Ever since I was a kid I can remember playing baseball and rooting for the Pirates. Despite recent shortcomings, Pittsburgh can still brag about a storied franchise, and the loyal fanbase revels in the Pirates history. The Pirates have won five championships and can claim a multitude of all-time greats including Honus Wagner, Roberto Clemente, and Andrew McCutchen. I was curious as to how much the Pirates influenced baseball around Pittsburgh, and decided to look at how many fields are in each neighborhood.
I first approached the assignment by finding the dataset on baseball playing fields and downloaded the excel file. The graphic and map embedded were created using Plot.ly. From there I used the latitude and longitude coordinates to create a map to view various baseball playing fields around Pittsburgh. Aside from the map, I used excel to count how many occurrences occur for each field, and was still left with a large amount of results considering I started with over 100 fields. After narrowing down neighborhoods with more than one field, I generalized some of the neighborhoods. Oakland, Lawrenceville, South Side, Squirrel Hill, and Perry all had multiple sub-titles that I merged into the one large namesake.
One of the challenges that I had faced was my original idea to track the expenses of the city, however with my current skills the 400,000 listings had made things over complicated. After I had switched ideas to baseball playing fields, I wanted to do a representation of the field dimensions of the outfield. However, there were many fields that did not have data listed for field size. On top of that, there were a number of fields that were multi-purpose, which skewed the dimensions. Wanting to get a set of data that wasn't dependent on other variables, I simply looked to where the fields were located.
In the future I would like to instead use the Tableau map feature instead of Plot.ly, as I learned Plot.ly's map is quite limited in the options that you have to customize it. I believe that I could work population data into this dataset to potentially show a correlation between a large amount of population and the frequency of baseball fields per neighborhood. If there was available data to the counties and districts outside of Pittsburgh, it would make an interesting case as well to compare the two.
Overall it was nice to look into Pittsburgh baseball fields and it made for a good learning experience. Plot.ly is a good start to data illustration, but I believe that Tableau will be the way to go in the future. My dataset was fairly simple, and in the future I will be looking to find more sophisticated information that will allow for a further in depth exploration. Regarding the css, I still need to improve on my formatting skills but have no doubt that all I need to do is practice for my future skills and projects.
Link to Github site: https://samiabuobaid.github.io/pghbd.html