. This procedure filled gaps that prevented polygons from being grouped together in latter analysis. The predicted time-since-fire ages were used to generate wildfire parameters such as age-class distributions and fire cycle. Three methods were examined to group forest inventory polygons together to predict fire event polygons: simple partitions, hierarchical clustering, and spatially constrained clustering. The predicted fire event polygons were used to generate polygon size distribution wildfire metrics. I found that there was a relationship between time-since-fire and forest inventory variables at this study site, although the relationship was not strong. As expected, the strongest relationship was between the age of trees in a stand as indicated by the inventory and the time-since-fire. This relationship was moderately improved by including tree species composition, harvest modification value, and the ages of the surrounding polygons. Assigning no-age polygons neighbouring values and grouping the forest inventory polygons improved the predicted time-since-fire results when compared spatially to the observed time-since-fire data. However, a satisfactory method of comparing polygon shapes was not found, and the map outputs were highly dependent on the grouping method and parameters used. Overall it was found that forest inventory data did not have sufficient detail and accuracy to be used to derive high quality time-since-fire information.

The main goal of this study is to seek the effects of some risk and prognostic factors contributing to survival of female invasive breast cancer in United States. The study presents the survival analysis for the adult female invasive breast cancer based on the datasets chosen from the Surveillance Epidemiology and End Results (SEER) program of National Cancer Institute (NCI). In this study, the Cox proportional hazard regression model and logistic regression model were employed for statistical analysis. The odds ratios (OR), hazard ratios (HR) and confidence interval (C.I.) were obtained for the risk and prognosis factors. The study results showed that some risk and prognosis factors, such as the demographic factors (race and age), social and family factor (marital status), biomedical factors (tumor size, disease stage, tumor markers and tumor cell differentiation level etc.) and type of treatment patients received had significant effects on survival of the female invasive breast cancer patients.

Housing investment plays a vital role in the real estate market. Although the housing investment has been extensively investigated, the application of Tobin¡¦s Q theory is relatively minor. Hence, the purpose of this study is to apply Tobin¡¦s Q theory to analyze housing investment, using quarterly data for Taipei City from 1973 Q2 to 2010 Q4. The Q ratio numerator is the pre-sale housing price and the denominator represents the value of the rent. The empirical model is estimated by using building permits and use permits as measures of housing investment. Moreover, because the housing market is imperfect, this study applies the threshold regression model to test whether different effects exist in the Q ratio. Finally, this study also compares housing investment in five cities. In conclusion, these findings imply that the Q ratio has a positive relationship with housing investment, as well as a threshold effect. Furthermore, the local housing investments are affected differently by local variables.

Increasing evidence suggests that the environment is related to many public health challenges. Unequal distributions of services and resources needed for healthy lifestyles may contribute to increasing levels of health disparity. However, empirical studies are not sufficient to understand the relationship between health disparity and the built environment. This dissertation examines how health disparity are associated with the built environment and if the environmental conditions that support physical activity and healthy diet are associated with lower health disparity. This research uses a multidisciplinary approach, drawing from urban planning, regional economics and public health. The data came from the Behavioral Risk Factor Surveillance System, and the GIS derived environmental data and the 608-respondent survey data from a larger study conducted in urbanized King County, Washington. Health disparity was measured with the Gini-coefficient, and health status and obesity were used as indicators of health. Hot spot analysis was used to identify the spatial aggregations of high health disparity, and multiple regression models identified the environmental correlates of health disparity. The overall trend showed that disparity has increased in most states in the US over the past decade and the southern states showed the highest disparity levels. Strong spatial autocorrelations were found for disparities, indicating that disparity levels are not equally distributed across different geographic areas. From the multivariate analyses estimating disparity levels, spatial regression models significantly improved the overall model fit compared to the ordinary least-square models. Areas with more supportive built environments for physical activity had lower health disparities, including proximity to downtown (+) and access to parks (+), day care centers (+), offices (+), schools (+), theaters (+), big box shopping centers (-), and libraries (-). Overall results showed that the built environment, compared to the personal factors, was more strongly correlated with health disparities. This study brings attention to the problem of health disparity in the US, and provides evidence supporting the existence of spatial disparity in the environmental support for a healthy lifestyle. Further research is needed to better understand environmental and socioeconomic conditions associated with health disparity among more diverse population groups and in different environmental settings.

