Ghana is identified among the developing countries with high maternal mortality ratio in Africa. This study unpacked the Demographic and Health Survey data by examining the maternal health indicators at the district level using GIS methods. Understanding the geographic patterns of antenatal care, place of delivery, and skilled birth attendants at the small scale will help to formulate and plan for location-specific health interventions that can improve maternal health care behavior among Ghanaian women. Districts with high rates and low rates were identified. Place of residence, Gini-Coefficient, wealth status, internet access, and religious affiliation were used to explore the underlying factors associated with the observed patterns. Economic inequality was positively associated with increased use of maternal health care services. The ongoing free maternal health policy serves as a cushion effect for the economic inequality among the districts in the Northern areas. Home delivery is common among the rural districts and is more prominent mostly in the western part of Northern Region and southwest of Upper West. Educating women about the free maternal health policy remains the most viable strategy for positive maternal health outcomes and in reducing MMR in Ghana.

Understanding spatial perspectives on the spread and incidence of a disease is invaluable for public health planning and intervention. Choropleth maps are commonly used to provide an abstraction of disease risk across geographic space. These maps are derived from aggregated population counts that are known to be affected by the small numbers problem. Kernel density estimation methods account for this problem by producing risk estimates that are based on aggregations of approximately equal population sizes. However, the process of aggregation often combines data from areas with non-uniform spatial and population characteristics. This thesis presents a new method to aggregate space in ways that are sensitive to their underlying risk factors. Such maps will enable better public health practice and intervention by enhancing our ability to understand the spatial processes that result in disparate health outcomes.

Disease prevalence varies with geographic location. This research pursues a medical geographic perspective and examines the spatial variations in disease patterns between Obuasi, a gold mining town and Asankrangwa, a non gold mining town in Ghana, West Africa. Political ecology/economy and the human ecology frameworks are used to explain the prevalence of diseases. Mining alters the environment and allows disease causing pathogens and vectors to survive more freely than in other similar environments. Certain diseases such as upper respiratory tract infections, ear infections, sexually transmitted diseases such as HIV/AIDS and syphilis, certain skin diseases and rheumatism and joint pains may have a higher prevalence in Obuasi when compared to Asankrangwa due to the mining in Obuasi.

Businesses cluster to achieve agglomeration benefits. However, research in developing countries suggests that the economic environment limits small business’ propensity to benefit from agglomerations. The study examines the location, networking patterns, formal structures and owner characteristics of 1256 micro businesses from ten industries and thirteen sample areas in Toluca, Mexico. First, the thesis analyses whether clustering has a positive impact on the success rates of the surveyed enterprises, e.g. higher sales per employee. On an industry scale only Retail benefits from agglomerations economies. However, results of the neighborhood data show that specific areas benefit from urbanization economies. Overall, the study finds that businesses located within agglomerations, have higher levels of formalization, networking and professional training, hence constituting a more sophisticated base for economic development. Conclusions can be drawn for development policies and programs, arguing for a more differentiated approach of small business development depending on business location and cluster characteristics.

This paper presents methods and results of county-level population estimation using Landsat Thematic Mapper (TM) images of Denton County and Collin County in Texas. Landsat TM images acquired in March 2000 were classified into residential and non-residential classes using maximum likelihood classification and knowledge-based classification methods. Accuracy assessment results from the classified image produced using knowledge-based classification and traditional supervised classification (maximum likelihood classification) methods suggest that knowledge-based classification is more effective than traditional supervised classification methods. Furthermore, using randomly selected samples of census block groups, ordinary least squares (OLS) and geographically weighted regression (GWR) models were created for total population estimation. The overall accuracy of the models is over 96% at the county level. The results also suggest that underestimation normally occurs in block groups with high population density, whereas overestimation occurs in block groups with low population density.

ArcView and ArcInfo were used to identify and delineate areas contaminated by fluoride, sulfate, chloride, and nitrate in the Woodbine Aquifer. Water analysis data were obtained from the TWDB from the 1950s to 1990s covering 9 counties. 1990s land use data were obtained to determine the relationship with each contaminant. Spearman's rank correlation coefficients and Kruskal-Wallis tests were used to calculate relationships between variables. Land uses had little effect on distributions of contaminants. Sulfate and fluoride levels were most problematic in the aquifer. Depth and lithology controlled the distributions of each contaminant. Nitrate patterns were controlled mainly by land use rather than geology, but were below the maximum contaminant level. In general, contaminant concentrations have decreased since the 1950s.