A GIS Case Study in Africa¶
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The time has come to run through an actual example of how the analysis and integration of thematic data, shown in the form of simple maps, will by appropriate combinations and numerical evaluation produce one of the most common outputs in a GIS study - a site suitability map. The IDRISI image processing and GIS analysis program is used to create this end result. The actual task is to pick the best location for growing sorghum (a crop) along the Senegal River in Africa. This is displayed in a map called BESTSORG.
A GIS Case Study in Africa¶
We extracted (with permission) the following from an example in the Database Query section of the Tutorial Exercises in the IDRISI for Windows Users Guide. This simplified example contains only a few of the many data elements that we normally consider using in the model. It does illustrate the workings and rationale underlying a GIS in operation. To manipulate data easily, it also relies on a different ranking mode within each element, which reduces to acceptable (1) versus not-acceptable (0).
The problem is identifying the best areas for planting a specific crop within a valley along the north side of the Senegal River at the border between Mauritania and Senegal, two West African nations along the Atlantic Ocean. The cereal crop is sorghum, a tall grass cultivated in tropical climates that yields grains suited as silage and fodder or, in some varieties, a cane sugar-like juice called sorgo. Many of the factors in the list on the second page of this section, pertaining to agricultural siting, apply in this case as well, but here, we consider only key ones, such as, soil types, relief, and periodic flooding (which promotes sorghum growth).
(NOTE: In the graphics that illustrate these steps, we leave off the legend created by the IDRISI program, because there is no provision to copy it when the we convert the image to a “.tif” format. We identify the category color(s) for each graphic in the text.)
(The sequence of increasing elevations, as color-coded is: blue (lowest, associated with the river), medium gray, purple-red, dark green, medium green, olive, yellow, tan, brown, red, darker gray, and black.)
` <>`__15-9: There seems to be something strange about this elevation map. The river is the lowest part of the scene, yet it appears to back up against the medium green pattern. What gives? `ANSWER <Sect15_answers.html#15-9>`__
` <>`__15-10: Why is the area beyond the river flooded where it is in the pattern map? `ANSWER <Sect15_answers.html#15-10>`__
As the flow chart indicates, the final step in the suitability process is to combine the information in the FLOOD and BESTSOIL maps as an OVERLAY to generate a decision end product map, called BESTSORG. Again, we require binary logic using Boolean algebra. On each of the intermediate maps, there are just two themes or patterns, one in color and the other in black, representing the idea of “good” or “appropriate” (flooded, optimum soil) and “not suitable” respectively. Let “acceptable” = 1, and “not suitable” = 0.
FLOOD |
BESTSOIL |
BESTSORG |
||
0 |
X |
0 |
= |
0 |
0 |
X |
1 |
= |
0 |
1 |
X |
0 |
= |
0 |
1 |
X |
1 |
= |
1 |
Since we know the area of each cell, the total area associated with suitable conditions is just the product of the number of cells times the unit area. In IDRISI, the two programs GROUP and AREA do this.
` <>`__15-11: Examine this BESTSORG map carefully. What is controlling the distribution pattern of the red that represents the optimum crop growth conditions? `ANSWER <Sect15_answers.html#15-11>`__
Of course, we would include many other attributes and thematic factors in a real suitability case, as for example: Access (roads, etc.), Market Requirements, Fertilizer Needs, Manpower Availability, etc. We express some of these factors as maps and others as data that we may organize into value tables. We can integrate these data in GIS models that add to the scores in each data cell. And, as we showed in the Site Suitability illustration above, we could apply a different system of ranking, in which we associate a range of rankings, say, from 1 to 10 (rather than just 0s and 1s), with each cell, and then sum them for all the data elements.