Applications in GIS, Final Project

Powerpoint Presentation: Here

Slide -by- Slide Commentary: Here

GIS Programming, Lab 11: Sharing Tools

Screen shot of the tool dialog box and resulting map in ArcMap 

This week we learned how to share custom tools.  We were given a script where we needed to make a few edits.  Once those edits were done, the script was embedded into the script tool.  This was done because it makes it easier to share.  We also learned how to password protect the tool.  The above screen shot is of the tool dialog box and the resulting map in ArcMap.

This was our last lab and I am grateful.  This course was definitely challenging but I did learn a lot.  Overall, it was a good experience and I can see how I can take away a lot from it and use in the GIS work industry.    

GIS Programming, Lab 10: Creating Custom Tools

This week we learned how to create a custom tool using a script.  We had to modify a script and set the parameters.  Below is the result of setting the parameters of the script tool.

MultiClip tool parameter window

Next we had to run the tool.  The print statements don't appear when running a script tool so we had to change them to AddMessage statements.  We also had to modify the arguments slightly as well.  The commas needed to be replaced with "+" signs.  After doing so, informative messages appeared in the dialog box - allowing users to see the progress of the script tool.

MultiClip tool dialog window displaying messages of completed actions and successful run

GIS Programming, Lab 9: Debugging and Error Handling

This week we worked on debugging and handling errors that I (tirelessly) encounter.  This lab was super helpful and I almost wish it came earlier in the semester.  

We were provided with two script templates which were littered with (okay, maybe not littered but had several) errors.  These errors were both syntax errors and and exceptions.  The first one, we had to fix these issues to allow the script to write the names of all the airports in the airport shape file from week six. 

Screen shot of my list of airports.

In the second script, again, we had to locate and fix errors that we came across while checking, debugging and attempting to run.  Once everything was fixed, the script printed each data frame's name and the layers it contained within the Austin_TX.mxd we worked with last week.

Screenshot of the data frames listed and the layer names they contain.

Overall, a very helpful lab!

GIS Programming, Participation #2

Title: Application of web-GIS approach for climate change study

Link:  http://meetingorganizer.copernicus.org/EGU2013/EGU2013-6682.pdf

Summary:

This article is about climate change and how much data and information is required to study it.  Because this is the case, dealing with that much data can be cumbersome and be detrimental to the outcome.  That is why a web-GIS system has been created.  The article states that some of these single datasets can contain up to tens terabyes!  This system was created for “analysis of georeferenced climatological and meteorological data.”  One of the solutions that is included in this system is object-oriented programming, which is essential to analyzing meteorological data.  The enormous benefit of this system is that the output that is created is a basic binary code that can be produced on any regular computer with an Internet connection.  This allows for more opportunities to analyze the data. 

Since this system doesn’t require much from the computer, it is being used in educational purposes as well.  It is a user-friendly system so that even a computer layman can compute and produce complex analyses.  From my experience using large datasets, it can significantly slow down my computer.  I think creating a system like this is a great idea and will be able to take data analysis so much further, not only in the meteorological industry, but many others.

GIS Programming, Lab 7: Geometry & Rasters

This week we had to write two scripts.  The first one was to create a text file that would print out the Object ID and coordinates of the vertices of each shape.  In this case, it was a "rivers" shapefile in Hawaii. 

A screenshot of my text file which displays the ObjectID, coordinates and name of the river/stream.

The second script we had to write created a new raster based on specific criteria pulled from an elevation raster and a landcover raster.  In this case, we pulled out specific forested areas.  This script took a lot of time to figure out and used various modules including relcass, slope and raster and used the spatial analyst extension.

Final raster created from taking specific criteria from two other rasters and saving it into a newly created gdb

This week took a lot of patience, trial and error and aid from classmates and the professor.  It was definitely rewarding once I was successful though.

Applications in GIS, Lab 9: Location Decision Analysis

This week we worked with location analysis and weighted analysis.  The scenario is: a couple is looking for a home in Alachua County, Florida.  The woman is a physician and works at the North Florida Regional Medical center and the man works as a professor at the University Florida.  They have 4 specific criterion which are: Close to NFRMC, close to UF, in a neighborhood with a high percentage of people aged 40-49 and an area with high percentage of home value (home owners).  

Basemap of Alachua County, FL

The first map we created is a base map of the county in which they want to move.  Here you can see cities, major roads, and public lands (and by who they are owned).

Four specific criteria requested by the couple who is house hunting.

The second map we created were 4 different data frames containing each of the criterion.  We used the Euclidean Distance Analysis tool, reclass tool and feature to raster tool to determine which census tracts would be more suitable than others.

Weighted Analysis.  We put more weight on the proximity criteria to change the ideal tracts.

The final map was created with ModelBuilder and the weighted overlay tool.  The map to the left was created with equal parts weighted or each criteria (of 4) was weighted at 25%.  The scenario then had the couple concerned about traffic, so two of their criteria then became more importance.  So we weighed them differently.  The proximity to the hospital and school became more important (weighted at 40%) than the high home value neighborhood and age range.