Using the L.A. Controller’s Office website, I was able to access the dataset for Los Angeles Police Expenditures. The dataset includes a myriad of data types, including the ID Number, Fiscal Year, Department Name, Vendor Name, Transaction Date, Dollar Amount, Authority, Business Tax Registration Certificates, Government Activity, Fund Group, Fund Type, Fund Name, Account Name, Transaction ID, Expenditure Type, Settlement / Judgement, Fiscal Month Number, Fiscal Year Quarter, Calendar Month Number, Calendar Month / Year, Calendar Month, Data Source, Authority Name, and Authority Link. The record in this particular dataset is the sum of police expenditures for the city of Los Angeles, spanning June 2011 to January 2014. This sum totals up to nearly 4.9 billion dollars.
Wallack and Srinivasan would go on to describe 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’. By this definition, a particular ontology works to build and enact paradigms within a social demographic and situate knowledge within a community. The Police Expenditures dataset collects and organizes information related to all police expenses and funds. Access to this dataset grants the Los Angeles community some level of clarity in relation to the LAPD. After parsing through this mass amount of data, citizens may develop a better sense of what funds are allocated where, and what gets prioritized by local law enforcement. A benign example being – how much is spent on veterinary funds, vs. how much is spent on training programs.
I found a major pitfall of the dataset to be its ambiguity. The spreadsheet is general and unspecific, pointing often to large monetary units categorized simply as “general funds” or “supplies”. Because of the dataset’s vague format, I’m inclined to believe the information is organized in a way decipherable primarily to those familiar with the rhetoric of L.A. law enforcement bureaucracy. Speaking as an L.A. resident and common citizen, I’m pretty lost on what the expansive term “supplies” might entail. I might be interested in knowing how much the city spends on firearms, vs. how much is spent on body cameras. After moving through such a considerable amount of data, I find myself still a little lost as to what is supposedly being “illuminated” by the data. The dataset seems to offer the facade of accountability– numbers, vendors, years, etc., while in reality revealing nothing citizens probably didn’t already know. If I were to rebuild this dataset, I would format the information in a way that is intuitive and legible to average L.A residents. This might mean specificity, or the creation of new fund and expenditure types.
I find your comment on the “facade of accountability” very interesting, and potentially applicable to a lot of datasets. Although many L.A. residents may want to better understand how their police department is spending its money, possibly not as many are inclined to study datasets and even fewer may be able to parse out the meaning of vague categories like “general funds.” From your description, the data does not seem that accessible to everyone who may want to access it, which can decrease its effectiveness.
In reference to the police in particular, I noticed in the dataset I studied, Payroll by Job Class, that police chiefs were paid significant amounts compared to the heads of some other departments. While I might be able to think of reasons for this, the lack of description/explanation accompanying the dataset makes it difficult for me to confirm these potential reasons. You said you would reformat the data to be more intuitive and specific, but I have to wonder why it was not presented this way in the first place.
Thought this post gave nice insight into Wallack and Srinivasan’s definition that a dataset ontology “represents reality”, given that “reality” is always biased, and that a state meta ontology is often incomplete. I enjoyed your commentary on this dataset very much, particularly your comment about “the facade of accountability– numbers, vendors, years” that the dataset offers “while in reality revealing nothing citizens probably didn’t already know”. It is useful to consider the power assumed by the state when terms are kept perhaps strategically ambiguous, obscure, or opaque to the general public.