Retrieved from: https://www.instagram.com/p/B1eTZp4FrOd/
As the title of this chart indicates, it looks at changes in women's pay between 1987 to 2017. Furthermore, the chart examines women's pay based on ethnicity and income. From looking at the chart, we can note that Asian women had the largest increase in pay compared to white, black and Hispanic women. Concerning Hispanic women, their change in pay was minimal throughout the years.
This visualization does a good job of showing the drastic differences in changes in pay for women of different ethnicities. Although the visualization is easy to read, there are some flaws could be corrected had the data been presented differently. Firstly, the color coordination of the years 1987 and 2017 to the pay could be shown differently. I did not catch onto the fact that the colors of the years were corresponding to the pay-per-year on the visualization. To correct this, a legend would be effective. Furthermore, from looking at this visualization and the source I had retrieved it from, I was not able to answer all the questions I had about this data. I noticed that the visualization does not indicate where this data relates to. For example, is this data for changes in women's pay between 1987 to 2017 in the USA, Canada or elsewhere? This could have been corrected by indicating where this data relates to somewhere in the title.
As previously mentioned, I would have incorporated a legend into this visualization to make it a little easier to read. The color coordination is very appealing, however, not everyone may have been able to catch on to the significance of the color in the years to the data in the chart. I found that the little pictures of women were very symbolic in terms of conveying the message of the data. It shows how black and Hispanic women are oppressed when it comes to receiving equal pay.
I had also noticed that this visualization does not have a baseline of zero which is very misleading. Unless comparing two values to each other, starting the baseline at zero is very important considering that the data will be given a false impression if the numbers were removed.
Ideally, I believe a line graph would be more effective as the significance of the changes and patterns over time would be shown. Line graphs are useful when you are showing changes in trends over periods. Considering that this data is gathered between the years 1987 and 2017, a visual timeline would be a lot easier to read and the data would likely be presented better.
I had also noticed that this visualization does not have a baseline of zero which is very misleading. Unless comparing two values to each other, starting the baseline at zero is very important considering that the data will be given a false impression if the numbers were removed.
Ideally, I believe a line graph would be more effective as the significance of the changes and patterns over time would be shown. Line graphs are useful when you are showing changes in trends over periods. Considering that this data is gathered between the years 1987 and 2017, a visual timeline would be a lot easier to read and the data would likely be presented better.
This visualization was not interactive.
Interactive graphs are beneficial to use when you want to limit the amount of
information to show at one time. This visualization could have been interactive
to show what the average income was for every year. For example, if we were to
click on white women and the year, we would be able to see what the approximate
average was and compare that to other ethnicities.
