Propotional Symbol Map of the Contiguous United States

Intro:
I was tasked with creating a proportional symbol map of the United States based on a given database of information from the US Census Bureau.  I decided that it would be interesting to map out the average farm size in each county in the US.

Methods:
I was given data based on the US county level from the US census.  As I explored the data I realized that farm size could vary greatly based on the region.  I then symbolized the counties based on average farm size using proportional symbols in ArcMap.

Results:
There are several interesting spatial patterns present in this map.  One interesting pattern is the larger farm size along the Mississippi River Valley.  It's actually rather easy to tell where the Mississippi River is just based on looking at the farm size.  The most noticeable pattern is the extremely large farm size in the central part of the country (east/west-wise).  However, there are also scattered clusters of larger farm sizes throughout the east.  These larger clusters may conform to the areas of typical CAFO (Concentrated Animal Feeding Operations) locations.

Choropleth Map of the Contiguous United States

Intro:
I was tasked with creating a choropleth map of the United States in ArcMap with a given database from the US Census Bureau.  I decided to map the percentage of elderly people per county.

Methods:
I first had to choose a projection for the data and decided on North American Equidistant Conic, as it is one of the more popular projections when mapping the contiguous United States.  I then symobilized the county feature class using graduated colors based on population of persons older than 65 years old, which i normalized with population. I used the natural breaks method provided in ArcMap to split up the classes.  At that point I just had to make sure that it was symbolized as a percentage.

Results:
There is a noticiable higher percentage of elderly people in the central part (east-west wise) of the United States.  A surprising result to me was that there isn't a higher percentage in the southern regions of the US, though Florida does have a noticeably higher percentage than the areas around it.

Economies of Europe Bivariate Map

Intro:
This is a map using both proportional symbol mapping and choropleth techniques.  It looks at the economies of countries in Europe by looking at both the unemployment rate of the country and the purchasing power parity (PPP) of the country.

Methods:
I began by researching the various unemployment rates for the European countries and recording them in an Excel file to manage the data.  I then wanted to look at the PPP of each country, which was more difficult than I originally believed it would be.  I eventually found a measure of each countries PPP which used the scale of how far one US dollar would go in the country; in other words it looked at how much goods that cost $1 in the US would cost in each respective country (ignoring regional variance and taking an average in each country).  The data for unemployment rates was extremely skewed with one very high value.  Due to this, I used natural breaks instead of the quintile method or equal interval method to classify the unemployment data.  I generated the country outlines and chose a projection (Europe Lambert Conformal Conic) in ArcMap and exported it to AdobeIllustrator where I inserted the color classes and symbol sizes into the map.

Results:
This map shows that there is at least a correlation (inverse) between unemployment rate and PPP of a country.  Though there are some outliers, particularly in Eastern Europe, this can be attributed to the way a country reports its unemployment rate and its unemployment benefits.  One pattern that can be seen in this map is that Southern Europe tends to have higher unemployment and lower PPP than the northern part of Europe, particularly in the Baltic Peninsula.  Though, Spain and Greece have higher PPP values, which tends to disagree with their high unemployment rates, this can possibly be attributed to the fact that they are popular tourist destinations and may be able to charge more due to this.

Proportional Symbol Bilingualism

Intro:
The purpose of creating this map was to explore the techniques to build a proportional symbol map.  We were able to explore our own data for this map and I decided that it'd be interesting to look at peoples' ability to speak English if they come from a non-English speaking household.

Method:
I searched online and was able to find data regarding the amount of peoples, per state, whom claim that they come from a non-English speaking home, and how well they claim to speak English.  This implies biligualism, though it is entirely possible that there are persons who speak more than just two languages well. It is also true that there are many people whom hail from an English speaking household but can speak more than just English well or very well.  Taking that into consideration this is not a true map of bilingualism in the United States.  I took the count of people claiming to be from a non-English speaking household but still claiming to speak English well or very well and dropped it into an Excel file.  I was then able to calculate the size I wanted for each of the symbols by taking the square root of all of each set of numbers seperately and then scaling them from 1 to 100% of the highest value.  This gave me the proportions of shape sizes I would need for my map.  From there it was just a matter of setting up the map and scaling the circles to the right sizes for the right states.  I chose to make the circles slightly transparent to help show the state boundaries behind them.

Results:
The results of my map aren't too surprising.  The states with the larger populations had a larger number of results, while the less populated states, such as the Dakotas, had smaller results.  One interesting observation is that states in the Southwest, despite having relatively low populations, have higher values than some more populous states; also, many East Coast states had high results.  This could be due to these regions having more immigrants than other regions, particularly the central part of the country.

North Carolina American Ancestry Choropleth

Intro:
This is a map showing the location of North Carolina residents reporting American ancestry and the various ways data can be reported and classified in a choropleth map.

Methods:
I began with an outline of North Carolina, it's counties, and some data regarding amount of people reporting American ancestry per county in North Carolina.  I already had the absolute data but had to calculate the percentage data based on total population, which was relatively easy in Microsoft Excel.  Then, I had to establish where the class breaks would be in the data.  To calculate this for quintile classification, all I had to do was take the highest value in the dataset, and divide it by five; this gave me the amount each interval would contain.  From there I chose the colors of the maps based off of a sequential set on http://colorbrewer2.org/.  Seeing as how I am not intimately familiar with counties in North Carolina, it took me a while to put my data onto the map as I had to make sure I was putting the right color onto the right county over and over again.

Results:
The main purpose of this map is to show how much influence a cartographer can have over the appearance of data.  It's clear from this map that how data is represented can have a huge effect on how the viewer sees it.  The appearance of the data can vary vastly based on the type of interval classification method used.

Reference Map of Portage, WI

Intro:
For this map I was tasked with taking creating a reference map of a location.  I chose to map my hometown, Portage, WI.

Methods:
I began by taking an aerial image of my hometown from ArcMap and exporting it into AdobeIllustrator.  Then, I proceeded to trace out some of the more important elements of my hometown which I wanted to be featured in my map.  These elements included, significant bodies of water, major streets, and major structures.  The most difficult part of this map was adjusting to the fact that not everything could be done perfectly.  The aerial map wasn't perfectly clear at some points and I had to adjust some of the features and labels to insure they fit well with the other map elements.

Results:
The end result is a very simple reference map of Portage, WI which could possibly be used to highlight the city to tourists or other visitors.

GPS: Grocery Store Access

Into:
This exercise's purpose was to teach me how to take gather data on a GPS handheld receiver and drop it into ArcGIS to create a map out of it.

Methods:
We had freedom with this exercise to choose what we wanted to map, and I was curious as to most peoples' everday access to food in the city of Eau Claire.  So I decided to do some research as to where the major supermarkets in Eau Claire are and drove around marking waypoints in my GPS as I got to each one.  I only used major supermarkets with an inventory of a wide variety of items, which in Eau Claire include:  Gordy's County Market, Target, ALDI, Walmart, Festival Foods, and MEGA.  I did not include small specialty markets, serving a special clientele, such as Just Local Foods.  I then took the census tracts from the ESRI database that the University of Wisconsin-Eau Claire provides students and put them on the map as well to help me show population levels.

Results:
As can be seen by the map, most of the major supermarkets lie in the outskirts of the city.  This leads to a mini food desert being created in the central area of the city.  I'd like to explore more into this issue and see what's being done about this potential food desert in Eau Claire.