Data Portrait

This project uses two datasets and a further extrapolation to derive its data. In the spirit of data feminism, I must point out that, for the sake of visualization, the data used has been cleaned up, limited, and manipulated for optimization with p5.

The Sketch

The sketch intends to draw out rampant racial inequality and wealth desparity plaguing New York City's public school system. NYC has a long, problematic history with school segregation and, according to the datasets used in my sketch, still vastly advantages private-schooled white students or white students in historically affluent neighborhoods. Being evaluated in the sketch is data from a dataset concerning outdoor learning locations in NYC beginning after the pandemic (at https://data.cityofnewyork.us/Transportation/Outdoor-Learning-Streets-Locations/vtjm-ngnu) and enrolled population of NYC public school districts (at https://data.cityofnewyork.us/Education/2018-2019-School-Demographic-Snapshot/45j8-f6um). I did research of my own with the data taken from the outdoor learning locations dataset and tallied the number of streets belonging to private schools, also including that in my final data visualization.

The Prototypes

The brainstorming process for the sketch was largely panic and procrastination: before this assignment, I'd only used an API once and decided it was too much of a hassle to try again. When I started working on the actual sketch, I was in a deep 2-day pigeon hole of panic and confusion that resolved into calm once I realized how accessing APIs actually works. With the trivial work out of the way, I still couldn't find a compelling way to present the data I knew I would use in a visual sense, and I dove into Data Visualization Twitter for guidance. Luckily, I came upon resources about including interactive maps in web sketches and the spectacular Mappa library for p5, which I managed to use to cobble together a usable, movable map with varying data. I was set on using a selection bar as soon as I drew out my first few sketches, though DOM and Mappa didn't play as nicely together as I hoped, so the selection tool is at the bottom of the sketch out of necessity and not design.

Reflection

My sketch, originally, was centered around neighborhood property values relatgive to number of outdoor learning locations. I finished a working prototype and felt it failed to convey much meaning, and I instead opted to find census data of NYC public schools from the past few years. With this data, I tested a hypothesis that open learning sites 1. were in predominantly white neighborhoods, and 2. were largely belonging to private schools (historically white, affluent institutions with next to no exceptions). The hope was to tease out data beyond the dataset, to inject a greater sense of context into the conversation surrounding recent measures to keep schools safe amid COVID and the general failure to protect people of color. The final data proved my little hypothesis right, as there ended up being a direct correlation between open streets, white populations, and private schools: predominantly white school districts had open learning sites belonging to public schools, while areas in which whites are the minority in the district either have very few open learning sites or open learning sites belonging exlusively to private schools. The number of private learning sites likewise correlate to historically wealthy areas like the Upper West Side, Upper East Side, and Park Slope, all of which hold more sites than the rest of the city combined.

I'm extraordinarily happy with my end result and likely will be including it in a professional portfolio. Between dumb luck and consulting my girlfriend for her comprehensive typography knowledge, I made something that, two weeks ago, I would've scoffed at as impossible and beyond my skillset. Moreover, APIs are an incredible tool that I was too afraid to use in my own practice, and now that I've gotten comfortable with accessing APIs, I'll absolutely be using them in my own practice.