I chose to explore the Payroll by Job Class data set in the Los Angeles City Departments. Los Angeles City began its data collection on January 1, 2011 and ended on June 30, 2015. The data types included are the year, employment type, job class title, department title, hourly wage and the total earnings for that specific position. For this dataset, each individual counted works in any one of the Los Angeles City Departments. All of the information collected on any one of these people constitutes a record.
Both Wallack and Srinivasan seem to characterize ontology as the organization of information and concepts into structured systems. These ontologies situate the existence of specific information into the community through which they were found. This dataset’s ontology is characterized by the data types collected, as it organizes specific aspects of the Los Angeles City Employee, especially pertaining to the professional world and the economic factors directly related to their profession. This ontology situates this profession-based data within the broader context of Los Angeles. Those represented in the data might find this information most useful/illuminating because these individuals can see how they compare to others that also work in Los Angeles City, especially in economic terms. For instance, if I worked in Los Angeles city, I would want to know whether my pay range is within the same ballpark as others who do similar types of jobs. This information could also be important and interesting for someone who is looking at possibly working in a department for the city of Los Angeles. One who is on the job hunt would likely want to know if their salary is competitive with other companies.
This dataset illuminates a few key things. It lists both the highest paid and lowest paid professions. It also shows which types of professions are the most popular or most in need. By this, it would appear that the City of Los Angeles places high priority on law enforcement. For a prospective employee, one can infer that law enforcement would likely have more jobs.
If I were to re-collect the dataset, I would include a few more details to provide a better understanding of the data. I would include the individual’s time in the profession, to see if any correlation between time and salary (i.e. long time in job, higher salary) could be made. I would also include something about the employee’s sentiment in their job field to also see if there is any correlation between job satisfaction and the higher wages. To switch the perspective around though, I would either separate by level (i.e. entry level, senior management etc.) or shift it to the individual’s point of view.