The first requirement for implementing a spatial decisionsupport system is access to data (Chapter 4). Ideally, decision-support systems use distributed GISs so that users can obtain data relevant to their needs, such as framework data and other thematic data (Chapters 5 and 6). A geolibrary is an example of an open distributed system that combines the idea of a traditional library with the resources of the Internet. Geolibraries make geographic data available to those with access to a computer and the Internet (NRC, 1999b).

Distributed geolibraries are global in reach and are part of the concept of the national and global spatial data infrastructures (Chapter 4). Data-sharing, necessary for a distributed system, often is inhibited by a lack of precedent and protocols for sharing among government agencies (EIS-Africa, 2001) and other entities. Fortunately, a number of African countries are creating protocols for data-sharing (e.g., Ghana, Mozambique, Senegal, Uganda, and Zimbabwe) as part of their participation in EIS-Africa (Chapter 4).

Decisions are on a continuum ranging from structured to unstructured. Structured decisions can be solved by computers. They require only manipulation of information and mathematical computations. Unstructured decisions involve human judgments, such as assessing risk or priorities, or human values like determining what is just or fair. These kinds of decisions cannot be made by a computer. Most decision problems fall somewhere between these two extremes and are called semi-structured decisions. Spatial decision-support systems provide computation and analytic power for structured decisions and model alternative solutions for human consideration. Through this process a semi-structured decision is made.

The process of formalizing the development of a decision-support system and related organizational requirements is referred to as “managed decision support.” J. R. Eastman (Clark University, personal communication, 2001) describes a managed decision process for applying geographic information to sustainable development that has three types of decisions: (1) resource allocation decisions, (2) resource status decisions, and (3) policy decisions.

The first step in resource allocation decision-making is the standardization of information including units of measurement and data accuracy. Assuring the accuracy of data is an important next step that adds cost and requires trained staff to collect field data for validation (ground-truthing).

Resource allocation decisions often involve tradeoffs or assessment of risk. These decisions are value questions as well as technical questions. Support for this type of decision includes (1) aggregation and weighting that enable mapping of priority and risk areas and (2) appraisal of options by modeling. Resource allocation decisions are also characterized by multiple objectives, such as food production and disease control. A decision-support system for questions about farming and disease would incorporate data on land cover, population distribution, hydrology, and other factors necessary to analyze and map the risk of disease and food insecurity. A GIS for this purpose would also allow users to analyze tradeoffs and weigh outcomes of alternative plans.

Resource status decisions involve merging routine observations of the status of a resource (e.g., timber, cattle, or fuel) with policy planning and management issues. Decision support requires routine, repeated collection of data on the desired parameters. There is a tendency in GIS technology transfer to focus on static factors, such as political units, elevation and slope, and other framework data sets (Chapter 5). Although these datasets are required for construction of base maps, they do not by themselves support decisions that require assessment of status and changing conditions. Analytical tools are needed to highlight changes.

FEWS (Chapters 3 and 6) is among the most successful demonstrators of resource status decision support in Africa. In FEWS, data come from routine updates of vegetation condition, through the Normalized Difference Vegetation Index (NDVI). The changes in NDVI provide an indication of moisture conditions that gives advance warning of drought. FEWS provides rapid decision support at localized scales using direct observations. Similar observations can be made for drought and fire risk; for example, the Miombo Network (Chapter 3) and programs of the MODIS land science team (Chapter 6).

Although local applications of geographic information are important, environmental effects on a large scale must also be monitored. A continental-scale application of condition and status assessment comes from programs that are developing early warning capability for El Niño¹ prediction. El Niño and land use are linked. Understanding El Niño has important implications for food productivity. Systems that couple large-scale advance warning with current regional or local conditions provide decision-makers with more time to respond.

A challenge for decision support is to assess the future impact of implementing policy options. For example, analysis of future land-cover changes as a result of future land-use distribution requires analytical capabilities that are time and

Front Matter (R1-R14)

Executive Summary (1-10)

1 Introduction (11-17)

2 Agenda 21 Implementation: Progress, Challenges, and the Role of Geographic Information (18-28)

3 Geographic Information Activities in Africa (29-36)

4 Facilitating the Use of Geographic Data: Spatial Data and Telecommunications Infrastructures (37-51)

5 Geographic Data for Sustainable Development I: Framework Data (52-78)

6 Geographic Data for Sustainable Development II: Other Thematic Data (79-103)

7 GIS-Based Decision Support Systems in Africa (104-113)

8 Building Capacity to Apply Geographic Information to Sustainable Development in Africa (114-127)

9 Lessons Learned and Recommendations (128-136)

Appendix A: Biographical Sketches of Committee Members (137-141)

Appendix B: Oral and Written Contributors (142-145)

Appendix C: FGDC Statement (146-147)

Appendix D: Acronyms (148-152)

Appendix E: Glossary (153-155)