This is going to be a SUPER quick post as this is a SUPER small (but useful!) viz that I whipped up. I literally have about 6-7 vizzes that I could put out but I'm working on an actual big story at the moment that I'm hoping to get picked up by some news organizations so I want to really give it the TLC it deserves. So this week instead of a deep dive you get a shallow wade into a more useful than data-filled viz!
I was poking around looking for some things to work with as far as GeoJSON data (which the new Tableau 10.2 supports!) and I came across the "New Mapping" group out of UK. I poked around a little on their github page and found the GeoJSON for all the Lexington Traffic Cameras! I thought, "Wow, this is neat!" and started building my viz around it... then I thought, "STOP!"...
Where did this data come from? Was it being used anywhere else!? Then I found it... Lexington Fayette Urban County Government had already built a site for this!
Then I realized their site doesn't reformat to mobile and while it does provide live video streams (for about 5 seconds before auto-closing) it required a click on each camera to show the data. This seemed like an unnecessary step so I made the dash below so that if you hover over a point the still image camera data will show immediately (and will refresh upon scrolling over another and back over again). Additionally I created a mobile-specific version formatted for phones! It isn't much but sometimes just improving a UI can mean a huge difference in the utilization of a tool!
The question I always ask myself when re-doing someone else's work
Click the image below for the mobile version or continue to scroll down for the desktop interface:
As always if you have any questions hit me up in the comments below or on Twitter @wjking0.
I knew I would have to do this eventually, I finally came upon a data set so large that I have to split it into multiple posts. I originally was turned on to this data as part of some work I had done with the University of Kentucky Police Department, I had done some work on their crime log and one of the captains thought I would be interested in traffic collision data (Captain Matlock who's super rad and a data nerd himself!). I said oh you mean traffic accidents? He replied, there are no accidents. when he gave me a sample of their data I realized I could deduce several trends in it, particularly in regards to pedestrian and cycling accidents as they occurred on campus.
Being an avid cyclist myself I saw the potential for this data to really help and inform other cyclists and people working on the planning of the University of Kentucky's roads and pathways. One of my last days as an employee of the University of Kentucky was spent with the cycling committee briefing them on this data . They then informed me that this looks to have come from a larger data set from crashinformationKY.org. When I pulled up the site I was giddy with excitement at the fact that there are so many data fields and so much historical data, way more than I had originally been given by the UK police department.
Unfortunately, the yearly downloads from that state police website were not very functional. They were .DAT files but neither of the data definitions listed on the website allowed me to properly parse those 2 GB yearly files into anything usable. I then decided that I needed to just scrape Fayette County as a proof of concept, however even that had to be done in six months intervals which go back over approximately the last six years.
Let's get into the data!
First I would like to mention that all points on the dashboards listed below are clickable, so if you click a roadway name it will reshape all of the shown data to reflect that roadway name until you click off of it clearing that selection. The same goes for things like day of the week of collision or hour of collision, any of those will reshape all the other existing data on the charts below this goes for all four of the dashboards I have posted below.
This first dashboard highlights the locations of collisions highlighted by particular roadways with day of the week and hour of day frequency being shown at the bottom. The map to the right of the roadway names shows coloration by number of injured in particular accidents the concentration of redness at different locations can be a good indicator of where are the most injurious areas of a particular roadway. Take a look around at the data and remember you can use the controls in the upper left of the map to zoom in on a particular area of interest after you have set your filters were selected your items on the other charts.
This second dashboard is just a comparison of the percentage change of pedestrian and bicycle collisions by month and year. Taken as a whole pedestrian collisions have risen slightly over the last six years while bicycle collisions have fallen slightly . The thing to remember is the percentage differences in these changes are less than 1% so not terribly significant. If we limit the collisions to the last three years we see those trends are reversed. In the last three years , pedestrian collisions have gone down 0.4% while bicycle collisions have gone up 0.2% . I didn't have any particularly significant dates to slide the slider to in order to examine a particular change in Lexington policy or anything. But I left the date slider on the right in case anyone wanted to check something out.
This third dashboard is a slightly more simplified version of the first except that I wanted to look at injury rates particularly. You'll notice the red coloration is the percentage of people injured on that given day or time. Again, the date sliders are on the right as well as selections for the day and hour of collisions though those can be selected by clicking directly on the dots as well . Things like bicycle and pedestrian collisions as well as, in this visualization, fatalities are included.
This last dashboard is just one purely to look at every aspect of timing of cycling collations including by year, month, day of week, and hour with both injury totals and total number of collisions also listed in the chart.
I will be linking the second or third or possibly even fourth part of this viz down below as I complete them but they will all hopefully be part of my one year of viz challenge that I've made for myself. As always, if you have questions or concerns you can leave them in the comments below this blog or hit me up on Twitter at @wjking0.
I posted the original version of this back several years ago as one of my very first geo-located dataviz that I'd created. With the new changes in Tableau Public I have finally found a way to get the live-updated data from the Lexington Health Department. If you'd like to see the raw Google Sheet that I'm pulling this data from I'll make it available here.
