Often times, when we look at the datasets around us, we take the information for granted and accept the presented ontologies without question. This lack of awareness might be a problem because we would likely not realize it when data is presented in a way to manipulate your understanding.

We should practice our analysis of data. For instance, we can look at the FY15 Fees, Fines and Penalties chart in Los Angeles City. This type of this dataset is a chart and the factors that constitute a record in this dataset include: the department name, revenue source name, revenue collected, revenue budget, fund name, revenue class name. According to Wallack’s and Srinivasan’s definition of ontology in ”Local-Global: Reconciling Mismatched Ontologies in Development Information Systems“, this dataset’s ontology is the different slices of the pie, such as Solid Waste Fee or Parking Fines. The different types of fees and the thickness of the slices make most sense to the City Government as it helps visualize the most “profitable” sources of revenue from fees, fines and penalties. Also, each governmental department might also find this chart useful if they filter to only their department and see which fees result in the most revenue. This chart seems to tell us that Solid Waste Fee and Parking fines are the two largest sources of revenue and, together, they bring in more than 50% of the all fees, fines and penalties collected. In this dataset, many details are left out, such as how many parking tickets are issued and for how much, and broader information is also left out, such as fees collected outside Los Angeles City.

If I start over the data collection process, I can categorize the fees, fines and penalties from the payer’s perspective. In other words, instead of looking at the revenue collected by the government, we can look at the money paid by each individual. The shift in perspective changes the characterization and naming of categories. For instance, Municipal Court Fines, Private Transfer Station Fees and Franchise Income-Public Education & Government fees might all be categorized as Municipal Fees and parking fees might be called parking tickets instead.

In fact, if we also collect some additional information, such as salary, whether he/she has a car, etc. With the collected data, we can then understand for instance, what percentage of the population (that has a car) pay all the parking fines and how many tickets are issued every year.

Though the FY15 Fees, Fines and Penalties dataset from the Los Angeles City is presented in a relatively neutral, unbiased way, there are still aspects that can be misinterpreted, therefore, it is important to be aware of the information that is presented to us.



*This is Blogpost: October 19 that I email you Week 9*