This week I analyzed the Listing of Active Businesses in Los Angeles, which I found on City L.A. dataset. This spreadsheet includes all the businesses that are currently registered with the Office of Finance. It is a reliable source of active businesses since it is updated every month. However it requires owners to report the cease of their business which may create some inaccuracies and a ceased business may still be on the dataset.
The reason I chose this dataset is because I’ve always wanted to find a hidden gem cafe in Los Angeles and make it my secret hideout and study spot. By filtering through the Doing Business As (DBA), I was able to find a couple of cool coffee shops, including Blue Bottle Coffee, which I am highly likely going to check out.
Now, let’s take a deeper look into this dataset to see what else we can find:
Dataset’s Ontology
This dataset is organized 16 columns of information which includes: business type, business name, location, origin, mailing address, North American Industry Classification (which furthers classifies the business), and Council District (the region where the business is located).
Point of View of Ontology
Originally this data was designed for the Office of Finance to keep track of the businesses around Los Angeles, most likely to track how the economy is doing. However, this data could also be very useful information for a business trying to enter the market in Los Angeles. By using this data set, they can determine if the market is already saturated with competitors and the exact locations of their competitors. They can also see where the businesses that complement their product or service are and strategically locate their business near them. The data can also be very useful for city planners to determine the best layout of the city.
Missing Information
Although there are a lot of businesses recorded, it did not supply any information regarding inactive businesses. I think it’d be very interesting to see which type of businesses has gone inactive during what time. This may be useful information to see which types of businesses are most affected by the economic state. Furthermore, some parts of the data were left blank, which will affect the overall accuracy when pulling numbers from the data.
New Ontology/POV
If I were to recreate this dataset, I’d want to make it in the point of view of the consumers and include the frequency of each business and the rating of them (for restaurants and popular places, this data can be potentially be pulled from Yelp). It would be interesting to see if the poor ratings on Yelp actually translate to the business becoming inactive.
Wow I love Blue Bottle Coffee!! We should go sometime. There is one in Playa Vista that was right next to my internship this summer!
In terms of your blog post, I really appreciated your emphasis on the NAIC information and incorporating that into other datasets. Also, your suggestion to add Yelp reviews would be an awesome addition to the data set that would make it much more useful to the general population of Los Angeles.