Critical Response 4

To create an intriguing visualization, you need to find good data. The question is, where do we find it? It is important to understand that whether you are gathering data yourself, or taking data from another source, you must consider things such as human errors, typos, etc.… Was there some type of transcribing error before that spreadsheet got to you.

If you are looking for the data yourself, you need to know where to look. Data can be found using a bunch of different resources such as search engines, universities, data applications, sports applications, political applications, or even directly from the source. Considering all of these different places to look for data make you think, what if you did not come across the information you needed in one place? What if the data you were looking for was spread across multiple web pages? You can use a method called data scraping.

Data scraping is basically a written code that tells the computer to visit a bunch of different web pages, take the data that you need, and store it directly in a data base or text file for you. A combination of programming within the programs Python and Beautiful Soup allow us to engage in this process of data scraping. The author, Nathan Yau, uses this process to collect data from the website Weather Underground to collect all maximum temperatures each day in 2009 in Buffalo, NY. This was a very efficient way to collect this mass amount of data.

Dr. Lynch discusses this same data scraping process in the lecture video this week. He talks about the many different speeches that Arne Duncan has given as secretary of education and mentions the similarities that arise in all of these speeches. Now, it would not make sense to watch every single lecture given by Arne Duncan to analyze the similarities and differences, so he engages in the data scraping process. He introduces a website called Voyant Tools that can do this analyzing for us. I found that this method looked a bit simpler than the one introduced in Visualize This. I liked the visualization of the main words and ideas in the speeches.

Once you have collected your data, you should be open to formatting the information in a variety of ways. Most people use excel which allows us to enter data and experiment with different types of data visualization, but there are many more formats to choose from. Some different formats that you should consider are, deliminated text (similar to the product we saw at the end of the scraping data example), Javascript object notation, and extensible markup language.

What is important to understand is that if you are going to collect data, you need to know where to find it and how to manage it afterwards. A data graphic is only going to be as intriguing as the data that the graphic is built upon.

It is also vital to consider the software that you will choose to visualize your data. People are most likely most familiar with visually representing data by using Microsoft Excel. You can input your data and choose to represent those data in a variety of ways. Google spreadsheets is another platform that allows you to create data visualizations. You can think of Google spreadsheets of the “cloud version” of excel. What Google spreadsheets does offer that Excel does not are interactive charts.

The main idea behind the discovery, tinkering, and exploring with each of these different types of visualization software is that you must be familiar with the tools within the program. If you are not educated about and familiar with the different tools associated with these software to create your visualization, then you will not have a solid platform to start on.

    • Gerald Ardito
      Gerald Ardito

      Gina,

      It is great to see how engaged you are with all of this.

      I loved this:

      What is important to understand is that if you are going to collect data, you need to know where to find it and how to manage it afterwards. A data graphic is only going to be as intriguing as the data that the graphic is built upon.

      As the kids say, tru dat.

      • Gina Raus
        Gina Raus

        Thank you for your responses, Dr. Ardito!

        • Gerald Ardito
          Gerald Ardito

          Gina,

          I am just revisiting this work. I can't tell from your post. Do you have any examples of having done with this work? I am not saying that you should, I just wanted to see and to ask you to post them (maybe as photos?) if you do.

          Thanks.

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