The dataset I have chosen shows restaurants in Los Angeles and the health codes they may have broken.
The ontology of this dataset is as follows. The data is broken up up the restaurant or market by address and name and then describes an identification number and various codes that apply to that restaurant’s inspection. It also tells the reader how many points were taken off for that particular code. It also gives the final score out of 100 and grade, A, B or C. It is pretty straightforward. This is useful so you can search for a particular restaurant by name and address. If you forget the name if a restaurant, you can search by city and so on… Common restaurant goers would find this information useful because it tells them why a restaurant that typically is very clean has as B rating.
As someone who is not from LA, I have strived to try new restaurants all across the county. It always surprises me when certain places that seem nice or clean have a poor rating so when I saw this data set I wanted to know what were the criteria that may have given restaurants a lower rating. Particularly, I enjoy going to Canter’s Deli when it’s late at night and I am hungry for some delicious matzah ball soup. It’s a Los Angeles Jew’s classic. I was shocked to hear that such a well-run establishment got caught for having vermin. It is also useful for those looking to open restaurants so they can see all that goes into maintaining the cleanliness of the restaurant.
What is left out of this dataset is a restaurant’s previous rating. This would be useful to see if a restaurant maintained consistency, greatly improved, or drastically got complacent on their cleaning rituals. Knowing this information could be helpful in choosing a particular restaurant when going out to eat to ensure that you are eating at a safe place.
There are some ID numbers that seem useless for an average person looking at the data so if I were to create this dataset I would change some aspects of the ontology. I would have the violations described (assuming I know the criteria) but I would also describe how the restaurant and kitchen looks to an average person walking in. “Are the corners dirty?” and so on… I would not have the points and the score because at a certain point, an A is an A, whether the restaurant scored a 91 or a 100 so I would not add those extraneous variables.
Hi Charlotte, I really liked your post! I agree that the dataset is useful because it allows you to search for a certain restaurant you may be thinking of. I also liked your suggestions for how you would create a new ontology. I think your categories they would definitely be easier to understand and more useful to the average consumer when assessing a restaurant.
Hi,
I’m also not from LA, and just like you when exploring new restaurants, the inspection ratings is certainly something that affects my decision! I definitely agree with you in that knowing the previous ratings is very important in having a more thorough picture of the restaurant. I like your ideas for a new ontology, because as you mentioned, even though two restaurants got the same grade, their condition can still vary, so that additional information would be useful to know when making a decision about where to eat.
I totally agree, it would be really informative to see a record of a restaurant’s past ratings! I like that the dataset takes time to record exactly how many points were lost because, while a rating is a rating, I would like to see what that rating is based on and how establishments get rated differently. I believe it helps with transparency, since a restaurant’s kitchens are behind closed doors.
I really like this dataset that you have chosen! It was interesting to be able to view the restaurant ratings and reasons for the rating. I agree that the previous rating should be shown in order to see improvements (or lack thereof). Also, your comment that showing the points is pretty irrelevant, as an A is an A, and so on, is a good point that you made (no pun intended..).
I can totally relate, I like to look at ratings from people and their comments about how they thought the food was. It somewhat helps me determine if I want to go there or not. Also being able to see violations and cleanliness of a restaurant will most certainly help me decipher if I am going to eat there or not. Great analysis!