This week, I will be examining the dataset titled Funds Relating to Housing and Homelessness. The dataset consists of all the funds in the Balance of City Funds that relate to housing and/or homelessness. It provides us with various kinds of information associated with each fund, such as the name, cash amount, source, associated department and contact person. An individual record in this dataset consists of one fund, with other informational categories attached.
The ontology of this dataset, defined by Wallack and Srinivasan as a system of categories and their interrelations by which groups order and manage information about the people, places, things and events around them, is one rooted in an administrative viewpoint of the various funds listed in the dataset. This is evident through the kinds of information that are being collected: information such as the purpose, cash amounts, and whether the fund is within budget will best help administrators to determine how they should allocate funds in the future. These could be current or future administrators, or they could be officials in other cities looking to implement similar programs. Such information could be helpful in planning for similar funds.
This dataset provides me with top-down understanding of the various funds within the city budget that are targeted at housing and/or homelessness. I can see what issues these funds are aimed at, which department is planning it and who is providing the funding for it. For instance, the U.S. Dept of Housing and Urban Development provides funding for 12 of the 39 funds listed in the data set. I can also see that most of the funds fall under the Housing and Community Investment Department for LA City. I am also able to see, through the purpose field, which funds are specifically targeted to alleviate homelessness.
However, one pressing thing left out from this data is the impact that these funds have had on communities, as well as the success and failure stories in the execution of these funds. As some of them were instituted several years ago, it should be possible to discern some impact of this fund on the directly-surrounding community. Another way of looking at this, and another ontology, would be to focus on those who have benefited from these funds and how their lives have been impacted.
I like your commentary on the top-down nature of this data. It seems like a lot of these datasets have that ontology—one that aims to organize around the structural and bureaucratic effects of the information at hand. Of course this is due to the unified source: the L.A. Controller’s Office, an entity whose primary aim is to record the movement of funds within a city.
I think this dataset is a really good example of what Wallack and Srinivasan brought up about the “digital divide” between information systems and affective experience. In the lens of this data, homelessness, a very salient and affective issue in society, is reduced to a glorified receipt. It shows how powerful narratives can be in the inclusion or exclusion of information and also how choosing the right ontologies is paramount in being able to correctly influence representations of reality in digital humanities.
Nice work! Remember, the data types are everything listed in the top row, not just some of them. Together, they represent a record for the individual funds.
This data set is really interesting, your mention about how this data is rooted from the viewpoint of administrators makes a very good point. That could also be linked to how the budget gets allocated to different departments, which also determines how effective they are. Administrators are also not very likely to understand the needs/problems of the homeless population, either…
This ontology is definitely more on the side of just being presented as data, with no real narrative — the numbers and straight data make it seem very impersonal and detached, which is the opposite of how the problem of homelessness should be attacked. It would be interesting to see this data over a longitudinal study, as maybe a narrative could emerge from there of how effective this particular way of funneling the budge has worked (or not).
I really enjoyed your choice in dataset and reading your blog post! I think homelessness is a problem that is often overlooked in our major cities across the country, and in this coming election we actually have the opportunity to vote on measures that will hopefully benefit homeless people in the long run. That’s what makes this dataset so interesting to me. I wished this dataset had some sort of visual representation of the data, but this is out of your control. I agree with your suggestion of a different ontology – I would be interested to see how these sorts of city funding programs have further affected people’s lives, whether homeless or not.
Nice observation pointing out the lack of evidence pertaining to the effect of this funding. I think it really speaks to the divide of supposedly objective evidence opposed to subjective experiences that aren’t particularly quantifiable. Without understanding the effect of this funding we cant make decisions that relate to the specific context in which this money is dispersed.
You did a great job of identifying the dataset’s ontology. It was a great point you made about how it’s rooted in an administrative viewpoint of the funds.
I think it would be interesting to see the impact that the funds have had on the communities; however, how would you suggest that the data type is represented in the dataset? Would the impact be presented on a scale of 1-10?
Overall, I think you did a good job with your evaluation of the data shown in this dataset. Your explanation of the flow of the money describing where it come from and where it was going to allowed me to get a sense of what the city is doing for its homeless population. Your question at the end concerning the impact of the expenditures does raise a good point, but I believe that that type of data is outside of the scope of this dataset.
I think you make a great point that this data set was created by administrators and for administrators. As someone who has spent a lot of time working with community organizers to alleviate homelessness, I agree that seeing which funds made an impact would be beneficial information regardless of whether or not the recorders are trying to tell a story. Without knowing how these projects are actually helping the community, policy makers might have a hard time discerning how helpful the projects really are. But maybe thats why they’re leaving out the information in the first place! Great job!