. "4 Facilitating the Use of Geographic Data: Spatial Data and Telecommunications Infrastructures." Down to Earth: Geographical Information for Sustainable Development in Africa. Washington, DC: The National Academies Press, 2002.
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Down to Earth: Geographic Information for Sustainable Development in Africa
FIGURE 4-1 (Read from the bottom to the top.) A spatial data infrastructure (SDI) typically consists of framework foundation data such as geodetic control, digital elevation and bathymetry, and ortho-imagery. Because of the central nature of people in sustainable development, data on human population distribution are equally important. Where possible, SDIs also contain essential framework thematic data layers, including hydrology, political and other boundaries, transportation resources, and cadastral information. Other thematic information such as socioeconomic data, vegetation, soils, geology, and land cover may be included in the infrastructure (adapted from FGDC, 2002).
An ortho-image is a specially processed image prepared from an aerial photograph or remotely sensed image that has the metric qualities of a traditional line map with the detail of an aerial image.4 Because ortho-images are geographically referenced, they are useful in their own right or as a backdrop upon which other information can be overlaid (e.g., drainage or road networks, utilities, or government boundaries). They also can be used as a reference base map to which other maps or images can be linked to detect changes in the landscape.
Human Population Distribution
Human population distribution refers to the location of people on Earth’s surface. People are both influenced by and have an impact on ecosystems in which they live, and are therefore central to Agenda 21 issues. Information on the geographic distribution of the human population and their attributes are equally as important as other SDI framework foundation data. In the current worldwide development arena, such key issues as good governance, anti-poverty strategies, and the need to promote economic growth with social equity all require population distribution and other demographic data at the local scale. Geographically referenced, standardized census data that can be linked to other layers of geographic data are required to meet national development needs. Progress toward Agenda 21 goals is impeded by the lack of reliable data on human population distribution.
Framework Thematic Data
There is general consensus in the geographic information community that four of the most important framework thematic datasets are Hydrology, Boundaries, Transportation, and Cadastral data (NRC, 1995, 2001; NSDI, 1997).
There are three categories of hydrologic features: (1) surface water features (e.g., oceans, seas, lakes, reservoirs, and ponds), (2) linear features (e.g., shorelines, rivers, canals, and perennial and intermittent streams), and (3) point features (e.g., wells). A complete hydrologic dataset requires information about how the hydrologic network is connected and the direction in which water flows.
Boundaries range from the political borders of countries to administrative units to communal and individual holdings. Without accurate boundary information it is difficult to monitor an activity with a given legal jurisdiction or allocate resources fairly to people within a specific administrative district. Box 4-4 describes the importance of boundary information in Ghana.
Transportation networks include roads, railways, waterways, and pipelines. Even in major cities of Africa they are inadequately mapped for such basic functions as delivery and collection services (ECA, 2001).
Cadastral data refer to the geographic extent of past, current, and future rights and interests of private and commercial property (FGDC, 2002). A cadastre is a map accompanied by a register showing the ownership or possession of individual units of land. It facilitates efficient land adminis-
During processing of the source data, an ortho-image is adjusted (rectified) to a standard map projection and datum. Geometric errors caused by topography and other anomalies are removed from the dataset during processing (Thrower and Jensen, 1976).