{"id":1101,"date":"2016-10-23T17:37:35","date_gmt":"2016-10-24T00:37:35","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f16\/?p=1101"},"modified":"2016-10-23T17:37:35","modified_gmt":"2016-10-24T00:37:35","slug":"best-city-in-florida-blog-post-4","status":"publish","type":"post","link":"https:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/23\/best-city-in-florida-blog-post-4\/","title":{"rendered":"Best City in Florida – Blog Post 4"},"content":{"rendered":"

The \u201cBest City in Florida<\/a>\u201d data provides 13 \u201cquality-of-life variables\u201d for 20 cities in Florida, including income, commute, job growth, physicians, murder rate, rape rate, golf, restaurants, housing, median age, literacy, household income, and recreation. No data type specifies the unit it uses, and while I can assume that income is measured in dollars per year, I am less certain about data types like recreation\u2014does this refer to the number of recreational facilities in each city? In this case, some metadata would be helpful.<\/p>\n

In spite of my uncertainty about some of the data types, I created several data visualizations using Google Fusion Tables. I found that bar charts were the most direct way to visualize the data (since I had a relatively large number of data sets, I chose the bar chart over the bar column chart). A scatter chart would have also been effective, but I found it more difficult to keep track of data points and to compare different data types in this format. Since I did not observe any change over time reflected in the data, I did not use a line chart.<\/p>\n

As an experiment, I began by creating a bar chart that included every data type. The city \u201cnames,\u201d designated by the letters A-T, appear on the x-axis, while the measurement for each data type appears on the y-axis. As you can see, the resulting bar chart is flawed in several ways:\"screen-shot-2016-10-23-at-5-19-00-pm\"<\/p>\n

First, the bar chart appears very crowded. It is difficult to interpret all the data at the same time, and thus to effectively compare them. Also, the units of measurement differ for each data type, which also complicates comparison\u2014average housing prices may seem extremely high in comparison to number of restaurants, but it is not necessarily relevant or helpful to compare these things. Finally, the scale differs for each data type, rendering some bars scarcely visible. Because housing prices are so much larger than murder rates, the latter data type appears tiny on the bar chart, when in reality murder rate has a large influence in a much different way than a housing price. While it is interesting to view all the data in one visualization, it is hardly more illuminating than viewing the data in Excel.<\/p>\n

At this point, I started to create bar charts incorporating only a few data types. I realized that it was most effective to compare data types with the same units of measurement, or at least those with similar scales. For instance, since the numbers of golf courses and recreation facilities, respectively, are on a similar scale, a bar chart comparing them is easier to interpret than my first bar chart.\u00a0\"best-city-in-florida-recreation-and-golf\"<\/p>\n

It becomes clear that, in general, there are more recreation facilities than golf courses, and that the number of golf courses seems to vary more from city to city than does the number of recreation facilities. However, despite the similarity of units and scale in these data types, comparing them does not necessarily illuminate anything significant about relative quality of life in each city. The fact that one city may have significantly more recreation facilities than golf courses may not affect every city resident equally, or even factor into quality of life much at all.<\/p>\n

It is only when you can see a correlation between variations in each data type that comparing data begins to illuminate something about quality of life. In comparing housing prices to household incomes, I adhere to my notion that the units and scales of each data type should be similar while also tracing a thematic similarity between the two data types. For instance, I would expect household income to generally increase with housing prices in each city. Yet the bar chart reveals that this is not always the case:<\/p>\n

\"screen-shot-2016-10-23-at-5-15-41-pm\"<\/p>\n

While the city with the highest housing price has a greater household income than the city with the lowest housing price, this is not the result of a consistent trend. As a result, I am able to conclude that while overall quality of life may not be lower where there is a greater disparity between household income and housing price, another factor (for instance, a lower murder rate) may have to improve quality of life in order to compensate for this discrepancy.<\/p>\n

Finally, it is probably simpler to view a data visualization that features only one data type. Separating household income and housing price into separate bar charts allows you to notice differences within one particular data type, allowing for a more in-depth understanding of each. However, while the bar chart with two data types is perhaps more difficult to interpret, it allows for more direct comparison of each than if I were to simply compare two different bar charts.<\/p>\n

Since I am new to creating data visualizations, I was a little confused by the data summarization function. Though the tutorial recommended using it, the \u201csummarize data\u201d button did not seem to make any difference in how the data appeared on the charts, other than requiring me to specify minimum, maximum, average, or sum for each value. \u00a0I am wondering if summarization makes more of a difference with more complicated datasets, or if I am just missing something.<\/p>\n","protected":false},"excerpt":{"rendered":"

The \u201cBest City in Florida\u201d data provides 13 \u201cquality-of-life variables\u201d for 20 cities in Florida, including income, commute, job growth, physicians, murder rate, rape rate, golf, restaurants, housing, median age, … Continue reading “Best City in Florida – Blog Post 4”<\/span><\/a><\/p>\n","protected":false},"author":50,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1101","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1101","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/users\/50"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/comments?post=1101"}],"version-history":[{"count":0,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1101\/revisions"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/media?parent=1101"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/categories?post=1101"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/tags?post=1101"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}