I examined the What We Buy dataset which reveals what the city of Los Angeles buys for its dwellers using their taxpayer money. The information is presented in the form of 15 datacards, grouping the dataset into relevant chapters: $12.3 million on 1 AW139 twin-turbine helicopter, $21,929 on 72 pairs of custom fit motorcycle patrol boots, $1,159,775 on leased golf carts, $8,549 on 6,670 soccer balls, $646,533 on 100 Radar Speed Signs, $6,797 on 2,723 basketball nets, $629,218 on 6,492,750 ballots, $4,638,600 on 4,339,676 lbs of thermoplastic marking material, $21,243 Graffiti Buster $530,238 on 5 Toro Groundmaster 5900 Rotary Riding mowers, $13,368 on Federal L.U.S.T. Tax, $1,348,566 on 7,617 fire hoses, $10,654 on 11,988 high visibility white traffic gloves, $161,628 on 30,685 wet mops, and $129,218 on 52,100 frozen rats. The datacards demonstrate that the city spends a lot of tax payer money on recreational sports, policing, traffic systems, medical research, janitorial practices, gardening, petroleum spills, and fire emergencies.
Each datacard then goes into more detail about why the city invests in the object, by answering the following questions: “What’s this?”, “Why do we buy this?”, “Did you know?” In this way, the makers of the LA Control Panel microsite are able to directly communicate with their primary audience: Los Angeles taxpayers and government officials. The questions provide justification for tax money investment decisions by the city government.
From the dataset, the user can see what problems or situations Los Angeles is facing, and the city’s priorities and values. For example, the spending in soccer balls and basketball nets demonstrate that Los Angeles strives to create sports and recreational spaces. The city values building a sense of community through athletics.
There are definitely gaps in the data collection. The data cards are not transparent about which companies and brands these purchases are made from. It would be interesting to see how these objects and materials are distributed throughout the city. The taxpayer demographic is also unclear, besides the fact that they are Los Angeles residents. But how old and which neighborhood?
Wallack’s and Srinivasan’s define a dataset’s ontology as follows: “Communities and states…[such as Los Angeles] represent the realities around them through distinct ontologies, or systems of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them” (p. 1). The “What We Buy” dataset is a way of organizing a dataset into a relevant framework for the intended audience in a way that makes its content accessible.
If I were to start over with the data-collection process, I would be interested in focusing on instances where the city wastes taxpayer money, or makes investments that aren’t relevant to the people’s wishes. I’d juxtapose surveys by Los Angelenos about how they want their tax dollars spent, alongside the expenditure decisions by government officials. Every citizen has a different set of values and priorities for their community. Where is the overlap and how do cities compromise their spending decisions? Are there alternative ways of sourcing these things for the city. Perhaps purchasing used basketballs from the Lakers or local college basketball teams, rather than buying new ones.
I like how your analysis of the data not only identifies the problems, but how you would personally shape the data-collection process as a taxpayer. Sure the data includes details about “Why do we buy this” and “Did you know” to offer a sort of answer to questions about where the taxpayer money is going, but I like how you pointed out that every citizen has a different set of priorities that may not lead to aligned goals with the LA Control Panel. Taxes are not a choice for citizens, but we should have more control and disclosure over what it is spent on.
A really nice breakdown of the dataset by examining the significance behind its numbers. I really like how you argue that Los Angeles “strives to create sports and recreational spaces” based on the expenditure on soccer balls and basketball nets. Also, it is a really nice idea to improve the ontology of the dataset with the surveys of people in Los Angeles regarding government expenditure, just like how Wallack and Srinivasan argue the meta ontology of the state should be more inclusive to accommodate communities’ needs.
Learning about what cities choose to buy with taxpayer money is very eye-opening! I’m glad that the city datacards do include answers to questions like, “Why do we buy this”, because I honestly question the need for over $1 million to be spend on leased golf carts. While it undoubtedly has its flaws and is not completely transparent, I think it is a step in the right direction that L.A. opens up about what they doing with their taxpayer money.
I also thought your analysis of using a different ontology was thought-provoking. I agree that it would be so interesting to see the ways that the city “wastes” taxpayer dollars, however, it would be very difficult to decide on what is considered “wasting” money and what isn’t. It would be quite subjective, and would -as discussed in a previous class- put lots of power and responsibility into the hands of those who do get to decide.
Great analysis! I really enjoyed the part where you said “From the dataset, the user can see what problems or situations Los Angeles is facing, and the city’s priorities and values”. I also did this dataset and was glad to see a different perspective. I agree that a more interesting dataset would be to also focus on where the waste is and to eliminate it. Great job!
Great blog post! If you are curious about which companies and brands these purchases are made from, these data cards are based off the LA Procurement data set, which contains some more information regarding where the purchases are being made from. I thought your observations about how the data cards show what LA prioritizes were really interesting and true. I also really liked the section where you discuss what you would do if you would start over from the data set. I agree that it would be interesting to see if what the citizens want is actually what the city caters to.
Risha Sanikommu