GIS 4048: Weeks 10-13 - Final

For our final project, we were asked to create a location decision criteria map for clients looking to buy a home within a certain area.  My clients are looking to buy a home in Orange County, Florida within the next couple of months.  Mr. Cahill retired as a corporal for the United States Marine Corps and initially planned on staying home to watch over their two kids - a 7-year-old daughter and an 8-month-old son.  However, he may seek employment options once the family settled into their new place.  Mrs. Cahill got a job as a crime analyst for the Orange County Sheriff's Office (OCSO), which is the sole reason behind their move.  The clients provided a list of priorities in order for them to find the perfect place for their new home.  It's a requirement for them to live in close proximity to the OCSO, as well as an elementary school for their daughter.  It's also important for them to live in an area with low crime rates.  They also request to be near a day care facility, and live in an area with people who are 22-39 years old, and in an area with a high percentage of homeowners.  

In finding the perfect place for them to live, I conducted multiple analyses.  First, the Euclidean Distance tool was used to determine the distances from the OCSO, local elementary schools, and local day care facilities.  I then created a population analysis, which shows the percentage of people who are 22-39 years old, as well as the percentage of homeowners.  Then, I created a crime map to show the variety of crimes that occur within the Orlando area.  With this information, I was able to create a Kernel Density map showing the density of three chosen crimes - burglaries, aggravated assaults, and homicides.  These crimes were chosen because they appear the most violent.  I then reclassified any required data in order to perform a Weighted Overlay Analysis. This analysis shows the best possible locations for the clients, based on equal and set weights.  All of the factors were weighted equally during the first overlay.  When creating the second overlay, the factors were set to specific weights, based on the priorities of the clients. Using this, I was then able to choose three recommended suitable locations in the area.

Overall, I'm very pleased with the results of all the maps I created.  I learned a lot throughout this past semester and I really enjoyed putting all of my newly retained knowledge to work.  Looking back on this project, if I could do something differently, I would probably start with an easier state, rather than trying to work with Hawaii - which was my original plan.  Hawaii was extremely tough to work with because I couldn't find any of the data I needed and that ended up setting me back 2-3 days.  If I had to create another deliverable to submit, I would add "In close proximity to nearby tourist attractions" to the list of criterion and create a map showing those tourist attractions. I would also use that information to create a Euclidean Distance Analysis, as well as a Weighted Overlay Analysis. 

Final PowerPoint Presentation

GIS 4102/5103: Week 11 - Sharing Tools

For our last assignment of the semester, we worked with toolboxes and previously created scripts. We used a new expression known as sys.argv[]. With this expression, the number 1 will be used as the first parameter instead of 0, as with the arcpy.GetParameter function. Just like the arcpy.GetParameterAsText() function, the sys.argv[] always returns string objects.  However, the sys.argv[] expressions have a character limit, which might cause your parameters to get cut short if they're very long.  Therefore, it is usually recommended that you use arcpy.GetParameter or arcpy.GetParameterAsText(). 
As shown in the image below, the number of sample sites has a dialog description.  An explanation was created for all of the parameters in the Syntax section of the Item Description box.  With the explanations set, the tool is more user friendly. 
Finally, we embedded and protected the script tool.  This allows you to share the tool more easily and prevent anyone without the password from editing or even seeing the script.  You only have the option to password protect the script once it has been embedded into the tool. If the script has not been imported, the option will not be available. Shown in the image below, you'll see the results from running the tool with the correct script, as well as a description of one of the parameters in the tool dialog box. 
Overall, I really enjoyed the class this semester.  There were a few bumpy roads, but I managed to do a lot better than I originally thought I would.  I hope that someday I'll have the chance to use all of the knowledge I have gained throughout this course. 

