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Down to Earth: Geographic Information for Sustainable Development in Africa
Geographic Information Systems
Geographic information systems provide an excellent medium for data integration and a basis for a spatial decision-support system (Cowen, 1988). A GIS supports decision-making by providing ways to examine and choose among alternative solutions, and takes decision-makers beyond the point of simply possessing data, information, and knowledge.
The concept of a decision-support system dates to the late 1950s (e.g., Simon, 1977), and systems using geographic data emerged in the last 10 years and subsequently grew dramatically (Densham and Goodchild, 1989; NCGIA, 1990, 1996). By 2000 over $900 million had been spent on GIS software, and GIS-related services had generated $7 billion in revenue. The annual growth rate of the GIS industry is now 15 to 20 percent, and there are over 2 million GIS users worldwide (Daratech, 2001).
Still, little of the growth in the GIS industry has taken place in Africa. Increased use of GIS in African countries depends on effective demand for geographic information and tools, and on technology cooperation with other countries. Lessons learned from past efforts to transfer technology from developed to developing countries are (Schmidheiny, 1998)
technology ought to be appropriate to serve user needs.
people should be educated and trained in the use of the technology.
technology should suit local conditions (e.g., climate, energy availability, customs).
technology should be transferred over the long term.
GIS technology is available at a variety of technology levels, or scales of implementation, from advanced systems using considerable computational power and large datasets to use of paper maps and GPS. The level of technology should be appropriate to its intended use. Hence, this report encourages the use of geographic information—not GIS technologies per se—to support decision-making, regardless of its level of technological advancement.
DECISION-MAKING AND GEOGRAPHIC INFORMATION
Decision-makers in African countries need data and tools to monitor and assess natural resource inventories and environmental and social change. These data and tools are also needed to predict scenarios (e.g., trends and needs for land and food), determine critical information needs, evaluate data quality, and identify data gaps. Entities ranging from governments to NGOs to farmers can use information from decision-support systems to reduce the impact of global change on human well-being and the environment. Needs and priorities vary among these entities. Therefore, decisions about sustainable development often involve compromises and trade-offs (e.g., setting aside land for wildlife protection versus land for farming, or deciding how much water from a river should be diverted to farming as opposed to industry or housing), and competing demands complicate the decision-making process.
From GIS to Decision-Support
A GIS aids the decision-making process by integrating and displaying data in an understandable form. Furthermore, a GIS is used to analyze relationships among different kinds of data (e.g., environmental and health data). The fundamental analytical functions of a GIS-based spatial decision-support system include (1) query analysis, (2) proximity or buffer analysis, (3) overlay analysis, (4) neighborhood analysis, (5) network analysis, and (6) modeling (Box 7-1). Various combinations of these functions are commonly used during the geographic data analysis process.
GIS is not an end in itself, however, but provides a valuable foundation for further analysis. A spatial decision-support system can be based on the primary functions of a GIS, but these basic functions need enhancements for analysis and modeling. For instance, for food-security analysis it is possible to link a GIS to a model that predicts grain yields from a range of spatial input data, such as soils, climate, and topography. This model can be linked to economic and demographic models showing where people live and the grain demand from these settlements. The combination of basic data, yield modeling, and human demand and location analysis provides a way to evaluate food security. Hence, using a spatial decision-support system is not simply a descriptive exercise. The desired outcome is not how the world looks, but instead how the world works.
A critical feature of a spatial decision-support system is its emphasis on linking data with analysis tools. Some analyses use spatial analysis functions often referred to as GIS modeling in which several data layers are merged to create a new synthetic layer. This is often the approach for risk assessment. For example, various habitat, human population, and climate data layers can be merged to provide a vector-borne disease risk map as a product (e.g., Chapter 6). This product can be updated rapidly and often is a means to marshal scarce resources.
Spatial decision-support systems also can involve numerical models, including forecast models that evaluate through simulation in map form various alternative scenarios based on different policy options. This type of decision-support usually is deployed for planning purposes. It can be useful as a way to integrate multiple planning objectives, or competing options for the use of a specific natural resource, such as land, or particular location, such as a watershed.
Lastly, a spatial decision-support system is indeed for support to decision-making—it does not make decisions by