Geoprocessing in Arc

This week in GIS programming we used model builder and PythonWin to make tools. Above is a screenshot of the results of a tool I put together in model builder. It is a basin with soils data in which I have excluded the areas of poor soils from the polygons.

Apps in GIS Lahar mapping

 

This week in Applications in GIS we worked with vector and raster data in the area of Mt. Hood Oregon. First we manipulated raster elevation data to reflect "streams" or areas of possible lahar inundation. This was a process that took many steps but the outcome was a  vector file, a vector "stream" file from raster elevation data.....I thought that was pretty impressive.  We were then able to relate the possible lahar inundation areas to populated areas.

GIS Programming: Intro to Python

This week we went over Python scripting language and ran our first script (ran, not created) and I must say it was quite a useful one. It was a script that created organized folders and subfolders which we will be working from and adding data to all semester. Talk about an assignment that starts you off on the right foot…..Thank you!!

How it was ran:

1.I copied the folder from my R drive to my S drive.

2.I then right clicked on the file “CreateModuleFolders.py” and clicked again on “Edit with PythonWin”

3. This opened up PythonWin, from there I opened up the files tab and chose Run

Utilizing Remote Sensing and GIS for Conservation Research

Using GIS in remote sensing applications for conservation biology and research most interests me. In the simplest terms, remote sensing is the acquisition of data without being in the same physical location as the object you are collecting data from. This is a widely used technology. Remote sensing technology can be used to collect data on a broad rage in the form of aerial images that encompass whole ecosystems, or much more detailed data like the movements of a single animal. The latter is where my interests lie.

In order to conserve and manage species of interest it is critical to understand their movements (daily and large migratory trend) and there geographic range. Because researches can not follow most species for an extended time, this is done with satellite transmitters and GIS technology.  I was fortunate enough to use this technology when working on a leatherback sea turtle tracking project.  Leatherbacks are an excellent example of why remote sensing is so critical to conservation biology.  In the Pacific Ocean the leatherback population is plummeting, however researchers were having a hard time putting together a comprehensive picture of what was happening to the population because so little was known about their geographic location besides where females nests. However satellite transmitters, some fancy software, and a GIS allowed researchers to identify critical migratory corridors in the Pacific where leatherbacks are facing high mortality do to interaction with high concentrations of fisherman.  The Atlantic leatherbacks  on the other hand have no defined critical migratory corridors and that is a large factor in why the Atlantic population is faring better.  This data allows policy makers to move forward to help enact laws or conservation plans to prevent high mortality in the Pacific.

This type of remote sensing doesn’t work or all species. I am eager to learn about other methods in which remote sensing can be utilized to identify critical species habitat. 

GIS 4043 Final Project!!

For our final project we used numarias data sets to analize the Bobwhite-Manatee Tranmition line. The maps and presentation can be seen at the two links below.

http://students.uwf.edu/snb22/sarabergeron_finalproject.ppt
http://students.uwf.edu/snb22/sarabergeron_summary.PDF

GIS 3015 Final

The map I'm presenting shows two different data sets concerning the 2009 SAT. The first data set is the mean score of the 3 part test by state. The second data set is the percentage of students that took the test. Because the 2 data sets have a very obvious inverse relationship, I chose to display the mean score data using a choropleth map. The mean scores of the SAT are displayed by graduated symbols on top of the choropleth map.

The results of the map show an inverse relationship between the percentage of students that take the SAT to the mean score by state. The lower the percentage of students taking the test, the higher the average. The higher the percentage of students taking the test, the lower the average. All of the states falling in the lowest percentile group of tests taken (3% - 11 %) fall in the 2 categories with the highest averages. Maine has the highest percentage of students that took the test, 90%, but the average score is only 1377. On the other end, Iowa, tied with North Dakota and South Dakota for the lowest percentage of test taken, has the highest ranking average.

The trend in geographic locations of this inverse relationship is also very defined. The Northeast US dominates in percentage of students who took the test, having all of the states with the highest percentage category (71.1% - 90.0). While all of the Midwest US has the lowest percentage of students taking the test with the highest average.