This week I chose to analyze the dataset of “Listing of Active Businesses” which breaks down the listings of all active business “currently registered” with the Office of Finance. The dataset’s ontology categorizes nearly 500,000 businesses divided into 16 columns reflecting on the following information: location account number, business name, Doing Business As (DBA) name, street address, city, zip code, location description, mailing address, mailing city, mailing zip code, NAICS (The self-reported North American Industry Classification System, used in classifying business establishments), primary NAICS description, council district, location start date, location end date, and location (geographical coordinates).
This ontology would make most sense from the perspective of either a city planner or city developer as the data seems highly categorical, specifying not only the addresses but also the precise geographical coordinates. With the description of the business establishments, this information would be most useful for businesses with the interest of entering the market determining which competitors are in the area and where they are located. Alongside competitors, the information can also determine complementing businesses that may assist and aide development and growth.
If I were to start over with data-collection and describe a completely different ontology, I would make the dataset more user friendly. Categorically organizing 500,000 businesses makes most sense using an excel spreadsheet, however the retrieval of information can be extremely mundane. A supplement to this dataset could also include a colourcoded map which provides a visual. With all the information already present in the dataset, a map could pinpoint exact locations while the colour coding helps determine the type of business and the concentration of types of businesses.
Hello,
I agree with your point of a visual map using GIS being useful for this data set. Especially those planners looking to quickly use this data to make decisions or plan policy. I believe it is fairly straightforward to do this with this data since there is a location column and although I have not completed this, there is a visualize button on the top toolbar where you can click on maps and use column information from the spreadsheet to create a map using openstreetmap.
That said it would be great if the data set included a pre-made map that has also been pre-edited for us so we would be able to quickly and easily digest the data. I do agree with your point that if you are presenting data to the public and storing it in the repository, if possible it is important to make it as manageable as it can be.
I also analyzed this data set and definitely agree that it could have been made more user-friendly. I think that with the current categories included, the data set would be of most use to an urban developer/planner as well. It’s really interesting how you pointed to how businesses can examine competition in surrounding areas before entering a new market. This would be a very strategic move on their part, especially if they are looking to offer similar or complementary services to other businesses nearby.
Hey ninaliu,
I found this dataset very interesting, and agree with you that it can be useful for businesses to scope out the competition before entering the market. The dataset definitely needs to be more friendly and with the addition of a map, it would make the information much more interactive and user friendly.