Recently I put the finishing touches on my "California Education Levels by Census Tract" map. This map is part of my project to learn D3 by creating ten D3 charts.

The first D3 chart+map that I completed was "Commute Methods by Poverty Status in California".

I figured I would write up a post giving a rundown of the kind of munging and processing of census data that is going on behind the scenes to make this map. While I am not computing anything terribly fancy, you can imagine doing some much more intricate statistical analyses (as I plan to do) of the spatial data using more sophisticated algorithms.

The Census Data

We begin with the census data, which was obtained through the Census Reporter API to US Census Bureau data. This makes all of the information in the Census available to everyone with a few key strokes. After overcoming my initial feelings of being completely overwhelmed by how much data I had access to, I started searching for interesting-sounding tables of data with keyword searches.

I soon found table B15002, which was education level by gender for every census tract. THat meant it gave a breakdown of the total population over 25 years of age considered; the male and female populations of each category of education level; and a wide range of different education levels specified.

My first step was to reduce the number of categories of education level to a 1-to-5 scale: 1 (less than high school) to 5 (Ph.D./medical school/law school). While this was somewhat arbitrary, it was dome mainly to implement a transform of the original data.

(To be continued...)