Wednesday, May 29, 2019

GIS Programming - Module 2 Lab

Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:27:44) [MSC v.1900 64 bit (AMD64)] on win32

>>> print ("Hello Everyone!") 

Hello Everyone!


This week’s lab was all about learning the basics of Python! For this week’s lab, I had three separate coding exercises. For the first exercise, I was tasked with printing my last name from a created list. For the second exercise, I had to edit the given script for a dice game with a handful of errors that would prevent the script from running and import a specific Python module. Finally, for the last exercise, I was tasked with creating a script that creates a list of twenty random integers between 0 and 10 and then select and remove every occurrence of a specific integer and print the newly updated list. As you can see below these were the basic flowcharts I created to help me construct my scripts for parts one and three as they were the scripts I needed to write myself and not edit.



After writing and editing my scripts, these were the results.



As you can see the script successfully printed my last name, the prewritten script that I edited and corrected errors ran for the dice game, and it created the list of twenty random integers, selected the unwanted integer, and removed all instances of the integer and printed an updated list. I really enjoyed this week’s lab as it brings back really good memories of when I first started Python, and I look forward to sharing my future progress with you!

~ Code On!

Monday, May 20, 2019

GIS Programming - Module 1 Lab


Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:27:44) [MSC v.1900 64 bit (AMD64)] on win32

>>> print ("Hello Everyone!") 

Hello Everyone!

Well, I can officially say that I have completed my first ever semester of Graduate school and I am loving it! To kick things off for this Summer semester, I am taking a GIS Programming class that will focus heavily on using Python (I just could not resist having a specialized intro for this blog series!) to create various GIS processes. For this weeks lab, I focused on primarily learning about the history of Python and some coding basics that include how to map out our code using flowcharts and pseudo code. I was also tasked with creating the required folder directories on my machine to work through the class from a provided script. To run this script, I had to use Spyder which is the Python interface/editor I will use primarily for this class. This was achieved by simply typing in the word 'spyder' into the Python command prompt. I then opened the script and examined each line to get an idea of what the script would do. Finally, I ran the script and it created my folder directories (seen below).
As you can see, the script I was provided created a series of directories for each module of the class. Since the class is 8 weeks in length, there are 8 modules in total. Within each module folder, there are three subfolders (Data, Results, and Scripts). I am so excited to be taking this class as I can continue to work with my preexisting Python skills and develop them further. I look forward to sharing my progress with you all!

Thursday, May 2, 2019

GIS 5007 - Final Project

Hello Everyone!

It's so hard to believe that my Cartography class is coming to an end, I have learned SO much this semester here at UWF. I never thought I would leave saying Adobe Illustrator is a Life Saver. For my final project, I was tasked to create a map of US national average cumulative SAT scores and participation percentages (seen below):



For this project, a bivariate map using choropleth and graduated symbols was created.  A choropleth map was used to depict the average SAT scores because it excels at emphasizing class-based data where phenomena are grouped together for a means of comparison (Slocum, Thematic Cartography and Geovisualization, 2009).  The comparison of state cumulative average scores would best be seen by the intended audience using a color ramp from red (lower scores) to green (higher scores).  The SAT score data was classified into seven classes to best represent the variance in scores across the nation and show more distinction than five classes.  For this method, equal interval classification was used, because it makes the thematic data easier for the intended audience to understand (Slocum, Thematic Cartography and Geovisualization, 2009).  Since this data pertains to average scores per state, the data was not standardized.
The second thematic method used was graduated symbols to summarize the state participation percentage.  Graduated symbols were used because they best show the change in quantity and the magnitude of participants taking the SAT.  This data was broken down into four manual classes ranging from two to one hundred percent.  This was done because it was the easiest method that would be understandable by the public.  Again, this data was not standardized since the flat percentages were given for each state.  While other more advanced methods of data classification such as natural breaks, quantile, or standard deviation methods could have been chosen, due to the research specifications and public audience, data simplicity was the primary goal.
            To best capture the data in question, the map was created in a portrait view to fit the entire continental United States.  The importance of this orientation was also that the state of Alaska and Hawaii could be fully represented.  It was also decided that the map should be grouped and labeled together by relatively loose regions, the southeastern/northeastern states, the Midwest states, the mountain states, and the Pacific states (Alaska and Hawaii included but not to scale).  An inset map was also included to show the Washington D.C. area that would be obstructed from a normal perspective.  To emphasize the regions, a drop shadow effect was used to draw the viewer's eye to the various regions and make the map have a pop out effect.  In addition to utilizing drop shadow, visual hierarchy rules were implemented.  First, the choropleth and graduated symbols were given the only color aside from the legend elements as these two components were the most important.  Second, the title was clearly visible and concise, but all other text was reduced in size to not distract the viewer from the map.  Finally, any elements that were not of visual importance such as cartographer information, data sources, and north arrows were minimize either in size or opacity.
The results of this project were very interesting, the results show an inverse correlation between the two variables with less participation yielding higher test scores with the highest average scores in North Dakota with the lowest participation (2%) and the lowest scores in D.C. with the highest participation (100%). It just goes to show that using the SAT alone does not give you an accurate portrayal of statewide data with potential unrepresented groups.