Drucker-capta / That which can be captured and represented about the world (as computationally tractable information).
The dataset I will be looking at consists of the raw data on each of Los Angeles City’s animal shelters. The various types of data collected include the following:
- Location of the shelter
- Types of animals moved in and out of the shelters
- Events related to the movement of the animals in or out of the shelters (euthanized, sent to foster, impounded, live release etc)
- Number of volunteers
- Number of hours
The data that are recorded are recorded by the shelter location, category of animal outcomes, sub-categories or additional information used to categorise the outcome further and the quantity of the events that occurred. For example, in East Valley Animal Shelter, under the category of ‘animals sent to foster’, ‘cats’ is a sub-category, and the number of cats recorded is 102, in June 2015.
According to Wallack and Srinivasan, this dataset follows meta-ontology. Information was collected by the Los Angeles City Controller, and is based on seemingly objective views, which gives an overall outlook of the quantity of euthanized animals out of all the animals that are brought in or out of the shelters.
This data was collected by a government organisation and is meant as a platform to allow citizens to be able to retrieve data about different organisations and their activities, in their city. However, most of these are quantitative data, such as the numbers of animals that had been euthanized, which might not be as informative, or involving, as they had expected it to be, to regular citizens. Instead, this ontology might make more sense to the government, and the staff of the animal shelters themselves, as they are able to understand and make better use of the data.
Instead of a dataset like this, in order to better match it with community ontology, a data visualisation might be easier for the layman, the citizens, to understand and make sense of, and eventually, use. In addition, the information provided in the category headings are not very informative either, and might require some insider knowledge to be able to interpret as well.
Likewise, this dataset claims that about one of every four lost or abandoned animals that entered the City’s shelters were euthanized. From this long list of data, I am unable to tell if what they claim is really true, because as a regular citizen, I would probably not calculate the total number of animals and compare them with the number of them that were euthanized.
In addition, what is left out of the data are the qualitative data, such as reasons that they were euthanized. Due to diseases, or sicknesses, or due to dangerous, unstable behaviour? It also does not show us if there are any relationships between the various data.
From a layperson’s point of view, keeping in view the phenomenon that this dataset originally was trying to describe, perhaps the only data that are required would be the total number of animals that were brought into the City’s shelters, and the number of animals euthanized, in a year. With fewer data, there might be less details, but it provides the community with a broader view of the situation. Some data that the community might also find important could be the reasons that the animals were euthanized, or where the euthanized animals were brought in from, so that the citizens are able to understand why these animals were euthanized, and maybe come to a conclusion to somehow reduce this number.