Listing of Active Businesses is the dataset of my choice to look at this week. This dataset is currently registered with the Office of Finance. An “active” business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations with monthly update interval. The ontology of this dataset can be examined through the content model, which includes 9 content types:
- Location account: the account number of the business location.
- Business name: the legal name of the business.
- DBA name: Doing Business As (DBA) name.
- Street address: street address of the business location.
- City: city of the business location.
- Zip code: Zip code of the business location.
- Location description: abbreviated address description of the business location.
- Mailing information: mailing address, city, and zip code of the business.
- NAICS and primary NAICS description: the NAICS indexing code and a brief description of the business.
The person who created this dataset focused largely on regulation of businesses in terms of their registration information, with a philosophy in using indexing accounts to organize and sort all the individual businesses. People who are trying to locate within a specific range of location accounts, zip codes, geographical coordinates will find this ontology helpful because this numerical values can be easily sorted and searched in the dataset. This dataset displays countless active businesses in Los Angeles in a organized way, which conveys a proof of fact that LA is a prosperous city which is currently thriving and growing.
However, there is still information that gets left out. First of all, besides a short description, there is no a clear column categorizing the type of business. Hence, it is hard to sort and search for business based on types. Secondly, certain kinds of individual businesses were lumped together in this dataset, such as the NAICS category of grocery stores and convenience stores, which does not clearly convey each individual’s business type distinctively. Based on this, I can start over with a data-collection process focusing on indexing and categorizing the types of the business, with distinctive subcategories if needed, in order to create an ontology focusing on the functions of the current active businesses.
Hello,
This is a really well put together blog that gives me a lot of information about the database. I like the layout of the database and it seems like it was organized very well. I can see how this would be very helpful to many people looking for a list of active businesses these days.
I think you did a really thorough and accurate job describing the ontology of the dataset! I also think you made good points on what could be improved about the dataset (such as grocery stores and convenience stores being put together). I would be interested in reading more about the ontology you would create if you were starting over, specifically the kinds of business sub-categories you have in mind.