8:58 got into my working spot, the data is already up.
9:07 fallen at the first hurdle, the data is in a KML file and I can’t figure out how to download it and get it into tableau/alteryx, this may require some googling.
9:35 finally figured out how to get it into alteryx with some help from Coach Kriebs, inevitably more issues are eon the way
9:52 data prep is done now I think my alteryx workflow is beyond garbage and could have been done much quicker but hey it worked.
10:56 been trying to think about what I want to do with the data for a while now. First thing I thought was how many drones there are per capita, but its not overly interesting is it. The issue is that without joining more data from outside the story I can tell is quite limited. I’m supposed to find insights and exploring the why in the data but at the moment it’s proving difficult
11:04 times are tough, I’ve eaten my allotted snacks for the day.
11:05 rereading Andy’s blog he’s saying that we probably shouldn’t use all the data, this makes me think I should drill down into the state level and look at one state perhaps. I think I’m getting closer to trying to analyse something for its comedic value rather than its statistical importance, which is worrying
11:47 I’ve done some thinking (and some more eating – lunch is gone) I think I need to essentially find some anomalous values, hopefully from these I can spin a story. It seems intuitive that in less populated and potentially harder to navigate regions that the use of drones would be more popular as it reduces travel time of public services particularly I think in policing where a lot of the drone work would be surveillance
11:52 The simplest way for me to do this is essentially to make a really long dashboard which details my thought process and answers questions step by step, but I also think that the objective is to be more analytical and less exploratory in our analysis. I think its getting to the point where I just have to put something on tableau and start making a dashboard now.
12:00 Wisconsin ranks very highly in terms of being a much higher rank for how many drones it has vs how few people it has. Also when comparing number of departments using drones per capita it has the highest value, so I’m focusing on Wisconsin.
12:21 I’ve spent some time researching Wisconsin and essentially trying to work together a story however I feel Like I don’t really have a lot of the data needed to make strong analysis and many of my insights seem speculative at best. I don’t feel like I’m quick enough at exploring the data to come up with a good story so I’m having to manufacture one as I go.
12:41 still not really got much in tableau, still searching for story lines, think I’m going to have to say that it has a very harsh Geography (I think it does at least?), difficult weather conditions particularly during winter (snow and ice) and isn’t very densely populated (24th in the USA) can play on the numerous famous serial killers of Wisconsin also.
13:36 I can see where the dashboard is heading now, don’t think I particularly like it, but I can see where its going, the major issue is that many of the insights are largely anecdotal and no very data driven and to get a point across I am having to story tell a bit, its not a particularly interactive viz its more a stationary informatic made in tableau.
14:48 I’ve got some more space at the bottom of dashboard, time for some more spurious anecdotal analysis methinks.
15:21 I think I’ve finished run out of time to make the dashboard pretty really, the data in it is rubbish, the insights made are cherry picked and my writing style as per is long winded and waffley.
Cheers for reading, hope you enjoyed (⌐■_■)
If you want to see my dashboard it can be accessed here https://public.tableau.com/profile/tim1752#!/vizhome/Dronedash/WisconsinDashboard