Applications in GIS (GIS5100): Lab 1

In this week's lab, we created multiple suitability analyses using vector and raster tools. In part A of the assignment, we primarily used vector tools to narrow down suitable mountain lion habitat. The Buffer tool, Union tool, and SQL Query were the most useful in part A since they help eliminate areas that were unsuitable mountain lion habitat. We were able to find out that only 7% of the study area met the criteria for suitable mountain lion habitat. The parts B and C is where things got kind of tricky since we were had to convert some of the vector data into raster data. The tools that were used for these parts were Euclidean distance tool, Reclassify tool, Raster Calculator, and Polygon to Raster tool. Using these tools allowed me to assign different values that provided visual proof of what areas were more suitable than others. I was able to combine those values of each raster dataset into one gave a better visual presentation of the data.

GIS Programming (GIS5103): Module 2

In this week's module, we learned the first part of Python Fundamentals which covered the basic uses of Python syntax that included assigning string variables, using methods and functions to manipulate strings and lists and performing basic math function with Python. The image above is the screenshot of the end result of the script that I created that print my last name and a number that equal to three times the number of letters in my last name. 


I wrote out the line of command on pen and paper so I could manipulate the physical and copy the line of code into the computer.  I began with the stringName command which was stringName = “Tara Ashley Scranton”. I followed this code with stringName.split(“ “) and this created the listName. I needed to have my last name printed so I used this line of code (lastName = listName[-1]) which brought up Scranton. To create the number value to my last name, I counted the letters in my last name and multiply it by three. Code was written as so:

lastNameLen = 8

tripleLastName = lastNameLen * 3

 

GIS Programming (GIS5103): Module 1

In this week's module, we were introduce to the scripting software called Python which we will heavily using for this entire semester. The assignment was not too heavily involved since we were running an already prepared script command in PythonWin. The script command moved all of the GIS Programming Module folders from the R drive to the S drive.

Notes:

1.      Copy and paste the Module1 folder into the S:\ drive. Go into the folder and right-clicked the CreateModulesFolder.py and select Edit with PythonWin.

2.      The Run button to run the script command is represented by a bold stick figure that looks like it is sprinting.

 

Cartographic Skills: Final Project

Every year in the United States, high school students are able to take a standardized test called the SAT that tests their knowledge in three particular subjects which are math, critical reading, and writing. Each subject is graded separately at first and then added together to give the composite score result. These composite scores are a requirement to get into most colleges across the United States. In 2014, CollegeBoard.org gather data from each state of their high school students’ participation percentage and the SAT mean scores in the three subjects that students were tested in. Based the data gathered, Washington Post wanted to utilize this information to write an article regarding the high school seniors and college entrance scores. This data was to be displayed by using a map to illustrate the SAT mean composite scores and the participation percentage by state.

The thematic method that was used to create this map was the choropleth mapping technique which allows the mean composite scores to be display by each state across the United States. The composite scores were classified by using five classes and the Equal Interval classification method. The Equal Interval classification method was used due to the fact that this method has each class placed at an equal interval along the number line. Using this method, map readers are able to interpret the information easily and there are not gaps in the mean composite scores on the legend of the map (Slocum, Thematic Cartography and Geovisualization, 2009). Another thematic method that was used was a loosely-based dot mapping technique which was executed in Adobe Illustrator. The ellipses were placed in each state which they had the participation percentage information.


The reason why this map was created was to cartographically show the SAT mean composite scores in each state, as well as the participation percentage of high school students, that occurred in 2014; however, the final product of the map needed easily understandable to the audience reading the Washington Post article. The orientation of the map is set to landscape mode which encompass all of the data more effectively and does not look cluttered. Gestalt’s Principles of figure-ground was implemented when deciding the blue color scheme of the choropleth map that would allow the states stand out from the mute light green-gray color background. Also, visual hierarchy was utilized as well when making the participation percentage ellipses a beige color so the importance of the number data that was being convey in each state would be more evident.