{"id":1472,"date":"2017-10-22T15:09:09","date_gmt":"2017-10-22T22:09:09","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f17\/?p=1472"},"modified":"2017-10-22T15:09:09","modified_gmt":"2017-10-22T22:09:09","slug":"week-3-ontology-of-apparel-businesses-dataset","status":"publish","type":"post","link":"http:\/\/miriamposner.com\/classes\/dh101f17\/2017\/10\/22\/week-3-ontology-of-apparel-businesses-dataset\/","title":{"rendered":"Week 3: Ontology of Apparel Businesses Dataset"},"content":{"rendered":"

For this weeks blog post, we are focusing on ontology by selecting a dataset from the City of L.A to discuss. Ontology is one of many organizational strategies for categorizing data and even tells you a bit about the dataset creator\u2019s perspective or focus. I clicked on a random page and came across the \u201cApparel\u201d dataset.<\/span><\/p>\n

This particular dataset, which is based from the Listing of Active Businesses, already tells you the general direction that its ontology will take you just by looking at it first glance. You notice from the title as well as the Business Name column that the businesses included are all obviously apparel included. There are 16 columns total, in which almost all of them are geographically related; location description, street address, location start date, and mailing address are some of the column titles included. Over 9,000 businesses are included, and based upon the two primary factors of geography and quantity, I presume that the creator of the dataset catered the ontology to appeal to a clothing business owner aspiring to either start a business in Los Angeles or expand their business into the Los Angeles area. By viewing the data in various visual forms, one can gain a lot more knowledge about the current success of established businesses and the most optimal opportunities for future business. By comparing the street addresses of the businesses, one can tell whether these businesses are residential or business services. This alone can give the viewer an idea of the size of the businesses listed. The street addresses can also tell you where there are high population and low population areas of apparel businesses, which will be very useful for properly placing a company\u2019s business location in order to optimize growth and sustainability. By tying in the location of these business with the start and end dates, one may gain further insight on how long the businesses may have lasted, and whether or not there are pattern and trends in success or failure of apparel businesses in relation to certain areas Los Angeles.<\/span><\/p>\n

Although these things here that I\u2019ve pointed out can tell a business owner information that will help them with their own business\u2019 future success, the dataset won\u2019t tell all information relevant to predicting success. Of course, because the world around us and markets are constantly evolving and changing, there is no \u201c100 percent\u201d certain way to predict and ensure a business success, however there is plenty of data to research in order to guide the way. As far things that were left out goes, Revenue and Expenses are two columns that would be nice attributes to the ontology of this business apparel dataset that weren’t included. Knowing the expenses might give an idea of what it\u2019s like paying for business addresses (flagship stores, consignment shops, etc.) in comparison to what the expenses may be like when listing your business as a residential address. \u00a0If I were to recreate the dataset in light of a completely different ontology, say maybe to cater to a consumer\u2019s interest, revenues and expenses would be left out, and it would take a more focused approach on business population geographically in order for individuals who might be wanting to spend a day shopping to pick areas where there are many and various shops in a certain location. I\u2019d also include whether the businesses are geared towards men or women\u2019s clothing so consumers don\u2019t end up in an area that may have shops that generally don\u2019t carry their own genders type of clothing.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

For this weeks blog post, we are focusing on ontology by selecting a dataset from the City of L.A to<\/p>\n","protected":false},"author":192,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1472","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1472","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/users\/192"}],"replies":[{"embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/comments?post=1472"}],"version-history":[{"count":0,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1472\/revisions"}],"wp:attachment":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/media?parent=1472"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/categories?post=1472"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/tags?post=1472"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}