Samantha Weaver
12/2/11
UEP 232: Intro to GIS
Assignment 7: Project Process Steps
Project Summary:
I plan to spatially examine the feasibility of non-fossil fuel derived base-load power in Montana.
Specifically, I will conduct a suitability analysis for geothermal facility siting in Montana. Geothermal
energy is a source of base-load power, and thus could be a viable alternative to coal generation in the
state. Montana’s primary source of electricity is currently coal.
The goal of this project is to examine suitability for geothermal facility siting using a GIS overlay analysis
in the context of factors beyond resource availability. Because the availability of geothermal energy
does not always imply its accessibility, additional spatial analysis of factors such as land use,
transmission line locations, and electricity demand centers is necessary. Data layers that I will use for
this analysis include geothermal resource potential, transmission line locations, population centers,
topography, road access, protected wilderness areas and areas of environmental concern, and flood
plain locations.
Data Layers:
Preliminary Steps Related to Data Preparation and Transformation:
1. Geothermal data: Geothermal resources data will need to be converted to a shapefile. (I did this
already and it worked!)
2. Transmission data: Transmission line data are for the entire country. I will need to select
transmission lines in Montana using the ‘select by location’ function and create a new shapefile
with just these features. (I already did this, too.)
3. Population Data: I am interested in which regions have the greatest energy demand, so I will
create a new shapefile containing the area of the ten most-populated towns (in terms of total
population) in Montana.
4. Elevation: In previous research, regions with slopes greater than 30% have been considered too
steep for geothermal development. I will need to create a data layer for topography/elevation
that shows the percent slope. I can do this using the Spatial Analyst Surface tool, Slope. I just
need to make sure to output the raster in ‘percent rise’. Then, I will need to select only layers
with slopes less than 30%, and create a new shapefile with that dataset.
5. Flood zones: I have maps for each county, but I want information for the entire state. I will need
to load all the county maps together and create a new data layer that contains all of the county
files.
6. Finally, it will be very important to ensure that all of these layers are in the same coordinate
system.
Processing Extent, Cell Size, and Snap Raster
The processing extent and snap raster for this analysis will be the DEM base map. This means that all of
the other raster layers I create will snap to the elevation data set. I will set the cell size to 90 (even
though 10 is what we commonly use in class) because this is an analysis of a relatively large area.
Overlay Analysis Basic Methodology
I will use the weighted overlay tool to create a rating overlay for geothermal development. The flow
diagram below shows how the process might work. I have also included a list of suitability criteria.
Suitability Criteria:
- Intersecting areas with geothermal resources
- Close to existing transmission lines
- Close to population centers
- Intersecting terrain with less than 30% slope
- Close to major roads
- Not in wilderness areas
- Not in a flood plain
Site Suitability
(5 = Good, 1 = Bad)
Figure 1. Project Flow Diagram
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Derived Maps
As an interim step in the rating overlay process, I will need to use the Spatial Analyst Euclidean Distance
tool to show how far a given raster cell in each of the following base maps: transmission lines, roads,
population centers, proven geothermal resources, wilderness areas and flood zones.
In my preliminary steps, I also created a data layer for topography/elevation that shows the percent
slope. This is also included under “derived maps” in the flow diagram above.
Interpreted Maps (Reclassification and Preference grids)
Now I will need to create preference grids for each of the distance data sets I have derived. I will
reclassify (Spatial Analyst tools – Reclass – Reclassify) the values into 5 groups with values 1-5 assigned,
where 1 indicates an area that is not suitable and 5 is most suitable. The figure above shows the criteria
according to which each data layer will be reclassified.
Site Suitability using Raster Calculator
Finally, I will use the raster calculator to combine each of the preference grids I have created. Each layer
will be weighted relative to the others, with proximity to geothermal resources, transmission lines, and
flood zones being the most important. Regions with the highest ‘score’ (40 points are possible) will
represent the most suitable areas for geothermal project development.
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