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Down to Earth: Geographic Information for Sustainable Development in Africa
FIGURE 6-15 Results of querying the MODIS Land Science Team Fire Web site (NASA, 2002b). The image shows fires (in red) during October 2001 overlaid on a vegetation surface reflectance image (courtesy NASA).
DATA FOR MANAGING HUMAN HEALTH
Human health is a major challenge for African societies and economies. Disease disrupts families, education, and the workforce. Of the 40 million people worldwide who are infected with AIDS or HIV, about 25 million are in Africa, and the number of Africans infected each year from AIDS-related tuberculosis is about 10 million (WHO, 2001). Additionally, there are approximately 110 million clinical cases of malaria worldwide per year, and over 80 percent of these occur in sub-Saharan Africa.
Although raw numbers on disease incidence are valuable to decision-makers, the distribution and rate of diffusion of disease relates to complex interactions among multiple factors, many of which are geographic (e.g., climate, vegetation, topography, elevation, demography, poverty). A geographic information system facilitates the integration and analysis of these diverse data layers (e.g., Box 6-3) and planning for distribution of medical supplies, assistance, and food. GIS also is a tool for addressing the spread of diseases.
Many of the factors that influence the spread of disease can be mapped using remotely sensed data. NASA’s Center for Health Applications of Aerospace Related Technologies (CHAART) generates data that illustrate links between disease and such factors as vegetation that can be remotely sensed (Table 6-4). CHAART evaluates existing and planned remote sensor systems enabling human health scientists to determine relevant data for epidemiological, entomological, and ecological research. It also develops remote-sensing-based models of disease transmission risk (Beck et al., 2000). CHAART conducts several research projects in Africa that apply remotely sensed data (mainly from Landsat Thematic Mapper images) to monitor and predict disease (e.g., Table 6-5).
People are using remotely sensed images and data in GISs for monitoring and evaluating factors associated with disease. They are using satellite instruments for mapping, sur-