The La Controller’s Office offers a series of data sets that describe the circulation and distribution of funding throughout the greater Los Angeles area. Specifically, I chose to focus on the data set entitled “CD 2 Neighborhood Council Expenditures”. Its purpose is to provide the yearly amount spent by neighborhood councils within the San Fernando Valley. It includes multiple datatypes such as account names( the neighborhood councils allocated to districts within the valley e.g. Van Nuys NC, Valley Village NC, etc); fiscal year, which in this dataset is condensed to the year 2014; and lastly the amount spent by the neighborhood organizations within that year.
The dataset appears rather straightforward at first glance. It shows that “Sun Valley Rec Center Field & Restroom” spent the highest amount in 2014, $357,121, a staggering amount compared to the second highest, which was Studio City at $35,957. What we may infer from this information is based on the digression of our ontological point of view. Having grown up in the san fernando valley, I understand firsthand the economical gap between areas of perceivable wealth and areas of perceivable economic deterioration. For example, studio city and valley village are both wealthy areas. This is supported not only by the dataset expressing that both of these cities spent the second and third highest on community sustainability, but also due to increased housing prices, and overall capital growth of the areas. One does not necessarily need a dataset to perceive the gap between these neighborhoods in comparison to other neighborhoods such as Van Nuys (spending 22,519 annually) , insofar that there are visual differences that describe these gaps. Such differences cannot be accurately or completely described in a dataset.
Wallack and Srinivasan’s essay on mismatched ontologies describes this division between community ontologies and bureaucratic ontological worldviews that may alter and reduce information to fit within a specific organizational system. This divide can lead to and promote already established economical inequality and instability within those communities.
If we look at the CD 2 Neighborhood Council Expenditures” we’ll see that Sun Valley has spent the most in 2014. We may want to infer that this is due to the city’s economical wealth, However this would be leaving out important information. In fact, despite the data shown, Sun Valley is far less stable economically then the cities listed after it. This could be problematic if we look at this data without having a hold on its context.
For example, if neighborhood funding is based on yearly allocated funds, their may be a lack of funding for 2015, assuming that Sun Valley may have spent through their limit. This does not consider the likely debilitating circumstance that may have caused the Sun Valley Rec Center Field & Restroom to spend such a large quantity. As a result, potential future funding may alternatively be provided for cities that are already economically prosperous.
Nice work here! Great summary of the reading. This dataset did not have a very good spreadsheet and I’m very impressed at the job you did despite that fact.
Dear Booger,
Your personal narrative as it relates to this dataset reveals a common pitfall in the syntax of data visualization which is lack of perspective. In the process of quantifying and thus simplifying expenditures which reflect real socio-economic variables, the observer may misinterpret the true social forces working behind many of the numbers: for example, in the case of Sun Valley’s copious budget, one can assume either that it was in the city’s interest to pump a lot of money into a public institution within underserved area; however, in light of your personal perspective as it relates to the visible wealth of Sun Valley, we can now interpret the data as being reflective of an already well-to-do area pumping resources back into itself.