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Chicone1003 / BeatPython

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how do think we could build our stories? #3

Open rosyshu opened 6 years ago

rosyshu commented 6 years ago

from the big concept to small detailed or make it a circle?

Chicone1003 commented 6 years ago

I think it is better for us to use a maps to tell this story(geographically transferring). Because we have no other information.What we could use is age, social status, occupation(it is so rare we could abandon), sex, marriage status. How could we make use of them and make up a perfect story? I do not know how could add them into a maps.

Chicone1003 commented 6 years ago

http://leafletjs.com/ https://plot.ly/python/ https://www.tableau.com/zh-cn These tools could help us combine our data with our maps! Besides, we could add other data like charges, tortures ,causes of malice. I have plot it ,you guys could check them now.

Firstly, we could from world to Scotland to see how many people were accused during witch hunt. Then, we just focus Scotland and use our dataset. But not only use accused_people this file, but also use other data to make a whole story, to tell people how witches were accused this process. My suggestion is background( unexplained phenomena), people( find powerless people to explain them), malice( find ridiculous causes), charges (ridiculous causes), tortures(to show how curl people were). Finally, from small to big.To show accurate number of accused witches from Scotland to World

Some data visualization:

2017-11-24 9 53 18 2017-11-24 10 09 46 2017-11-24 10 03 54
zouder commented 6 years ago

Firstly, we can begin with the comparison with witches we knew before and ones real in history. We can use maps to tell the general information such as the number of accused witches from the whole world to the Scotland since our dataset is based on the Scotland. Next, we can tell more detailed information about witches using other illustrations and find some links between datas and put them together. Or maybe we can focus on a group such as females. But one thing confused me a lot is that in our dataset there are lots of blanks......