For this week’s blog post, I chose to explore the Neighborhood Council’s Expenditure for the fiscal year of 2014 through the L.A. Controller’s Office. I thought this would be an extremely interesting section to explore as I would get to know the “behind the scenes” aspect of what types of things Neighborhood Council’s invest in.
Some of the data types included in the expenditure includes the name of the neighborhood council, date of the purchase, description of purpose, vendor, spending category, task, and the amount spent (expenditure). There are records which go under each of these specific data types.
Srinivasan’s and Wallack’s article highlights the discrepancy that is too often found between ontologies of the state and local communities. Through their definition, this becomes obvious as the dataset truly reflects a state ontology. As they describe, “State data systems are the infrastructure of administration” (Srinivasan, 1). Thus, the data is found to be very structured, with only 7 categories used to fully explain neighborhood council expenditures. It is easy to see that this ontology makes the most sense to a state’s point of view as the information can be easily found through “spending categories.” From this dataset, I can only see a cut and dry version of what goes into the upkeep of a neighborhood. A community ontology is notably absent as no history is provided regarding specific neighborhoods or what conditions are unique to each of them that would draw a connection to the “why” of each expenditure. For example, an “ANC-Narconon Drug Prevention and Education” program is purchased and put under the simple “Task” of non-profit. There is no information about why this program was brought in or its influence on the particular neighborhood. In addition, there are several “operational expenses” that only offer a vague description of “MISC PERSONAL SERVICES” without giving any more detail of what these specific services are. Overall, this ontology reflects the state’s need to be concise and to the point, without offering any community perspective that could highlight the diversity of each neighborhood.
If I was starting over with data collection, I would create an ontology based on the community’s experience, and therefore their unique reality and what they are surrounded by. I would make sure to include the history of each neighborhood and more detailed reasons for the necessity of purchasing specific things. Most importantly, I would highlight both the immediate and long lasting impact each purchase has on the members of its neighborhood. Thus, the state would come closer to having what Srinivasan and Wallack describe as an “effective engagement with communities” that would prevent an overshadowing of citizens’ concerns.