Your Project Proposal is your midterm!
It is worth 15% of the grade for this class.
This is your chance to tell me what you've
learned about GIS
You have been assigned chapters to read. Instead
of an in-class or take-home midterm, you will fold this information
into your project proposal. The format is to convince a manager
or agency to fund your analysis of this problem using GIS. They
need to be educated on what GIS is, how it is different from
other methods of analysis, and how you will use it solve your
problem.
Your proposal should include elements from various
chapters, including:
- Purpose of this GIS project and how it compares to
other GIS purposes.
- Model of data you will use (raster, vector or both),
objects types, measurement scales (nominal, ratio,
etc), what type of attribute data you will have, etc.
- Sources of data: projections, conversions, scale
(why does scale matter?). Is all of the data available in
a georeferenced format already? If not how will you georeference
it? What other data could you use? How would you georeference
it? Scanning? Digitizing?
- Analysis of data: some type of analysis should be
performed, there are plenty of examples in the later chapters.
Be sure to justify your choices in each case, ie raster
vs vector... Don't just say "I'm using vector data"!
Your proposal should also include some
background information on the problem, ie Why is this important?
This will be re-used in your final report, so it is time well
spent!
Conciseness and clarity are crucial in this type
of write-up. You've easily got 3-4 pages of material here (I
think...), I would imagine going no higher than 10 pages if
you have a lot of graphics. In general I am impressed
less with bulk and more with clarity.
It has been noted
that diagrams can help a lot. Here's an example.
Have fun, don't stress out...
Here are some potential group projects:
Water Resources and receding
glaciers:
Breeding Bird
Survey (combine with LULC and NLCD)
Glacier database - Predict glacier occurence
from topography (elevation, slope and aspect) and climate data
using Logistic
Regression, glacier data here,
climate data here.
Use random generated points within study area, attribute each
with whether it falls within a glacier or not (1 or 0), multiple
linear regression within Excel to determine coefficients for
each variable (precip, temp, elevation, aspect), multiply rasters
by coefficients and run through equation in PPT.
Carbon Sequestration: Stratigraphic units
have different ability to act as CO2 sink. Plot distributions
of viable candidates with regard to CO2 sources, and transportation
corridors.
Geothermal in Alaska predicted by geochemical
and geophysical properties. Data layers.
Water Quality data in the Metro Region. 10
year study of SWRP
data vs urban change
data
Coastal cores, loess distribution and shelf thickness.
