This example of data visualization is a network map arranged from broad sets of data down to more specific categories. In this map the data being represented is one persons phone calls over the course of one year. It starts with only two main categories after the initial number of total calls, spam and trusted, before it gets very specific, who the actual caller was. The thickness of the lines shows the frequency if the caller, and the lines are easy to differentiate due to their difference in color. From all of this information I was able to learn that the vast majority of this person’s phone calls were spam calls, most of those unidentified. It seemed to be particularly useful to use this type of data visualization because of the sheer number of factors contributing to the data set; it is all arranged well enough that it doesn’t become confusing to look at. This would be particularly useful for people easily susceptible to scams. By analyzing and recognizing trends for scam phone calls, those people can be informed of the attributes and nature of scammers. I also find interest in the fact that this person has called their repair shop more times than their sister.
The data visualization above could only be described as a word cloud. It depicts words used in high frequency in tweets after India’s Supreme Court ruled out the criminalization of homosexuality. The bigger the word, the more times it was used in tweets. The format of this data visualization is important to the message being conveyed. With words like “love,” “LGBT,” and “India” having the most space in the image, a lot of attention is first drawn to them. Even smaller words still carry only positive messages which shows the campaign of the data. Certainly there must have been some hateful comments that were excluded form this research, so not the entire picture is shown. Besides that though, I learned of apparent overwhelming support of India’s Supreme Court decision, and I was able to learn it very quickly. There wasn’t a lot if thought or analyzing that had to go into this in order to interpret the results of the collected data. It’s interesting as to why they picked so many words the be green. There doesn’t seem to be any sort of pattern to the coloring of words, yet the grouping of green draw the eye to those 6 or 7 right by each other.