I chose to view the City Budget Expenditures for the city of Los Angeles. This dataset goes categorizes all information by budget year, fund name, account name, adopted budget, total expenditures, budget change amount, budget transfer in amount, budget transfer out amount, total budget transfer, encumbrance amount, pre-encumbrance amount, account group name, fund name, account, and department. This data set is a complete record of all expenditures by the city of Los Angeles from 2012 through prospective expenditures in 2017.
A record in this data set is constituted by the combination of several inputs in order to categorize an expenditure made by the city of Los Angeles through their specific city budget. This combination of data is used to organize all expenditures into an orderly format that is easily digestible and trackable by the viewer.
Wallace and Srinivasan define ontology as “the distinct systems of categories and their interrelations by which groups order and manage information about the people, places, and events around them.” This dataset’s ontology is how the city budget of Los Angeles is utilized by various city and local level governmental agencies. From viewing this dataset, the priority levels for the use of the city’s money can be distinguished as well as which groups are allocated what sum of money. This data will be found interesting from officials in the treasury department at local, city, state, and federal levels of government. Additionally, any individual wanting more information on the use of taxpayer money can be enlightened by information from this dataset. For example, a viewer of this dataset can find information on how much money Los Angeles spends on salaries for the Recreation and Parks department or how much money is used on the upkeep of the Granada Hills Pool and Aquatic Center by the aforementioned department.
While this dataset does a great job at showing what expenditures were made by the city and when they were done, it fails to go into enough depth to actually allow the viewer to fully comprehend what the expenditure actually accomplished. For example, when looking at the General Services Department category, one can see that just under $5.6 million was allocated toward a fund and account named “General Fund: Maintenance Materials.” This fails to shed any light on what was gained by this expenditure. I believe this dataset could be improved by including very brief one sentence descriptions of what was accomplished by the use of this money for each data point. While this may seem tedious, taxpayers should know what their money is going towards in their community. Furthermore, creating completely separate folders by year would make this dataset more readily understandable because viewing expenditures anywhere from 2012 to 2016 in the same area can cause the data to become murky.
Hello, I really liked how you provided your definition the ontology and then your own interpretation in context of the dataset. What I enjoyed most about this blog post is your critique on what this dataset and what it fails to do and your suggestion to resolve the problem.