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Module 1: Introducing Python

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Module 1: Introducing Python This week, we learned about expectations for this class in the form of deliverables and blog posts and went through an introduction to Python, IDLE, Spyder, and Python within ARCPro. We also learned how to calculate using pseudocode and about the Zen of Python, which I thought was very thought provoking and really liked.  I felt that this lab was pretty easy to follow and learn from, but I can see the great opportunity for things to become more complicated as we go along. The most trouble I experienced was retrieving The Zen of Python from Spyder or Python on ArcGIS Pro. I ultimately did it, but I am not confident in my skills there. I am also iffy about pseudocode and will find out if I am on the right track when my assignment is graded and by the feedback I receive. I really appreciated that the lab for Module 1 was useful for the course overall. A lot of time was previously spent creating folders and transferring data each week in my pre...
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Final Project

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For our final project, we were instructed to create a map displaying two thematic datasets also known as a bivariate map. This was very similar to the map we created in week 7 and I expanded upon the skills I learned in that module to create a more visually appealing and coherent map. I chose to work with the first scenario for the objective of my map. I created a map that could be used by the Washington Post to compare mean SAT scores and test participation rates of high school seniors in 2014. Both of these statistics can be compared to state size on a larger scale as I used the United States Albers Equal Area Conic projection. Since this map focuses on the comparison of average test scores to participation percentages, two comparable data presentation types were used.   For test scores, I elected to use graduated symbols so that I could separate the data into the same number of classes as the participation percentage variables. These symbols were separated into five c...
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Module 10 Lab: Dot Density Mapping

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This week, we learned about dot mapping and created a dot map of South Florida's population density. Dot mapping is used to display information while revealing the underlying patterns in the enumeration units of raw data. This is accomplished by reviewing the concentration of dots on areas of a map. A dot equals a predetermined amount of a phenomenon and it is placed approximately where that phenomenon occurs. This reveals patterns in data, in this case, that the population of South Florida is more densely concentrated by Miami and St. Petersburg. There are three main ways to place the dots. For this map, the dots are geographically based, which is the most accurate and error reducing method. As noted in the instructions, creating this weeks lab was not extremely complicated, but there were a few steps that took a significant amount of time. The first was troubleshooting to find an appropriate size for the dots while maintaining that there was at least one dot in ...
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