I looked at the City Revenue data from the L.A. Controller’s Office. This data set was displayed on a spread sheet which contained the description about what type of department the revenue was coming from and the data relating to revenue. To be more specific, each record constituted of:
Different descriptions:
Fiscal Year the data was collected, The department name, revenue source name, fund name, revenue class name
Different types of codes:
Revenue source code, revenue class code, and the department code
Revenue Data:
Adopted Revenue, Amended Revenue, Revenue Budget, Revenue Collection, % Revenue Collected, Fund.
From my perspective, I don’t think this data set succeeded in focusing on individual people or even individual group, rather their goal seemed to be recording the amount of revenue different types of department was bringing in and a bit of information about what type of business that department is under. Especially after reading Wallack and Srinivasan’s article about ontology, it seems like there’s a huge disconnect between the data gathered by the city vs data that would be helpful for the people within the communities that this data is extracted from. With this type of data, it would be most useful for government officials or anyone in general who are seeking to find out what types of department bring in certain amount of revenue. Seeing that the goal of this data set was to just relay budget vs collected revenue, the data set was able to fulfill that goal. The data set was only provided information about the revenue in relation to a department within the city, which I thought was disappointing. I think the data set could have also included information about the people associated with these department.
Although the data set gets pretty specific on showing the different revenues each department brings in, there’s no information in the data set that relays what businesses these departments are part of and where they are located. I am aware that the data came from businesses within the L.A. county, but L.A. county itself is a huge region. There could be a department of building safety in both Westwood, CA and Pasadena, CA. The location of these department could have a huge impact on how much revenue they bring in. Furthermore, the type of business are associated with also plays a huge role in the amount of revenue they can bring in.
If I was trying to collect data about the city revenue so that anyone could easily access it and get an idea about the revenue each county brings in, I would first exclude all the different types of codes within the current data set since it provides almost no additional information to the layman. I would then include the location and the name of the businesses each department is under. I might also add in what the average amount of income is for that county so people could get a general idea how well the business is doing relative to the county’s economy. It may also be useful to include how many department each county has as well as the county population to see if each county had enough of the said department.
Interesting. I also looked at a data set that had to do with finance and had the same idea. I did not believe that they focused on individualization, rather just recording information for their own organization or benefit. I also noticed groupings in departments and when individual people were mentioned, it was confusing because they had no names. I liked how you mentioned that LA County is such a huge county and the dataset could benefit from comparing regions. I think that would be interesting to see in a dataset like this.