The dataset I have decided to analyze is the “Restaurants Active in LA” that is updated monthly to reflect the “…registered business[es] whose owner has has not notified the Office of Finance of a cease of business operations”. The information provided by the dataset includes the name of the restaurant, the address, and the starting date of the location, as well as other details. All of the data is separated and organized based on the restaurants’ location in relation to the other restaurants. For example, if you wanted to look at all of the registered restaurants in the neighborhoods around UCLA, like Westwood and Brentwood, you could use the draggable map to locate the area and then click on the group of restaurants in that area. It is also possible to sort the restaurants based on alphabetical order of their names, which is helpful as well. The ontology of this dataset makes it extremely easy to locate a specific area and the restaurants in that area, or search up a specific restaurant.
The organization of the data in this collection would be useful for many reasons and to many people. Individuals whom are interested in opening a restaurant may find this dataset helpful, as they could determine how many other restaurants are in the area, as well as how long they have been there. Furthermore, someone traveling in the Los Angeles area may find this useful for determining which neighborhoods they want to visit based on the restaurants in different areas.
Unfortunately, this is not a conclusive collection of information on the restaurants and a lot gets left out. The data provided simply reveals the name, the location, and a few other details, but fails to mention information that would help to form a better understanding of the restaurant. The type of food, the owner of the restaurant, and the overall review of the restaurant could all be important details included in the dataset in order to create a more complete dataset. For example, if I was to start over and create my own ontology for the data collected on the restaurant, I would organize it a little differently than it has been done. I appreciated the arrangement of restaurants based on location, but I felt it would have been helpful for my own personal uses if they were sorted according to not only their location, but also by type of food. Gratned, that information was not provided in the initial dataset, so outside research would be required, but I think it would be a better source for the general public.
Type of food would be super handy!
I also analyzed this dataset and I didn’t even think to mention the type of food! Great note to point out. I just went under the assumption that people knew what an In-N-Out was for example, but for visitors that are new to LA it is highly plausible that they might not know what type of food is associated with certain restaurants. Good Job!
Hi there! I found the dataset you choose to analyze was pretty interesting. Knowing the information about the various restaurants around Los Angeles seems pretty interesting and useful! I agree that just knowing the location and name of the restaurant is not sufficient information. Adding the type of food the restaurant provides would be a great detail to add to the dataset! Great analysis!
I also agree that type of food would be a useful category! I think another useful category would be the current health inspection rating of the restaurant. In L.A. specifically, I noticed that displaying the health grade in the window of the restaurant is a very common practice (might be required?). Knowing whether the restaurant takes the necessary steps to be sanitary and have good cleaning practices is very important in this business.