Final Project

Blog Map 10 - Bivariate

Blog Map 9 - Dot Density

Lab 10 - Bivariate Map

Lab 9 - Dot Density

Final Project Proposal

                For our Choropleth Lab I had done the violent crimes per capita of the 50 states.  To expand on my initial map I will be delving more in depth by mapping violent crime as of 2012 in the mid-Atlantic region by county.  

                The United States Geological Survey classifies the mid-Atlantic as Virginia, West Virginia, Maryland, Delaware, Pennsylvania, New Jersey, and New York.  This is a total of 327 counties.  As these are the most likely states for local travel, I would want to know which counties have the most violent crime.  As someone who likes to avoid violent crime, it would be beneficial to know where exactly to avoid.

                I would utilize the Uniform Crime Reporting Statistics which was created by the Federal Bureau of Investigation and the Department of Justice.  This crime report houses data from almost two decades back.   However, the most recent data is most complete when it comes to county by county statistics.

                Data permitting, I will expand my view of states to North Carolina, Ohio, Massachusetts, Rhode Island, and Connecticut.  This would bring up the total to 542 counties.  If these states are complete I will incorporate the location of the schools in the A10.  This theoretically would add a new element of not just knowing which counties to avoid, but also showing which schools lie in counties that are more prone to violent crimes.  

                This project is exciting in that it could expand even further.  It could include moving to violent crime data by county for the entire east coast or the United States as a whole.  The data set from the FBI also provides other crime data so that with future projects can map larcenies, robberies, sexual crimes, murders, and other derivatives of violent crime.  It allows the ability to not only analyze the crimes but predict where crimes will most likely occur.  

Crime data such as this can be both useful not for domestic knowledge, but also with decision makers deciding on financial budgets nationwide.

Uniform Crime Reporting Statistics

Lab 8 - Isoline

What is SMERG?  It's bad!

Blog Map 8 - Dot Density

Population Density - Each Dot Represents 50,000 People

Lab 7 - Proportional Symbol Map

Lab 6 - Color Choropleth

           Comparing the printed version versus the digital version shows very abrupt differences mostly with the background.  The digital version correctly shows the background color I chose to allow the map to "pop".  However, the printed version looks almost pink, like a medium rare salmon pink.  The other main difference is with states like Alaska, Nevada, and Tennessee.   The color on the digital copy looks lighter than the printed version.  Between the two I actually prefer the printed version for this very reason.  When designing it, it originally looked as bright as the printed copy, but, most likely due to the compression for the web, the color lost some value.

Blog Map 7 - Wine! (Proportional Symbol)

Blog Map 6 - Color

Blog Map 5 - Choropleth

Lab 5 - Grayscale Chroropleth Map

Natural Breaks

Equal Interval

Data Source - FBI Crime Data

 Lab 4 - Boundary Files

Penn State - University Park Campus Map
 

Zoomed in on the campus

Legend

Lab 3: Typography

Lab 2: Mollweide Graticule

This map uses size of the font to depict which area has more persons with the same surnames.  An example would be Murphy which is typically from South West Ireland.  My family's lineage is from the Murphy's in the East in county Wicklow.

A bigger version

Zoomed In On A Portion

Making Thematic Data Work

The Goode-Homolosine projection is an interesting projection as it is a compromised projection that preserves the relative shape of the continents.  It appears to distort both shape and area.  It is almost as if you peeled the world the same way you would an orange if it was all one piece.

Goode-Homolosine Projection

Lab 1

Map of Burglaries in Washington D.C. in 2008 during the economic collapse.  I like the use of dot density to show crime hot spots during a time of vast turmoil.