GIS 3015 Final Project: ACT Composite Scores and Participation Rates by State

The final project of the semester for our Cartographic Skills course tasked us with creating a thematic map to communicate two datasets to the public.  Conceptually, we were to consider the end use of the map as an accompaniment to a Washington Post article on test scores and high school seniors.  We were to choose either the SAT or ACT test as our target, then acquire and communicate via map the average test scores and participation rates by state.  I chose the ACT because it was the test I took over a decade ago when I first entered college at Ohio University.
The first step was to acquire the test and participation data.  The links for this data on both tests were provided in out lab instructions (with the ACT data being acquired from http://www.act.org/newsroom/data /2013/states.html).  I copied this data into an Excel worksheet.  I then created my initial ArcMap document with the data layer of the United States transformed to the North American Albers Equal Area Conic projection for a more pleasing visual.  I imported the Excel worksheet into ArcMap and joined it to the states layer.  This resulted in the basic ArcMap document I needed to create the final map.
Before exporting the map for final design work in Adobe Illustrator, I needed to decide exactly how to represent the two datasets.  I decided the simplest method would likely be to represent one dataset via a choropleth map of the states while the other would be represented via proportional symbol.  The provided map examples from prior years took this route.  After creating test maps with the data represented both ways, I decided to use a choropleth map to represent average test scores and proportional circle symbols to represent participation rates.  I chose a diverging color scheme for the choropleth map with six categories to make the score differences by state clear without overly cluttering the image.  For the participation rates a simple blue circle with five categories (each representing 20% increments) seemed to work the best visually.
I began in Illustrator by importing the main map of the contiguous United States with the legend.  I then imported layouts of only Hawaii and Alaska, the latter of which was resized to better fit the layout.  From ArcMap I had left turned on the labels showing each states participation rates in addition to their state abbreviation.  I had hoped to be able to resize the participation rates to fit within their circular symbols.  This worked relatively well except for the tightly bunched and small circles of New England.  In an attempt to make these more legible, I imported another layout from ArcMap showing only the New England states.  Of course, I could not also increase the size of the proportional symbols without creating an entire new symbology for the data, but the labels are nevertheless a bit clearer.
Due to the purpose of the map as well as the need to clearly communicate the two datasets, I decided to keep the map relatively simple.  I added drop shadows to the outlines of the states; I was initially displeased with how the drop shadows appeared within the lower 48 states, but I believe they give the map a sense of depth and relief.  Finally, I added neatlines and contrasting backgrounds to the inset maps and legend.
The final map shows a striking pattern of high test scores in states with low participation rates.  This pattern does not hold for every state, of course, but I am sure a statistical analysis would show a strong correlation between participation and score.  Incidentally, a glance at the data for the SAT scores and participation data shows a similar pattern.  This correlation may be related to students having a choice between the two tests versus being required to take one or the other.
In the end, I believe the finished map communicates the two datasets without unnecessary clutter or confusion.  The project instructions allow for the use of text to be included within the map layout for additional explanation, but I believe the map title and legend adequately communicate the content and purpose of the map.

GIS 3015 Lab 12: Google Earth

In the final lab of our Cartographic Skills course, we explored the fascinating world of Volunteered Geographic Information and how everyone can get involved in disseminating geographic knowledge (and opinions).  The lab, however, focused on Google Earth and how to import data from ArcMap into Google's free and widely used product.  While the depth of functionality and data manipulation of Google Earth may not be near that of ArcMap, it is much easier to use for those not versed in GIS software.  Additionally, data imported from ArcMap can be made widely available to anyone who may desire to see and use it.  In the map above, I imported the dot density map from last week's lab into Google Earth.  Once imported, certain properties of each layer can be altered within Earth (such as transparency) or a layer can be turned off.  The latter was useful for the second portion of the lab, wherein we created a Google Earth tour of southern Florida.  I began the tour with the dot density layer turned on.  As the view zoomed in to individual cities, I turned the layer off so as not to clutter the view.

Overall, this lab was a fun experimentation with a product, Google Earth, that I never viewed as being capable of such interesting work. I look forward to playing with its capabilities.  On the topic of fun, we were asked to find an image from Google Earth that we liked for this blog post.  The first image below is of Zion National Park in Utah, one of my favorite places to explore whenever I get a chance.  The last image is of Area 51, a place I have only been to the gates of and would love to explore if the security wasn't so tight.

