This thesis presents a Web-based geographic information system (GIS) application for campus emergency management that allows users to visualize, integrate, and analyze student population, facilities, and hazard data for efficient emergency management of University of North Texas before, during, and after a terrorism event. End-users can locate and search the source area of an event on a digital map from the ArcIMS-based Website. The website displays corresponding population information and attributes of impacted facilities in real time. School officials and first responders including police, firefighters and medical personnel can promptly plan the appropriate rescue and response procedures according to the displayed results. Finally, the thesis outlines the limitations of Web-based GIS in the arena of campus emergency management.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.

This study analyzes the proposed passenger rail line expansion along US Interstate Highway 35 in Denton County, Texas. A multi-dimensional approach was used to investigate potential environmental justice (EJ) consequences from the expansion of the transportation corridor. This study used empirical and historical evidence to identify and prioritize sites for potential EJ concerns. Citizen participation in the decision making process was also evaluated. The findings of this research suggest that the southeast Denton community has the highest potential for environmental justice concerns. This study concludes by offering suggestions for an effective public participation process. These include the incorporation of a community's local history into an environmental justice assessment, and tailoring the public planning process to the demographics and culture of the residents.

The purpose of this study was to determine the effects of attendance at a senior center on the well being and quality of life of grandparents that were rearing grandchildren. Using convenience sampling, grandparents (N=130) who were rearing grandchildren were given a self administered demographic data survey along with an attendance at a senior center questionnaire, the Quality of Life Scale, the Well Being Scale by Liang, the UCLA Loneliness Scale, the Caregiver Burden Scale, and the Role Satisfaction Scale. An initial MANOVA (F 7, 69 = 2.72, p < .01) suggesting that senior center attendance affect the measures as a set was conducted and then a series of one way ANOVAs were carried out to test the hypothesis that attending a senior center has an effect on the dependent variables: well being, quality of life, role satisfaction, caregiver burden, loneliness, current health, and heath one year ago. Subsequently, a hierarchical regression analysis was conducted to find out whether frequency and quality of attendance of a senior center predicted quality of life, caregiver burden, well being, loneliness, and role satisfaction, controlling for the demographic data. The results of the MANOVA showed that the dependent variables: quality of life, caregiver burden, …