1.\tDescribe your dataset\u2019s ontology
\na.\tLooking through our class blog post, I seemed to go with a popular option by picking the \u201clisting of active businesses\u201d dataset. The dataset is owned and updated monthly by the Office of Finance. The site describes the dataset as a \u201clisting of all active businesses currently registered with the Office of Finance.\u201d And goes on to disclose that an “active business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations.\u201d In detail, the dataset consists of 497,000 rows and 16 columns. Each column is a different piece of information about the listed businesses. Column names are: Location Account #, Business Name, DBA (doing business as) Name, Street Address, City, Zip Code, Location Description, Mailing Address, Mailing City, Mailing Zip Code, NAICS (north American industry classification system) code, Primary NAICS description, Council District, Location Start Date, Location End Date, Location. The dataset also allows for the data to be organized into various types of graphs, charts, and even calenders. I think it\u2019s safe to say there is plenty of data contained in this site.
\n2.\tFrom whose point of view does this ontology make the most sense?
\na.\tThe first thing I thought of while studying this dataset is yelp. I\u2019m an avid yelper myself, and momentarily mistook this dataset as yelp\u2019s equivalent. That was wrong. Though this dataset does allow the user to gather a fair deal of information about each business, it\u2019s data that the average yelp user wouldn\u2019t care to know. Therefore, I\u2019m lead to believe that this dataset is used for formalities of some sort. For example, this information could be of good use to the police\/government. Things like the NAICS code and the Council District point to this. Potential investors would likely also find this information useful.
\n3.\tWhat can this dataset tell you about the phenomenon it claims to describe?
\na.\tOverall, the dataset is a description of the LA\u2019s economy \u2013 generally speaking of course. If there are nearly 500K active businesses in LA then I think we\u2019re doing alright. However, this question also points to what\u2019s missing in the dataset: relevant information to everyday civilians.
\n4.\tWhat gets left out?
\na.\tAs mentioned above, I want to know all about the active businesses of LA \u2013 but I\u2019m not talking about their NAICS codes and mailing addresses. Instead, I want to know what their peak business hours are, what their most popular products are, etc.
\n5.\tImagine you\u2019re starting over with data-collection and describe a completely different ontology, from someone else\u2019s point of view.
\na.\tTo put it simply – if I were collecting this data, my 16 columns would be completely different. They would include hours of operation, type of products sold, average age of shoppers, things like that.<\/p>\n","protected":false},"excerpt":{"rendered":"
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