I first looked at the data of healthyridespgh but wasn’t really interesting in other people’s biking habits. The only thing that did intrigue me was that I had all this information about street names, and I began to get curious abut that data. If you look at a city like NYC, which is mostly streets and avenues, its data would appear very different from a city like Pittsburgh. I then decided to swap out the healthyridepgh data for the actual list of every street in Pittsburgh. With over 7000 streets, I was able to sort the data into this d3 graph.


  1. Copy all the street names from this list into a txt file
    1. pittsburgh.txt
  2. Create a python file to convert the txt to a csv
    2. pittsburgh.csv
  3. Create a python file that has a dictionary of all possible street types, then sorts through the csv and counts how many of each type there are, then sort through the dictionary and determine the corresponding percentage value for each street type, then export the dictionary with percentage values as a JSON file
    2. pittsburghData.json
  4. Substitute a bar graph block with my json data
    1. index.html

The most beautiful bar graph I’ve ever made. I say this because I spent a very, very long time trying to make this force cluster visualization work. I thought I had lowered my bar enough in terms of what I was asking of from d3. Currently the colour category of the clusters are evenly distributed. After attempting to understand d3 enough to just change even distribution to uneven distribution. screen-shot-2016-11-04-at-9-21-10-am



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