The dataset I chose to look at is the All City Departments by Payroll dataset for Los Angeles in 2015. It contains the data types of string, integer, and double (float). A record in this dataset consists of Department Title, Year, Job Class Title, Projected Annual Salary, Q1 Payments, Q2 Payments, Q3 Payments, Q4 Payments, Payments Over Base Pay, % Over Base, and Total Payments.
Based on the Wallack and Srinivasan reading, ontologies are “systems of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them”; essentially, they are ways of defining realities surrounding an individual or group. In the case of this dataset, it seems the ontology is concerned with categorizing the data, namely salaries, based on city departments.
This data would be of most useful to someone whose main concern is finance, as that is what the dataset really highlights. For example: how much the city is spending on paying workers in total? Which department is spending the most money? How do these figures compare to other cities of a similar size? How do these figures compare to figures in past years? Specifically, this dataset focuses on the differences in total salaries for the workers in each department, so someone interested in the distribution of the city’s money may also find this data illuminating.
I think one phenomenon that this dataset shows is that the LAPD salaries take up almost ¼ of the total amount paid to city workers. This leads to a lot of potential questions, such as: how does this percentage compare to other cities? Why is it such a high percentage of the total amount of money spent on salaries? Overall, I think this dataset really highlights the stark differences in the total salaries of all the city departments. However, what is left out of this dataset, which I think is really critical, is the size of the department, i.e. how many workers does each department have? Without knowing this information, it is impossible to tell what exactly is causing the discrepancies in total salary between departments. Does the LAPD have many more workers than the other departments? Or are policemen being paid more than the average city worker? Without these numbers it is impossible to say.
If I was starting over with data collection with a different ontology, I would again look at finance and look at the salaries of city workers, but I would organize it not based on department but based on position within the department. So, instead of comparing departments against each other, the dataset would focus on comparing the salaries of those in a higher position in the department to those in lower positions and look at the distribution of the total salaries between these groups.