Developing sound or reliable statistical models for analyzing vehicle crashes is very important in highway safety studies. A difficulty arises when crash data exhibit overdispersion. Over-dispersion caused by unobserved heterogeneity is a serious problem and has been addressed in a variety ways within the negative binomial (NB) modeling framework. However, the true factors that affect heterogeneity are often unknown to researchers, and failure to accommodate such heterogeneity in the model can undermine the validity of the empirical results. Given the limitations of the NB regression model for addressing over-dispersion of crash data due to heterogeneity, this research examined an alternative model formulation that could be used for capturing heterogeneity through the use of finite mixture regression models. A Finite mixture of Poisson or NB regression models is especially useful when the count data were generated from a heterogeneous population. To evaluate these models, Poisson and NB mixture models were estimated using both simulated and empirical crash datasets, and the results were compared to those from a single NB regression model. For model parameter estimation, a Bayesian approach was adopted, since it provides much richer inference than the maximum likelihood approach. Using simulated datasets, it was shown that the single NB model is biased if the underlying cause of heterogeneity is due to the existence of multiple counting processes. The implications could be poor prediction performance and poor interpretation. Using two empirical datasets, the results demonstrated that a two-component finite mixture of NB regression models (FMNB-2) was quite enough to characterize the uncertainty about the crash occurrence, and it provided more opportunities for interpretation of the dataset which are not available from the standard NB model. Based on the models from the empirical dataset (i.e., FMNB-2 and NB models), their relative performances were also examined in terms of hotspot identification and accident modification factors. Finally, using a simulation study, bias properties of the posterior summary statistics for dispersion parameters in FMNB-2 model were characterized, and the guidelines on the choice of priors and the summary statistics to use were presented for different sample sizes and sample-mean values.

The Company often enlarge its scale to maintain its competitive advantage by investing. When company lacks of internal funds, it will raise funds from outside. The purpose of this study is to explore how company chooses financial instruments and influence of the announcement effect on stock price. This study analyzes Taiwan listed company by the the sample period from 1993 to 2005. There are two parts of the thesis. The first is the factor of choosing certain financial instrument. We use logistic regression model, both binary and multinomial, to figure it out. The second is the influence of the announcement effect has on the stock price. We use event study to find whether abnormal return exists. Conclusion: 1. If the company¡¦s size is larger, it will choose debt to raise funds. 2. If R&D expense relative to net sales, debt ratio, the proportion of intangible asset are higher, the company will be tend to raise funds by choosing convertible bond 3. If the stock price is overvalued, the company will choose stock. 4. Taiwan listed company will experience negative stock return whatever it chooses stock, debt, or convertible bond.

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Previous researches find that advertising expenditure and performance can significantly influence fund flows. With a unique data from Securities Investment Trust and Consulting Association (SITCA) of Taiwan, we can use monthly data of exact purchasing amounts, redemption amounts and advertising expenditures to gain more insight into investors¡¦ investment behavior. We examine the impact of advertising on mutual fund investors¡¦ behavior and the performance-flow relationship. This paper differs from the existing literature, which only concerned with the average advertising effect on fund flow. We follow the procedure of Tsay (1989) time series autoregressive processes model and modify it to cross-section variables threshold model to examine whether threshold effect of advertising on fund flows exists. We generate four empirical results. (1) Performance is significantly associated with higher fund flows. (2) Advertising is significantly associated with higher fund flows. (3) Disposition effect exists in Taiwanese mutual fund market and advertising expenditure can partially enhance the disposition effect. (4) We also measure the threshold effect of advertising on fund flows.

The price of real estate is the result of economical operation and, most importantly, regulation mechanism of resource distribution for real estate industry. Since the process of economic reform began in 1978, there have been several times that the Chinese government imposed contractive measures intended to slow down the economic growth. This paper applies insights from economic theory to explain recent housing price patterns in China’s four largest metropolitan areas. (Beijing, Shanghai, Shenzhen and Guangzhou) and discusses how the Chinese Government’s stance and policy affect the development of real estate. By examining the degree of impact on the housing market as a result of Macro Control Measures, excluding other housing market drivers; the empirical results revealed the degree of effectiveness by the Chinese Government administrative control over the housing market vary across the regions.