I decided to analyze the procurement dataset, which contains records of what the City of Los Angeles has bought since Fiscal Year 2012. These records contain fiscal year, department name, vendor name, transaction date, description, unit price, fund name, and other information. The Los Angeles City Controller website also uses some of this data and visualizes it on data cards, making it more accessible, user friendly, and easy to understand. These cards show how much the city of LA spent on certain items. When you click on the cards, it tells you what the items are, why they were bought, and some other facts related to the item.
Wallack and Srinivasan define an ontology to be a way to represent reality through “systems of categories and their interrelations by which groups order and manage information about people, places, things, and events around them.” This procurement dataset represents the city through the eyes of the ‘state’ or governing institution. It breaks down what is procured into how much it costs, where it came from, etc. but hardly provides any information about what it’s specifically used for or why it was bought, and what impact it had after being procured. All these questions that this dataset fails to address are what actually impact community members, whereas the information it addresses about where it comes from, what fund is being used to pay for it, etc. are what the governing body is more concerned about. The data card visualizations are better catered towards the ontology of community members as they show information of what exactly the items are and why the items were procured, which is on the level that citizens experience these procurements in their lives.
The ontology of the procurement data makes the most sense through the eyes of a government official who is perhaps in charge of the yearly budget or has other fiscal responsibilities in the state. It is very easy to see from this dataset how much these items cost, where the money is coming from, who it’s being paid to, etc. which is exactly the information that government budgeters need.
Although this dataset attempts to make what the city is procuring, how much it is spending on these items, and where they are coming from more transparent, it leaves out important information on a community level, such as what neighborhoods or areas these items are being given to, why they are being purchased, to whom these items will benefit, and what impact these items will make on the general community. To a general member of the city, these are the aspects of the data set that are more important than how much an item costs or where it is coming from. It seems that the city controller website is attempting to bridge the gap with the data cards, which clarify the procurements to an extent, but many questions still remain unanswered. When I look at the data cards, such as the one describing the city spending $1,159,775 on leasing golf carts, although I am able to learn that they are used for the City’s municipal golf courses, I am left questioning what neighborhoods or groups in LA most benefit from this and why the city decides to spend money on golf carts rather than some other matter.
If I was to start over with data-collection and create the data from a LA resident’s point of view, I would include not only what was bought and how much was spent on it, but also a description of what purpose the items serve, where it is being used, how much more or less is being bought than the year before, and the impact it has on the community. For example, for the data record of 6,670 soccer balls being bought, it would perhaps include what youth leagues the soccer balls are going to, how many more soccer balls were bought than last year, and show that they were bought because there was an increase in people joining the soccer league. The data cards presented on the city controller site includes more of this information than the procurement data set does, so they are definitely a step in the right direction to bridge the gap between community and state ontologies.
Risha Sanikommu
I agree that for the sake of transparency, the dataset would’ve been better had it offered more information. I think the citizens would have been better served if they knew why these purchases were approved, though with the dataset, I am curious if they could’ve categorized this in a way that was comparable and commonly definable amongst the records. I’m not sure that could be included in a spreadsheet, so I think creating transparency, in this case, could be created in ways outside of a spreadsheet or “data cards.” Very interesting analysis!