Module 6: Choropleth Maps

When I started this lab, my immediate struggle was figuring out how to change the map projection. After reviewing discussion topics and a few google searches later, I had what I wanted and I was able to continue. My color map was relatively straightforward with the exception of the North arrow. I somehow lost it in transition, and I didn't want to use the one from the AI symbol library, so I generated my own. This lab was by far my most successful one in terms of working with layers. This should prove to be productive going forward. The grayscale map involved tabulating our raw data into census divisions. This was not difficult; however, it was a little time consuming. After rolling all of the states into their respective census divisions, I used the two columns of data and generated a population change percentage. An example of my equation in excel might have looked something like this "=(C8-D8)/D8*100" where C8 would have been the sum of 2000 populations and D8 would have been the sum of 1990 populations. After this, I reviewed the basics on equal interval data classification, and I had something to work with. I didn't have a census division falling into my fourth class, so I hope that I am not missing something.

My only regret is not labeling the divisions clearly on the map. I checked out other blogs and saw some ingenious techniques that I considered applying, but I did not want to be guilty of blatant plagiarism. All in all, I am happy with my final product and look forward to doing more!

Module 5: Map Composition

In this exercise we were given fundamental map elements, with instructions to add a few that were missing, and to compose the elements into a visually pleasing and informative map. I wanted to place as much emphasis as possible on the mapped area. The default color patterns were decent; however, I am partial to a more uniform color pattern adjusted for intensity. I was hesitant to place the legend and the inset at the top of the paper. Our text had warned about a top heavy design and the intrinsic sense of gravity. After evaluating the size of the legend and the inset, I decided that they could work at the top after all. The mapped area carries enough emphasis that balance is still maintained. Working with the peninsula of Florida will always create a balance challenge, but at the same time, it opens up the door for interpretive design. Feel free to contribute any and all feedback.

Module 4: Map Elements and Typography

This was a challenging assignment, as I was working with Adobe Illustrator for the first time; however, after I gained some momentum I was able to move right along. One of my favorite features so far is being able to warp text on a path with a negative arch. I believe that this gave my map a traditional feel, especially labeling bodies of water. I like the idea of generating a look that map users are already familiar and comfortable with. I bent the rules slightly by using italicized text to label the different keys. Our reading says that we should limit the use of italics to hydrological features, but I think it is acceptable to apply this style to keys since they are so closely associated with a major hydrological feature (the ocean).

Module 3: Data Classification Lab Assignment

I tried to decide which method of classification best represented the given data by evaluating which of the four methods offered the clearest picture of the data. It was easy for me to rule out the standard deviation classification because this classification would not allow me to use a color ramp similar to the others. Additionally, with only four breaks in the data, I felt that far too many tracts fell within the smallest break. I felt the same way about the equal interval classification.

I decided that the quantile data classification portrayed the best model of the data. On the map below, I made some key changes to a quantile style map to further enhance the portrayal of the data. First, I created eight breaks. One problem with five breaks was the large range of data included in the 5th class. There is still a significant range in my 8th class; however, I believe that this is appropriate as I am placing an obvious emphasis on demographics. After that, I wanted to change to a color ramp that did not directly correlate with racial demographics. Such a color pattern could be interpreted as insensitive and offensive, and I wanted to avoid this. My final product resulted in a map with enough color variance and enough classes to easily interpret the data provided.

MAP CRITIQUE LAB

http://piratemapster.com/maps/free/PM1209001.gif

Above, you will find my example of a bad map. It is safe to assume that this is a treasure map, and we can only hope that the cartographer's intention was to make the treasure hard to find (or to fill a page in a coloring book). The map has a North seeking arrow; however, the only other geographic references are some poorly defined symbols. Presumably, you are approaching the area by sea, so why not land your craft closer to the "destination" to avoid the walk of doom past the giant bear? Is there cliff in the way, or a rocky coastline?

(http://upload.wikimedia.org/wikipedia/commons/7/73/US_presidential_election_2004_results_by_county.jpg)

Above is my example of a good map. This map illustrates the 2008 presidential election results by county. Sen. John McCain received a majority of the votes in the red counties. President Obama received a majority of the votes in the blue counties. This map would be a great foundation for other layers of data such as population density, demographics, and even poverty. The first thing I thought when I saw this map is, "it is really, really red, how did President Obama win?!". A second look at this map confirms that President Obama carried more densely populated areas such as counties in the Northeast, much of the Pacific coast, counties with major cities, and counties where major universities are located. For an example of the significance of demographics, look at how President Obama fared around the US Highway 80 corridor through central Alabama and Georgia. I have titled this area "The Civil Rights Belt". I believe this is a great map because it caused me to think about whole new sets of data, and it inspired more study. My only concern: why is Alaska white?

Off and Running!

We are off and running into a new world of Cartographic Skills!