LECTURE-9+10 ... 06+13 December 2019
Objective: use simple multicriteria ( also called "multifatorial") analysis to decide which area is most suitable for growing MANGOES in Madagascar! This will help the local population to have a source of income and we are there to help them with GIS.
Tutorial: MultiCriteriaAnalysis simple ENG
Hint: Storyline examples of multi-criteria analysis here !
Dataset: madagascar
Available informative spatial layers in the above dataset are 4 raster models and 5 vector models - see file in dataset called data_sources.txt for more information :
- Raster with number of rainy days
- Raster with average rain days
- Raster with Digital Elevation Model (height above sea level)
- Raster with Classification
- Vector with rivers
- Vector with existing farms
- Vector with Country borders
- Vector with province borders
- Vector with temperature values at points
The first thing to do is to define a set of raster that are informative to determine suitability of growing Mangos... surely temperature and rain is important, but many other can be derived - in this excercise we will derive the foollowing new rasters:
- We will derive a distance (proximity) map from rivers - first we have to convert river vector to a raster vector, and then apply the proximity module
- We will derive slope and aspect information from the Digital Elevation Model (DEM) - (Working with Terrain Data)
- We will create a raster with temperature by interpolating (Interpolating Point Data)
- We will use DEM to check that temperatures are inversely correlated with height above sea level (question: what is the temperature range due to height difference in Madagascar?)
After creating the rasters, we will normalize each raster to a scale and then apply weights or thresholds - e.g. if we know Mangoes are not able to grow above 1000 m, we will apply a threshold (mask) to give suitability of "0" to any pixel above 1000 m, using the DEM. This will isolate the red area below and indicate that suitability map will have value 0 there.