GIS 4043 Lab 13: Georeferencing, Editing and ArcScene

The thirteenth and final lab exercise of our Introduction to Geographic Information Systems course, like the previous one, introduced us to multiple tools and functions of the ArcGIS software.  Our first task was to georeference a raster image that included no coordinate information to a referenced vector layer of UWF campus buildings.  This involved matching points on the raster image to the corresponding points on the vector layer.  ArcMap then alters the raster image based upon these control points.  Exactly how the image is altered depends on the transformation chosen by the cartographer.  In the example above, I used the 1st order polynomial transformation for the north raster and the 2nd order polynomial transformation for the south raster.  I applied a 3rd order polynomial transformation on the south raster as well, but the 2nd order transformation appeared more accurate.

The second portion of the lab introduced us to editing in ArcMap, an integral component of the software that I am sure will be utilized much in future courses.  In this case, we added one building (the gymnasium, in yellow in the map above) and one road (Campus Lane, in red).  In addition to the line and polygon additions, we edited the corresponding entries in the layer attribute tables.  

The final activity this week involved importing our map of the UWF campus with the addition of our edits into ArcScene to explore the three dimensional capabilities of the software.  As seen below, the ArcScene layout was exported as a JPEG and then imported into ArcMap to add the legend and text.  Again, this was a mere introduction to a component of ArcGIS that could be extremely powerful for certain projects.  I can imagine its use in archaeology, for example, in visualizing and reconstructing complex sites.  

GIS 4043 Lab 12: Geocoding and Network Analyst

Approaching the end of the semester, this lab was split into three sections to introduce use to ArcMap functions that will be explored further in later courses.  One section focused on an exploration of ModelBuilder.  Reminding me of a "Lego-fied" version of Python, ModelBuilder allows you to create a chain of processes by creating a visual model composed of data inputs, tools and outputs.  These models are easily created, edited, saved, and shared with others.  I look forward to learning more of ModelBuilder in the future.

The other two sections were connected and resulted in the map above.  The geocoding section had us create a point data file showing EMS locations by creating an address locator for use in connection with an Excel file of EMS addresses.  We saw how the process can work automatically when all data match properly; we also had to deal with several addresses that did not match automatically.  For these we had to manually select the correct address.  We saw how to do this using the best match candidate list as well as manually locating the address in question directly on the map.  We also explored the use of outside mapping services (e.g., Google Maps) to verify addresses.

Using the geocoded EMS data, the final section had us use ArcMap's Network Analyst to create a sample route from one EMS location to two stops.  Like the other sections, this was an introduction to a powerful tool that we will learn more about in the future.  This lab introduced us to the basics of Network Analyst:  how to create a route, adding and editing stops, tweaking the options for the route analysis, and exporting the route as a new feature class.  I used this route data for the inset map in the above map document.

GIS 3015 Lab 11: Dot Density Mapping

This week's lab introduced us to the relatively specialized dot density map.  This style of thematic map represents data in the form of dots distributed across the map surface.  In the example above, I created a population map of southern Florida wherein each dot equals 20,000 people.  Dot density maps have the advantage of being simple and intuitive to read for the basic information communicated.  However, the distribution of the dots may also intuitively suggest to the observer the actual data distribution when in fact the dots are placed randomly.  To ameliorate this weakness, we can choose to include and exclude different areas on the map that dots may be drawn based on other data layers.  Thus, based upon what we know about the data, we can more accurately portray where the data should best be located.  In the example above, the dots representing population were excluded from the surface water layer and further limited to a layer representing urban areas (not symbolized).  While the dots were still drawn randomly given those limitations, we can be safe in assuming the resulting dot placement is more accurate than without such criteria.

Many of us ran into issues with ArcMap as we worked through the process of finalizing our maps.  We learned, for instance, that drawing the dots for the dot density map is an immense strain on the software. This is compounded when making other edits with the layout, creating delays each time.  To minimize this issue, the lab instructions had us turn off masking (i.e., the inclusion and exclusion criteria).  Combined with the fixed placement option, this would theoretically allow us to create our layouts with fewer software delays.  However, when turned back on, the dot placement did not recognize the inclusion and exclusion placement criteria.  I experimented with different orderings of the data layers in the table of contents with no effect.  For my second attempt, I created a fresh map and, rather than turning off masking, I simply unchecked the south Florida layer so it did not display.  This method worked and allowed me to complete the above map.