The dataset I chose to review was the Housing and Community Investment Service Locations dataset. The dataset describes office locations that are associated with the Housing and Community Investment Department. Within the dataset there are tabs for location type, facility title, address, phone number, hours open to public, hours closed, and a brief service description.
This ontology makes the most sense coming from a planner’s point of view. Someone who was involved with the establishment of most if not all of these sites and is proud to gather all of their information in one convenient dataset for people to view and gather information from it. It is clear and concise and give you exact names, locations, and contact information about each location. Although this dataset is not describing a phenomenon, it is supposed to be providing information about housing and community investment services to the public. It is trying to show how easily accessible all of their locations are. The dataset briefly describes what each location offers and what times the offices are open. What it fails to include is valuable information that would actually be relevant to the public. Most people can just google search where the nearest community location is and the results will give you the same information; location, hours of operation, and contact info. I think the data set would be a lot more valuable if it included things like how successfully each location actually runs, or how helpful each office is, are there long wait times?, etc.. Members of the community are probably going to be more interested in whether or not a certain office can help them with their specific needs or if they are better off going to a different location.
If I were to start the data-collection over, I would do exactly as I described above. I would get insight from members of the community about what type of data to collect in this dataset. What would be most helpful to their community? I think the dataset would contribute more to the community if they contributed first hand accounts of dealing with each office location. This would help rate whether or not an office is helpful. I might consider including how friendly, helpful, or flexible the staff is. All of these would be better approaches to the data-collection process for this dataset. This information is more crucial than having basic information that could be easily found elsewhere.
Yes! You got it! That’s exactly what it means to think about ontology — to figure out whose categories make sense for whom.
I really liked and agreed with your ideas about getting insight from members of a community. I think that is the best way to enhance a dataset. Furthermore, I agree about your ideas about making community descriptions for insightful and first-hand.