Final Project

Caption - A thematic map on ACT participation and results seems to indicate an interesting pattern. Scores are typically higher where participation rates are the lowest. Can states that mandate the test for high school graduation affect its use as a measure for college admission?

Module 11: Google Earth

"NIMBY", or the "not in my back yard" syndrome seems to be the most prevalent argument from opponents to wind power based upon reports by the Ohio Department of Health, and many other sources. The vast majority of citizens are in support of wind power operations, so long as large wind turbines are not located in a place where shadows, glare, or noise are apparent from any concerned individual's residence.
Micrositing a wind farm at a specific location appears to be the greatest task in wind power planning.
At first, I wanted to place my proposed wind farm far enough offshore into one of the Great Lakes to negate the NIMBY problem; however, I began to wonder about engineering concerns. If a wind turbine, or associated submerged power lines, required maintenance during the cold winter months of the Great Lakes then ice flows and ice accumulation could present problems that could render the project useless.
Therefore, I moved my search back to land. After some consideration, I decided to borrow a page from Bowling Green's playbook, and I sought out a landfill to collocate my proposed wind farm site with.
After searching in several states, I found a compatible site at the BFI Ottawa County Landfill, which is located at the base of a peninsula that sticks out into Lake Erie, approximately four miles northwest of the city of Port Clinton, Ohio and 20 miles southeast of Toledo, Ohio.
The site is sparsely populated, and bordered to the North by Camp Perry, an Ohio National Guard training installation.
This site appears to fall well within a Fair Wind Power Class, 6.4-7.0 meters/second at 50 meters as indicated by the National Renewable Energy Laboratory's Wind Power Resource Map.
Ornithology seems to be a concern because of bird populations associated with landfills; however, no specific concerns have been noted at the Bowling Green wind farm.
The local residential population may be as suited as any to deal with wind power noise as Camp Perry is noted as the world's largest outdoor firing range, and there is a Class I railroad line located on the south side of the landfill.
By locating the wind farm on the north side of the landfill, shadow flicker nuisance would be mitigated as the long shadows cast after sunrise and before sunset would fall on virtually uninhabited property.
There is no shipping impact for this land based site, and the visual impact may actually be perceived as positive because turbines at this location would at least complement the landfill and military installation.
Nearby, in Toledo, there is an alternate location at an oil refinery and tank farm; however, wind measurements may not be as favorable, and turbines may be visible from the majority of the city.

Module 10: Isarithmic Maps

I wish I could have had this new level of comfort with the pen tool, anchor points, and handles when I was working the previous module! Nonetheless, it was encouraging to feel a little bit better about it during this exercise.

One of my favorite features on this map has to be contour values that are incorporated into the lines. I used a small white background behind each one to make sure that these numbers did not get lost in other map traffic. It was just a matter of ordering the layers properly, and we were good to go.

I tried to give a meaningful legend a stab, so we will see. I could have pulled off shading or coloring in each precipitation zone relatively easily, but I am satisfied with black and white.

Module 9: Flow Maps

For this map, I wanted to pursue the look a classic textbook map. I ended up being very pleased with the layout and design of this map. One of my favorite highlights is the branch and trunk feature I was able to create for the flow lines coming from Central America and the Caribbean Islands.

This particular project was slowed by a few technical difficulties. A few of them were specific to my computer; however, I encountered one problem that I have yet to find a solution for. I posted about it in detail in our class discussion. To summarize, at some point during my work, I must have changed a default setting in Illustrator. Now, when I create any object, whether it is a line, or a shape, or anything else I get a small black line around it, whether I want it to have a stroke or not. I cannot even change the color, it stays black.

Unfortunately, this technical limitation influenced design considerations, and ultimately the final product.

Module 8: Dot Maps

I have dots that tend to coalesce and my map is probably not the best map for extracting data from; however, the strength of this map lies in the ability that an engaged reader will have to recall patterns in the data from memory. I devoted much of my time to locating dots in fashion that would reflect real-world locations of housing units within each enumeration unit (the counties). It was a manual approach that seems to be almost a hybrid of the geographically weighted and geographical based methods of dot placement. You might be able to recognize the I-4 corridor through central Florida, and you should see clusters of population densities around different metropolitan statistical areas.

For a while, I considered a black map with white dots as a means of creating a map that looked like you were looking down at Florida at night. I may go back and give this a look just out of curiosity.

While I feel like I am far from an expert on dot maps, I do feel like I have been properly introduced. I did not have trouble with the mathematical concepts, but I could use more experience.

I am very grateful for every one's input in the class discussion board. The wealth of knowledge there bailed me out of some confusion, and the shortcuts discussed are the best.

Module 7: Proportional Map Symbols

This was the first exercise where I felt immediately comfortable with the technical aspects of the assignment. In addition, I am feeling more and more confident with the nuances of Adobe Illustrator. As a result, I was able to devote the majority of my time to the design and appearance of my map. I hoped to establish a noticeable figure ground with the light blue background and purple circles for my proportional symbols.

One problem I encountered was with changing the color of my country labels. At first, when I attempted to lighten the labels to gray, my entire artboard would turn the same color. After a few moments of frustration, I realized that my label layer had imported a path shape from ArcGIS. As soon as I identified this, I was able to delete it and continue.

MS Excel's function features allowed me to calculate circle sizes needed quickly, and I ended up with what has been my favorite map project so far!

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!