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Mini-Project # 4, Winter 2023
Due Date: 03/07/2023 11:59PM (Pacific Time)
Total Possible Points (Non-Extra Credit): 40 Pts
This project will focus on suitability analysis with raster data. Your tasks will be both
conceptual-level and technical.
At the conceptual level, you will define a suitability model of your choice, for an area of
your choice. See https://en.wikipedia.org/wiki/Suitability_model for a brief description of
what a suitability model is. For example, you may be looking for best areas for
community gardens: these are often in underutilized land in residential land uses, with
good soils, accessible (not steep slope), etc. In this example, you would be looking for
areas with a specific type of land use/land cover, with an appropriate range of values of
slope, etc. You may build additional criteria based on a range of precipitaiton values,
whether the area is affected by wildfires, has mild temperatures, low levels of soil
erosion, has vegeration (eg derived from satellite imagery using NDVI), etc. Feel free to
use the imagery layers we explored or mentioned during the raster-focused lectures.
One of the cells in the last raster notebook contained a list of about a dozen such layers
available through AGOL - but feel free to find more. Be creative!
You can use any two of the map combination techniques discussed in the lecture. You
should clearly identify the map combination techniques you use, and discuss any
uncertainty issues associated with these specific techniques.
As the outcome of this first part, you will need to a) describe the suitability model you
want to develop; b) identify the raster data layers you will use; and c) describe two of the
map combination techniques you will use to derive the two suitablity maps, and their
pros and cons. In your model you will need to combine at least 3 raster data layers.
The second part will involve implementing your suitability model using arcgis.raster
functions. For this exercise, we'll mostly use local functions, and possibly focal and zonal
functions. Examples of what works are in the lecture notebooks.
The third part will be a brief write-up comparing the two output rasters generated for
your suitability model using the two map combination techniques.
The notebook should include documentation of the steps, as usually.
Names:
IDs:
1. Formulate a suitability model (markdown, about 100 words)
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5. Name the two map combination techniques you will use to combine the data and
describe their pros and cons (markdown)
In [ ]: # 2. Imports, etc.
In [ ]: # 3. List imagery layers to be used in your model. This cell should contian laye
# Include at least three initial raster sources.
In [ ]:
In [ ]: # 4. Derive the area of interest (AOI) and its geometry and extent.
# The smaller the area the better (so that you don't run into raster size limita
In [ ]:
In [ ]: # 6. Prepare your input layers for map combination:
# clip to AOI, remap/normalize, add color map, visualize the layers and legends.
In [ ]:
In [ ]: # 7a. Generate a composite raster layer for your first map combination technique
# name of the technique (refer to lecture slides):
In [ ]:
In [ ]: # 7b. Generate a composite raster layer for your second map combination techniqu
# name of the technique (refer to lecture slides):
In [ ]:
In [ ]: # 8. Compare the results, and describe how the different map combination techniq
#
In [ ]:
In [ ]: # Please let us know how much time you spent on this project, in hours:
# (we will only examine distributions and won't look at individual responses)
assignment_timespent =
extracredit_timespent =
In [ ]: