In addition to studying DH at CUNY-GC, I am also enrolled in a teacher certification program, so I decided to look at 311 complaints that were dealt with by New York City’s Department of Education.
My research question is: what patterns can be shown in the timing of DOE complaints, and what can we learn from deviations in these patterns?
This entails comparing the frequency of complaints by months and by years, and examining complaints that arise due to seasonal weather conditions.

To be fully honest, my initial audience for this first attempt at data visualization is myself and our Intro to Data Viz course community. But with additional work, I could imagine that these visualizations could be useful to the Department of Education, to NYC schools and to school advocacy groups, including parent bodies. If the DOE can anticipate seasonal surges in complaints, they can be prepared to allocate more staff to managing complaints at those times; if schools and parents can identify seasonal patterns, and situate their own school’s problems within systemic realities, they can think more efficiently about solutions and lobby more effectively for them.

THE VISUALIZATIONS

TABLEAU VISUALIZATION

SHEET 1: Dept. of Education 311 Complaints Map

This map shows the distribution of total complaints over the 10-year period across the boroughs of NYC. It is intended to anchor the data about the complaints in a physical geographical setting. It also aims for user-friendliness by adapting some of the familiar forms used in the Restaurant mapping in Lab 3.

SHEET 2: DOE 311 Service Requests by Month of Complaint

This line graph traces the frequency of complaints from 2010 to September 2020. The month of the year is visible in the tooltip.
The graph gives a sense of annual patterns, with very few complaints in August each year (when schools are generally closed) and with some annual spikes, though these are less regular than the lows: September and January tend to have many complaints (when schools re-open, after summer and winter vacation, respectively); May, and sometimes March, also sometimes have peaks.
With respect to specific years, 2011 had an usually high number of complaints, and the number did not subside substantially after September as in other years; 2018 had some particularly high-frequency months; 2019 began with an exceptionally high number. 2020 is anomalously low, with a plunge in April 2020, when NYC school buildings closed due to COVID-19.

SHEET 3: Complaint Types

This bar graph shows that, by far, the largest number of complaints to DOE were about School Maintenance.

SHEET 4: Top 5 School Maintenance Problems

In this bar graph, I created a group of the top 5 “Descriptors” of complaints within the complaint type of “School Maintenance”, excluding the second-most common category. of “other” since the generality of this label makes it difficult to work with at granularity.

DATA & DESIGN DECISIONS

Although we were instructed to try to begin our visualizations without Tableau, I found myself unable to get a meaningful grasp of the data by inspecting it. I hoped that by replicating some of our lab work, some patterns might begin to emerge for me to grasp onto and begin to see some other directions for thought. So I began with by mapping the locations of the complaints. It seemed to me that geographical patterns would be too difficult to explore with the data at hand, and my limited knowledge about NYC schools– for example, I saw many more clusters of complaints in Manhattan and the Bronx than in other boroughs, but it is possible that there are many more schools in those boroughs as well, and higher population density.
I then thought that looking at Time and Complaint Types might yield some patterns.
For Time, I used the y axis for the number of complaints and the x axis for the time, adjusting the unit to Month. The line graph shows both seasonal and annual variations, which is useful, but I’d like to break down the years and months further, and differently– e.g., to compare specific months across years.
Poking around the Complaint Types data showed that School Maintenance was the biggest problem. Breaking that down further shows a number of the most frequent complaints relate to weather: air conditioning, heating, and snow. The very most frequent complaint is “Unclean Conditions” and “Rodents” are also very high. I’d like to plot these over time, but do not know how to do this.

NEXT STEPS

Prof. McSweeney suggested looking at school types: e.g., charter schools vs. public schools. I don’t know how to access this data, however.
I’d like to plot the types of complaint over time– both, to see whether any problems have diminished or worsened over time, and to see the correlation between seasonal variations and the types of complaints.