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Many of this can be a reflection on the number of residents per neighborhood and access to a larger group; I did not look into or consider these prior to my analysis. Instead, I began the first part of the analysis by first collecting the open data from WPRDC's website, assessing which district in the city had the most ballparks and then zoomed in to the three neighborhoods of choice from there. 

From this exercise I learned that it is hard to find a middle ground between representation of data and accuracy. It was really hard to capture the amount of courts in one park because of the way my data was organized. I could not simply change the size of the dot to represent the density. This of course was a fault on my end that I would correct in future. I also discovered the difference between static representation and interactive maps. I found that the interactive map better represented the data across all neighborhoods at a scale was was easily understood. I was disappointed with the scale and quality of representation I obtained when exporting my map to a still image, however increasing the size of the symbol only ended up masking others below and around. This is one of the many areas I see carto lacking in quality. Pretty maps do not always = pretty maps. 

Caricature and exaggeration is a choice that should be made upfront based on the story you are trying to tell. By making that choice earlier on in the process after the initial analysis, it will be much easier to determine the best route for representation. 

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