Thesis (D.A.)--George Mason University, 2009. / Vita: p. 343. Thesis director: Allan Falconer. Submitted in partial fulfillment of the requirements for the degree of Doctor of Arts in Community College Education. Title from PDF t.p. (viewed June 10, 2009). Includes bibliographical references (p. 334-342). Also issued in print.

Thesis (Ph. D.)--University of Pennsylvania, 1907. / Various pagings.

Thesis (PH. D.)--University of Pennsylvania, 1907. / Various pagings. Also available in digital form on the Internet Archive Web site.

Slope stability maps are useful in seismic hazard evaluation, land use and emergency planning, and geomorphological studies. Such maps were generated for Giles County, Virginia (approximately 1000 square km) by the synthesis of data sets for topographic slope, surficial deposits, geologic structure, and seismic slope response. Both graphical and digital techniques were used to generate a topographic slope map. Relevant limiting slope angles according to seismic shaking and land movement studies are <15, 15-35, and >35 degrees. These slope categories were integrated with geologic factors (presence/absence of colluvium; dip direction of bedrock) to develop a set of 12 slope stability categories tailored to the Appalachian Valley and Ridge geologic/topographic province. The techniques developed are directly applicable anywhere in the Appalachian Valley and Ridge province and can be adapted to other similar physiographic provinces. Both the slope map and the regional slope stability map can be produced graphically or digitally. The graphical method is inexpensive and fast when used to examine the detailed slope stability of a small area (7.5 minute quadrangle or less about 150 square km). The computer method but is more cost-efficient than the graphical method when studying the slope stability of larger areas. The location and type of slope movement triggered by seismic shaking, torrential rainfall, rapid snowmelt, or human activities can be estimated with the use of a regional slope stability map. Integration of the slope stability map with additional factors (such as joint pattern, seismic intensity attenuation pattern, or the local water table) results in a more detailed map for specific site studies. / M.S.

Geographic utilization patterns of nursing homes were examined by comparing patient origin with facility location. The 1985 Patient Origin Study produced by the Virginia Department of Health was used to see if there are predictable geographic patterns of patient migration to nursing homes. A random sample of thirty nursing homes was taken from all facilities in Virginia in the 1985 study. The migration data were used to determine median migration distances, to investigate decreases in utilization with distance from a facility, and to distinguish spatial markets of nursing homes. Comparisons were made between urban and rural facilities to see how factors of limited availability and children-as-decision makers affect the migration patterns. The findings show that there is a distinct pattern of decreasing utilization of nursing homes with distance from the facility. Both urban and rural nursing homes primarily serve the immediate environment in which they are located. Maps of patient origins show that there are service areas for nursing homes which are modified by population distribution and physical geography. General findings show that location is crucial for nursing home utilization. The existence of a facility generates need awareness and utilization throughout the immediate population. On the average, twenty-six percent of the patients at a nursing home come from the same zip code in which the nursing home is located. More than half the patients come from within eight miles of a facility. Eighty-three percent of the patients come from within 26 miles. The ratio of out-of-region migration to urban nursing homes is significantly higher than that for rural nursing homes. The results of this thesis can be used to predict utilization patterns of nursing homes. The findings also have implications for Medicaid budgeting because they show geographic, demographic, and economic factors which affect nursing home utilization rates. / Master of Science