Since I work closely with the Administration of Child Services (ACS), I wanted to use NYC Open Data and look at payroll data before and after covid 19. What I found was a data file titled
Citywide Payroll Data (Fiscal Year).I filtered the data to show ACS data for the 5 boroughs from 2019-2022. The 3 visualizations are overtime hours, median and average pay, and median pay by title description. Other filters included Pay Basis is Annual only, excluding hourly pay basis.
I decided to use Tableau because it is a common program used at my place of work. I was pleasantly surprised by how little experience is needed to navigate the program, and it was fairly simple to select and compare different types of visualizations for the data.
After reading “Against Cleaning”, I wanted to use the data as is and see what it can show. Although I didn’t start with the idea to investigate over time hours the visualization “Median Paid Over Time Hours” showed a significant dip of hours across all boroughs during covid 19. The interesting part of this visualization shows Manhattan, with close to 0 over time hours paid across all 4 years.
A challenge with my visualizations were adding labels to the boroughs as side from the key. A viewer would need to hover or select the key to distinguish boroughs on two of the visualizations. I was unable to publish my visualizations to be public, as of now the publication can viewed by other tableau users.
While looking through the readings this week, what catch my attention the most was reading about different ways of showing and capturing Data visualization. It was very interesting to learn about different needs of different fields for Data, and what would be the best way to create it. It does make sense to me that graph’s design can be helpful in certain field, but in some subjects, for example films, or others, that require more emotional understanding, a graph is not very helpful, and it is necessary to find another type of visualization to show the ideas behind a specific movie, for example.