GIS 4102/5103: Week 10 - Creating Custom Tools

This past week was all about creating tools using PythonWin and ArcMap.  We took script files that were previously created and created tools from them.  In doing so, we created a new toolbox in ArcMap which then allowed us to add a script.  In creating the script tool, we filled out all of the required information - such as the name, alias, description, etc.  When we were finished with that, we opened up the Properties dialog box for the newly created script tool.  There, we were able to complete the Parameters as instructed.  In the first image shown below, it shows the result of setting the Input and Output file locations under the Parameters tab, within the the Properties dialog box.  We then were ask to make some changes to the original script.  One of those changes being to replace the print statements with the arcpy.AddMessage() function.  This allowed the statements to be printed, and seen, in the Results box (as shown below). 

I thought this assignment was fairly easy, compared to previous modules.  I didn't have any real complications throughout this lab.   

GIS 4102/5103 - Week 9: Working with Rasters

Raster files are images that are used in ArcMap.  They can come from a variety of sources and formats including .img, .tif and .jpg formats to name a few.  Python can be used to describe components of a raster file, to remap and reclassify landcover, and to calculate slope and aspect.  The most commonly used extension in working with raster files is the Spatial Analyst extension.  Within Python, the arcpy.sa module is most used and all functions should be imported. 

Our lab this week had us use a number of arcpy.sa functions to remap and reclassify landcover as well as calculate a desired slope and aspect of the elevation raster file. My final result is shown below. 

GIS 4048: Week 9 - GIS for Local Government

Luckily, I was able to finish this assignment in time after encountering some technical difficulties with the online desktop.  This past week, we were asked to provide information regarding a specific parcel for a client.  The client, Mr. Zuko, provided us with three specific requests.  His requests are as follows: Utilize Marion County Appraiser's website and online map to view the property and to obtain data for adjacent land parcels; create a site map showing all adjacent parcels, zoning, and property owner data within ArcMap; create a series of PDF maps displaying results. 
The image shown below displays only one of the 16 pages containing the parcels.  In the map, you'll notice that the different zones are broken down by color, making it easier to read. The surrounding parcels of interest are outlined in purple.  You can also see Mr. Zuko's parcel which contains the red diagonal lines.

GIS 4102/5105: Week 8 - Geometrics

Over the past couple of weeks, I learned how to write a text file using PythonWin. During the assignment, we were asked to create multiple for loops to create the text file shown below. We first created a search cursor for the rivers shapefile provided. The cursor called on the following three things: the OID field, the SHAPE geometry object, and the NAME field.  The first for loop created iterated through each row/feature in the shapefile's attribute table.  Within the row for loop, I then created another loop which iterated through each point in the array of the row/feature. I used the .write() method to add a line to the text file shown below. The file includes the feature/row OID, the vertex ID, the X coordinate followed by the Y coordinate, and the name of the river feature. 

GIS 4048: Week 8 - Urban Planning: Location Decisions

Unfortunately, we we finished up the crime analysis section, but now we get to take a look at urban planning and location decisions. This week's assignment focused on finding a home for a couple within Alachua County, Florida.  She has accepted a position at North Florida Regional Medical Center (NFRMC) and he plans to teach at the University of Florida.  They want to live somewhere that's close to both NFRMC and UF, as well as within a neighborhood with a high percentage of people aged 40-49 years old, and in a neighborhood with high house values.  

The map above displays Alachua County, Florida with all of the public lands within the county.  This map was compiled in ArcMap then all of the layers were placed into a basemap layer. Once placed into a basemap layer, ArcMap will draw the layer using optimized map display logic. 

This map is a compilation of four data frames. The two on the left were created using the Euclidean Distance tool centered on NFRMC (top) and UF (bottom). This criteria is important to a couple who are looking to buy a house located near NFRMC and UF.  The two data frames displayed on the right are showing U.S. Census Bureau Census Tracts by the percentage of people aged 40-49, and the percentage of homeowners.  This criteria is important for those looking to buy a home within a neighborhood that has a high percentage of that particular age range, and in a neighborhood with high house values. 

Using the data from the previous map, I was able to create two weighted overlays to help the couple determine where they should start looking to buy their new home.  Upon landing jobs in the county, it became more important for them to be closer to NFRMC and UF.  For the overlay shown on the left, I set the equal influence so each criterion is weighed the same.  As for the overlay on the right, I increased the influence on the distance criteria for each to produce the second overlay.