This data set certainly has provided some great raw material for a lot of visual analysis. There are some out in the media who criticize Mass Shooter's approach to collecting this data (specifically their definition of a mass shooting), but the fact that we have this wealth of carefully curated data is a real asset to the overall gun control debate.
I really like what you did with the longitudinal quartiles...very elegant. I don't know what to make of the temporal distributions other than notice the 'Do The Right Thing' phenomenon, i.e. more violence tends to occur in the hotter months.
I also put together a visualization using this data, focusing on some slightly different questions. Take a look if you're interested: Pixel Drifter: Using Tableau to Analyze Mass Shootings in 2015
Thanks for the reply! It is such a gripping topic.
Yes, regarding the longitudinal quartiles, I am quite happy about it. Initially, I tried to put a marginal boxplot under the map. It kind of shows the summary density. By using colors for the quartiles, it makes the viz so much more interesting. I also had latitudinal quartiles, included in the workbook. But it looked less interesting in comparison. I might have included it all the same, just for the sake of comparison.
Does the temperature has to do with the incident rate? Something that might be worth of further research.
From your viz:
1.It seems for death number >=8, the number of incidents have increased 600% this year!
In general, things are getting worse.
2.for the two incidents in 2013 and 2014, the dates are creepily close.
3.there are a few typos in city name spelling. Phoenix is spelled two other ways. I have to fix them first. It may change a bit the ranking in your 3rd tab.
Yeah, I noticed that too, namely that 2015 pulls way ahead when you refine the definition of a mass shooting to be higher (and not include wounded). Very sobering.Regarding the data errors, that is something I should fix as well. I've got an Alteryx workflow that I used to prepare the data, and I already corrected a few mis-spellings that I caught the first time through. I missed the Phoenix examples. This is why crowd-sourcing / checking this kind of data is very important. :-)