space sensitive. Many predictive land-use and cover-change models are simple extrapolations from trends in variables such as estimates of population growth. Policy decision-making requires the analytic and modeling capabilities of GIS.

Many policy decisions require spatial data and analysis of overlapping sets of data layers. Necessary data analysis includes computer functions that merge various layers to produce a synthetic layer. For example, soil erosion risk maps are produced by merging land use, slope, and soil information.

Geographic information technologies are used in African countries and elsewhere, but are rarely used in routine support of policy-making, natural resource management, or planning (e.g., EIS-Africa, 2001). Five examples illustrating different aspects of implementation of decision-support systems in Africa at scales ranging from the continental to the community level follow:

The Mapping Malaria Risk in Africa (MARA) project maps malaria risk using in situ data on malaria occurrence in combination with spatial modeling to predict the geographic distribution, seasonality, and endemicity (peculiarity to a locality or region) of the disease. The project uses the GIS to evaluate location and risk and to disseminate information to national and international decision-makers.

The MARA project is a federated network of scientists throughout Africa who are mapping malaria risk at the district level. Five regional data collectors are responsible for obtaining malaria datasets from neighboring countries. Stratified risk maps of the type and severity of malaria transmission are produced from geographic data on demography, climate, elevation, ecological zone, vector distribution, and malaria endemicity.

MARA uses continent-wide datasets (e.g., land cover, elevation, biotype) in addition to local precipitation and temperature data. This combination of information from local and continental-level sources presents challenges for accuracy and compatibility, as does the need to organize data on an administrative district level. Health authorities in each country rank each district in terms of the severity of the malaria risk. This derived product of risk-ranking assessment is then mapped in the GIS and provided to health officials. MARA provided the first continental maps of malaria distribution and the first quantitative “burden of disease” estimates. Maps produced by MARA are widely used for planning, intervention, and prevention. In the committee’s opinion, this simple decision-support system is effective because it relies on low-cost data, operates on a routine basis, and involves a broad network of African institutions and scientists.

FEWS NET is a network of 17 African countries working with partners to address food security issues (Chapter 3). FEWS NET operates on the principle that gradually unfolding natural disasters influencing food security give decision-makers time to prepare and take preventive action.

A range of data and information sources is used by FEWS NET including continent-wide, 10-day NDVI (Chapter 6) and rainfall estimates from NOAA and European satellites, ground-based meteorological data, data on crop and rangeland conditions, commodity pricing data and agricultural production data (Chopak, 2000). FEWS NET handles a large volume of data, and has developed automated processing and analysis tools for routine operations.

Food-security analysis is broken into five assessment activities: a start-of-season assessment, a preliminary crop forecast, an annual food balance sheet, a harvest assessment, and a current vulnerability assessment (Chopak, 2000). After the analysis stage, information is disseminated to decision-makers. Monthly updates for all member countries are posted on the FEWS NET website (FEWS NET, 2002). Additionally, memoranda are issued to warn of developing food security issues (e.g., Box 7-2).

The USGS provides technical support to FEWS NET partners in the use of remote-sensing and GIS and develops data-processing and analysis tools. Additionally, USGS as-sists with data archival and dissemination.

The Miombo Network was formed to create a regional network for environmental research on the dominant biome² of southern Africa, the Miombo Woodlands (Chapter 3). It is an informal network of scientists funded through grants and contracts from donor and science agencies.

One of the important environmental threats in the region is fire. The Miombo Network has developed a remote-sensing approach to fire detection and mapping using the MODIS sensor onboard NASA’s Terra satellite (Chapters 5 and 6). MODIS data are retrieved and provided on an Internet-based interactive GIS, which shows the location of fires and the underlying vegetation cover.

Remotely sensed vegetation maps are incorporated into the GIS and merged with in situ data to derive fuel loads, which are then mapped and merged with fire location. The