Tidy Tuesday has promptly responded to the BLM movement and focus this week’s theme on African American’s achievements.
Let’s first have a glimpse on how African American’s achievements being recognized throughout the years.
It looks like it has came a long way, isn’t it?
Apart from different fields, I’m also interested in how gender plays its role along the way.
As obvious as it is, way less African American women have recognisable achievements compare to men despite time or different fields. But one step a time, eventually we will get there.
Furthermore, I have found a dataset from Harvard dataverse about social movements around the world from 1990 to 2015.
From the world map, it is obvious that the data is incomplete or deficient since even countries like China and Russia have more protests than North America countries like the USA and Canada. Nevertheless, we can see that European countries protested the most according to the dataset.
So, given from the map above that China interestingly have a significant number of protests, I decided to see which cities those protests located. Unsurprisingly they were mostly from Hong Kong, followed by Beijing with only one-third of the number from HK.
Then I compared the number of protests in some chosen countries between the year of 2010 and 2015. Looks like Kenya has a significant increase in social movements between the year of 2010 and 2015.
Lastly, let’s look at how protests were responded by government officials. As expected, most of the social movements were just ignored by the governments. Only 862 of the protests from the dataset were accommodated. Initially, I wanted to see how many people participated in the protests that have their demands accommodated, then I realised most of the data are unknown. The same for the total amount of protest as a whole.
I think this is just one of the major issues that we are having with big data these days, although the volume and dimensions of data are increasing exponentially constantly, the amount of fragmented data increase accordingly as well. This, on the one hand, allows us to extract more valuable insights than before, but require way more time to do so on the other hand.
