Due at midnight on Friday, 17 March 2017
NOTE: We weren't kidding. This is a REALLY complicated assignment! Read it through, CAREFULLY, all the way to the end, before you start working on it!
This assignment is worth 20 points—5 points each for your map and data, and 10 points for your model.
A Bren School group project (Boland et al. 2005) developed a multicriteria analysis (MCA) framework to evaluate conservation potential of land in Ventura County based on viewshed, habitat, and public access criteria. In this assignment, we will use a simplified version of their approach to evaluate the conservation potential of lands in the southern region of Santa Barbara County.
Your task is to advise a land trust on which watersheds have the best overall conservation potential. The land trust wants to conduct conservation efforts in watersheds that would help preserve both riparian habitat and scenic viewsheds through conservation easement purchases on inexpensive, but developable land. You will use an MCA framework to produce your results.
The following data come from a variety of sources, and are
available in the
geodatabase. Except where indicated, we've projected these
data into the Teale Albers (NAD 1983) projection and clipped them
to our Region of Interest (ROI), which is a collection of
watersheds in the southern region of Santa Barbara County.
Parcels.NET_AV(in $) divided by
Parcels.Shape_Area(in m2). Note that if multiple parcels are located within a cell, then use the parcel that has the largest area within that cell.
In Landsat imagery, Band 3 is red, Band 4 is near-infrared (NIR), and band values are 8-bit unsigned integers (0 to 255). Once the band values have been converted to floating-point numbers (0.0 to 255.0), the NDVI formula is:
NIR - red / NIR + red
which yields a floating-point number between −1.0 and 1.0.
Use a raster-based index model, or "scoring" approach to perform an MCA that will prioritize watersheds based on your client's criteria.
All MCAs follow four basic steps:
Your task to develop an MCA model that will implement each of these steps (Figures 1 & 2).
Figure 1: A conceptual model for the complete analysis. Your task is to replace each of these "Run" tools with the geoprocessing workflow you develop for that step. To simplify your task, please use a single model for all of your workflows.
Figure 2: Example workflow for scoring the watersheds in the "Run MCA" step.
The output of your model will be a
that has a score for each watershed. The last step in Figure 2 is
to use the Zonal Statistics by Table tool to generate this
CALWNUM as the unique key for the
watersheds. (You can use the output table's
field for the watershed's score.)
Before you start your analysis, download the
file and setup your project with a project folder (
and a map document (
file includes, in addition to the usual geodatabase, a Python
CalculateQuantiles.py) and a toolbox with a
pre-loaded link to that script. Note that you must have the actual
Python script and the link in your toolbox to properly use this
tool. In the HW4 toolbox, create a new model in which you'll
perform all of your analyses.
Set your model properties as follows:
When designing the geoprocessing workflow for each step of your analysis, we recommend using these ArcToolbox tools (note: steps may use tools more than once):
NOTE: Calculate Quantiles is not a standard ArcMap tool, but is an external script. You can tell ArcMap to calculate quantile breaks for a given attribute field by manually going through the "Classify..." option in the Reclassify tool. However, this quantile calculation cannot be automated in Model Builder, so requires user interaction (i.e., if your input dataset changes, you have to go back into the Reclassify tool and tell ArcMap to reset the class breaks to the new quantiles). The included Python script automates quantile calculations and does not require user interaction after initial parameter setup. Calculate Quantiles can be dragged from the toolbox into Model Builder and used just like a regular ArcMap tool. It will generate a reclassification table that can feed into the Reclassify tool.
See "Homework 4 notes" for more tips
about specific tools.
Create a user interface for your model by designating model environment settings, input datasets, output table, reclass tables, and score weights as model parameters. Specifically, set the following as model parameters (variable type in parentheses):
* Asterisked parameters will need to be set by creating new variables (see: Setting model parameters). Non asterisked parameters can be set by simply right-clicking an object already in your model and setting to Model Parameter.
Design a map that communicates your results, and clearly identifies your prioritization of watersheds. Also, include a brief (50–100 words max) description of your analysis on your map.
You will upload three files: your map, your model, and your results data:
HW4Lastname.tbxand make sure it contains a single, run-ready model.
Scorestable into a dBASE table with only two fields:
||Text||CalWater watershed identification code (Interagency)|
||Float||watershed score from your model, where 1 ≤ score ≤ 5|
To do this, use the Table to Table tool with these parameters:
MEANfields, and then rename your