I chose to look at the list of active restaurants through a heat map, which shows the locations in Los Angeles county with the highest concentration of active restaurants. It is separated by several categories which sort the data through different outlets, including the business name, DBA name, street address, city, zip code, mailing address, mailing zip code, description of restaurant, location start date, and map coordinates of the restaurant. Not all restaurants had records of their DBA name, mailing addresses, mailing cities, and mailing zip codes. There were also duplicate restaurant names for chain restaurants, like Denny’s and Panda Express.
This data would be useful for urban city planners and people thinking about opening a restaurant in the Los Angeles County. City planners need to have a general sense of how many (and what kinds of) services are concentrated in different parts of Los Angeles, and this heat map would give an overview of this information. Aspiring urban planners could also refer to this email for more information about Los Angeles’ city architecture. Another possible group that would be interested are people interested in looking for new restaurants, although this would be less popular source for people looking for places to eat.
The dataset does a pretty good job at listing the active restaurant businesses in a cohesive manner. Because Los Angeles County spans a large amount of space, it is very educational to understand where the highest concentration of restaurants are. This helps urban planners and future restaurant owners plan where to concentrate businesses and create new ones, respectively. However, information about how much traffic each restaurant receives is missing from the dataset. It would be helpful to know which places receive the highest traffic, so that urban planners and new restaurant owners can plan accordingly. Additionally, email addresses for each restaurant would be good to have in case people need to quickly contact the business.
If I were to redo this data-collection with a different ontology, I would emphasize the business’ popularity in relation to one another, to judge the most important restaurants from the ones that are smaller. This may shed light to areas of Los Angeles that might have more traffic than others, giving urban planners better insight to reorganize the city in a way that reduces traffic. I would also emphasize the distinction between restaurants in different zip codes, for a similar reason as mentioned previously. This would organize the data into sectors of Los Angeles.
Interesting that you observed that although certain areas may have more restaurants than others the dataset does not tell us whether this means they receive more traffic from customers than other areas. I agree it would be interesting to look at the popularity of the businesses and thus the congestion in that area could be addressed.