Today I took a look at the City of LA’s data set on active restaurants. They created a neat heat map to display concentrations of restaurants in certain areas.
Duarte describes an ontology as an organizational strategy for dividing information into data – yet the data may reflect a bias of the person who organized the data.
The City of LA’s data is a record of all active restaurants in the LA area. You can even view the data as segregated by points or boundaries, that is, how many restaurants are in each designated area. I’m not certain how the boundaries are decided, as some are mini districts of LA (Westwood, for example), and some are just large areas. My guess is by council district, possibly?
The data is additionally separated by Business Name and DBA Name, which stands for “Doing Business As”. I thought this was interesting as I hadn’t known that certain restaurants went by a separate name, as opposed to their “Business Name” (i.e. Denny’s is Marwaha Restaurants Inc.). Most of the smaller business have their owners name as “Business Name”. The data also includes when the business started, its Council District number, and location coordinates.
I think delivery apps might find this information useful, such as Postmates or Uber Eats. I wouldn’t be surprised if this heat map was used for their algorithms to match delivery jobs to drivers passing by certain districts. Additionally, Yelp might’ve used such a data set to recommend restaurants based on your location.
This dataset in the way it is presented via heat map describes a phenomenon of density. If an area has a high density of restaurants you might be able to assume that it is a popular destination or a touristy area in LA. It might also be useful to prospective business owners who want to know where would be a good location to open up shop. If an area is not densely populated, it might not generate as much foot traffic and thus not attract enough customers to your restaurant.
I imagine what gets left out of this data are the smaller food businesses, such as food trucks or stands. Additionally, the data doesn’t describe what kind of restaurant it is – whether it’s fast food or expensive cuisine. I suppose it isn’t necessary, but it is interesting to see how Yelp’s ontology is different, yet based on the same or similar data set. They would organize the restaurants by distance, cuisine, and price range.
Hi Michelle,
Super interesting and informative blog post! I loved the inclusion of the heat maps and completely agree with your insights that the heat map is comparable to a population map. I think it’d be super interesting to account for this (perhaps heat map based on ratio of restaurants to population) and see if the map is different from the current one here. Hopefully we’d be able to find some foodie areas?
Great job on this!
Wanda