I looked at the Funds Relating to Housing and Homelessness dataset. This dataset includes information on the different funds supporting housing initiatives for the homeless population. The data includes financial data breaking down the individual funds into outstanding, receivables and liabilities. It has been organized into clear depictions on where the fund is from, what it is eligible to be used for and how that purpose is broken down.
A record in this dataset consists of the following major categories: Fund, Fund Name, Cash, Department Name, Fund Purpose, Sources of Funds, Eligible Uses, Fund Category, Ending Fund Balance, Assets, Liabilities, Grant Receivable, Other Assets, Current collected Revenue, Cash Disbursements, Outstanding Commitment, Date Fund Established, Fund Group Name and Fund Type Name.
Wallack’s and Srinivasan’s definition of ontology states that it is a system “of categories and their interrelations by which groups order and manage information about the people, places, things and events around them”. This definition is applicable to this dataset because the data establishes a relationship between the donors, the general public and the government systems operating the flow of funds to these housing initiatives. It provides a clear and transparent picture to see how donations and taxpayer dollars are allocated and what those funds are used for.
This ontology makes the most sense for the government and researchers in seeing how funds are broken down and distributed to aid housing for the homeless population. It makes sense because it shows what each individual fund is used for, as well as how much is leftover. This puts the funds in a very logical and transparent order.
This dataset tells me that a lot of different funds have been set up for housing for the homeless. However, this funding is primarily for housing rehabilitation and housing preservation. Not as much funding is allocated to the building of new affordable housing for the homeless.
Details about the completion of homeless and housing projects are left out of this dataset. In addition, there is not much information regarding the livability and effectiveness of this housing for the underserved population. It is one thing to put something on paper, but it is another to see these housing initiatives in action.
If I was starting over with data collection, I would be interviewing the occupants of the refurbished housing. I would ask questions on if the availability of housing has helped get them back on their feet. I would also ask if these housing initiatives are sustainable solutions to their situation. In addition, I would ask if the housing is suitably furnished, built and located to fit basic living needs.
Very interesting data set and blog post! I like how you included the definition of ontology from the article and explained how this data set did and did not fit into the definition. It was a great how you noted that this data set would be useful for the government, but went on to question if they are using this data effectively to benefit the homeless population or if there are other alternatives.
I really like the layout of your post as it was clear and efficient! I also really appreciated the way you would restart the data as I think it would incorporate a lot of factors that need improvement in LA. Great post!
Great analysis of the dataset! Homelessness is a serious problem in LA. I liked how in your proposed data collection process you would do a more one-on-one humanistic method, gathering information from homeless people directly. I agree that more affordable housing options would alleviate the problem, but of course homelessness is more complicated than just housing.
Dear Melody,
You are correct in observing that the the efficacy of such homeless housing initiatives are left out of the dataset – I suspect that in most humanitarian and social practice projects it is often very difficult to define efficacy at all. In this situation, however, it may be incredibly helpful to have numbers on retention rates of these institutions, in addition to rehabilitation statistics. However, it is not surprising that this type of data is missing, as this study was probably not conducted on the basis of pointed social science research, but merely was a dataset meant for government officials to have as a record of allocated resources.
I really liked how you described the data set and what information you can gain from it. I especially liked how you mentioned what you would do differently to make the dataset more appropriate for your needs. The details you mentioned would really add to this data and help us build a narrative from it.