DataLA states the goal of the content model is transparency in means of professional accountability and elimination of the space between the Angelenos and their City. I decided to further explore the data pertaining to foreclosed properties in the Los Angeles area. Of the 1601 listings of data on DataLa, 12 were of foreclosure. If Los Angeles is going to continue to move forward it is important for people to recognize and address the cities weakness. The content model of 2014 Registered Foreclosure Properties is divided into three sections. At first glance, the eye is quickly pulled to the top of the page by an interactive map of Los Angeles. The map marks the locations of the foreclosed properties, that are listed below. The last section provides facts of the selected property such as: APN, registered date, property type, address, zip code, council district, lender contact, property manager and their contact information. Its simplicity allows the user to easily navigate through the data via colorful filters and grants access to properties outside the Los Angeles area.
Entities interested in foreclosed properties in the larger scale would find this data set helpful. It provides important facts such as location, building type and lenders which may point to an alternative reason for delinquency that surpasses the past homeowner. The data is also beneficial to the government for city planning and inflicting policies and procedures.
To me, the dry data gains agency through the map. It allows the user to zoom into different areas of the city, displaying the exact placement of the properties. I was reminded of the phenomenon of the market crash with each dot on the map, which largely represented single family property types. One major improvement that can be made to the ontology is to include foreclosure data of the previous year. In doing that the user would be able to analyze patterns and demonstrate their consistency/inconsistency.
My Ontology would focus on the loans of the properties, rather than the properties themselves. In doing so the data would include loan types and the price the house originally sold for and the year. Their inclusion would allow the development of metadata in comparing market price then and now. I would attempt to provide a more intimate point of view on the property. The data of loan types will allow space to ask for accountability from the lenders rather than just place all the blame on the borrowers. It will lead to further exploration of a weak system and the ways in which it was exploited.