The dataset I chose this week is Active Restaurants Heat Map. The dataset is registered with the Office of Finance, and defines active businesses as a registered business whose owner has not notified the Office of Finance of a cease of business operations. The dataset has a fairly straightforward ontology that is based on geographic location. There are 16 columns, labeled as follows: Location Account #, Business Name, DBA Name, Street Address, City, Zip Code, Location Description, Mailing Address, Mailing City, Mailing Zip Code, NAICS, Primary NAICS Description, Council District, Location Start Date, Location End Date, Location.
This ontology makes the most sense from the point of view of a city planner or developer, particularly someone who is interested in tracing the locations of restaurants around the city. The dataset itself has a lot of useful information regarding the location and active status of a business, and the accompanying heat map shows the concentration of restaurants so you can easily visualize where restaurants are located and what areas are most popular.
The dataset lacks a couple of categories such as type of cuisine, type of establishment (upscale, casual, fast food, etc.), phone numbers, operating hours, popularity, size, etc. If I were to start over with this data collection I may want to look at it from the point of view of a restauranteur who is looking to open a new restaurant in the LA area. This dataset’s ontology would still require the location from the original ontology but I would also want to include the categories that I listed earlier. These additional details paint a larger picture and would allow a restauranteur to figure out what areas may have potential to start a new restaurant and do well.
Similar to that of the active businesses dataset, I too found that they aren’t very helpful in the eyes of the average citizen. Much of the data presented would be important knowledge for a city planner or someone in the legal field but there isn’t much data useful for the casual person looking to go out to eat. I agree with the changes you propose if you were to arrange this ontology in someone else’s point of view.
Hi @ariana,
Wow, thanks for sharing! I too looked at a set of data that showed the active restaurants in Los Angeles, but rather than a heat map, I had a map that just showed the number of restaurants. I found it interesting how you mentioned it is pretty useless for the average citizen, and is more geared towards a city official, because with my dataset I thought it wouldn’t have been the most useful data, but still saw some potential uses. I wonder if it has to do with the organization and presentation of the data! Very interesting to see 2 different types of ontology playing out.
Thanks again!
Hi.
I personally really like this dataset just because I am an avid foodies. That aside, I agree with you that the dataset doesn’t really tell us much aside from the geography of active restaurant. I agree that with the categories you suggested because that would make the dataset much more useful to the public, business owners or even food journalist.