I didn't do too much as far as changing this data from it's original form except making the data a live-updating format and putting some additional filters and analysis on top of what I'd done previously.
First off I'd like to announce that I've developed what I think is a good mobile version which you can pull up on your phone if you'd like to bookmark to be able to quickly/easily check food scores/violations for a place. Click on the image below to be linked out directly to the dash!
If you'd like to see the full dash and analysis list click below to open up the rest of the blog post!
Since the 2014-2015 University of Kentucky Salary Viz was such a hit I figured I would both redo and improve some of the fancy stuff I did previously. First off let me say that I work at the University and have for quite some time. I've loved my work and the people I work for, I also think that everyone should watch this video (embedded below) first before wanting to be super-secretive about what they make:
After the last time I presented this data several emails went around UK regarding if I had done anything illegal in creating these viz's. Soon afterwards I actually met with UK Human Resources (who were extremely nice by the way) to talk about ways to get this info in a more "live" format for them to use. To be able to do quick assessments on medians and averages for different position levels or departments was apparently something they'd been striving to do for about 2 years. Even going so far as to hire a person specifically for the development of that kind of system. I ended up meeting with them and discussing the idea of developing a live version using our UK Tableau server but never really heard much back after the initial meeting. Anyway... the upshot was, I didn't get in trouble as that one (like this one) was done on my own equipment, on my own time, using publicly available data.
NOW, on with the viz!
The three graphs you'll recognize from last year but I also squashed several bugs. The first is the bubble graph showing all salaries and years worked. Years worked is represented by the size of the circle and salaries is the richness of the blue coloring.
Next I wanted to establish medians with quartiles for both years worked and salary. At about 17 years now I'm in the upper threshold of the quartile for years worked and hovering just over the median salary at UK.
Third is the big "full list" of salaries that you can scroll through. It's sorted by position (aka "job title") and then Department, Full-Time or Part-Time, then "Rank" (which only applies to faculty so there is a rank of 'non-faculty' for everyone else), Salary, and the number of Years Worked. I sorted it by position because if you hover the mouse over the Position the info on the right (medians and averages for years worked and salaries) will change to reflect the job title you're exploring.
Now comes the big one... the one that I know a lot of people have waited over a year for. What can you calculate when you have TWO years of salary data? Raise percentages. The BIG THINGS to keep in mind here is that when calculating these I made sure the people met the following criteria:
In the same Department as 2014-2015 data
In the same Position as 2014-2015 data
In the same Full-Time/Part-Time status as 2014-2015 data
In the same Exempt/Non-Exempt status as 2014-2015 data
The other thing I thought about was if there was any discrepancy to raise % by the number of years worked. IE is there a good-ole-boy system at play that rewards people who stick around longer? The short answer to that is not particularly, pay rates DO go up over years but that's to be expected, raise percentages don't tend to increase that much as you go up the scale (and remember the "scale" we're looking at is over 40+ years in length of service). The one noticeable trend I did see was that, over time, raises at the Medical Center side of campus tended to add up to larger amounts. You'll see the orange line at the top of this dashboard represents a MOVING AVERAGE of raise percentages. This is generally pretty low because there are LOT of people, particularly when you include part-timers, that receive 0% raises.
You'll notice the list of raise percentages is very similar to the median/averages for salary/years worked from the other dashboard but the primary sorting field is now department so you can click on a particular department and see how raises have been handled by the number of years worked. Obviously, when you have such few numbers you'll notice that the moving averages tend to shift pretty severely... still, if you select a department with hundreds of people you can get a pretty good idea on what the financial upward mobility looks like there.
This last little one is just to give you median's by "Rank" (faculty/non-faculty) over time. To keep the graph at least someone accurate and smooth it out a little I limited it to at least 5 people with that particular number of years worked. You can bump the slider up to 10 if you'd like a little more accurate picture.
Ultimately though the university spends $1,137,609,613.91 on Salaries according to their released data for the 2015-2016 fiscal year. While that may seem like a METRIC TON of money (and it is), think that the University of Kentucky is also one of the single largest employers in the state of Kentucky. It's the flagship university of the state of Kentucky. And sadly while none of us working here will ever be like the guy picture below, it's nice to know where state money is going and to know (now that we can calculate raise percentages and have historical data) that nothing crazy is going on.
Lastly I'd like to make this data as available as possible so you can obtain all the data used in this here: https://goo.gl/S8fVTc
As always you can hit me up in the comments below or on twitter @wjking0
So it's been a while since I posted. Sorry about the delay I've just been overwhelmed with WHAT to post. Right now I've got a lot of irons in the fire but the big thing is I'm doing a Tableau presentation on Friday which I've essentially had more than 2 months to work on and I'm only presenting for an hour! I've got WAAAAAY too much stuff to fit into an hour presentation! I'm going to show off:
My work in Import.io using Bulk Data Extraction from set URLs
Data Sets of Interest
UK Salary Data
Lexington City Salary Data
Twitter Data
Instagram Data
So it'll be a busy day just showing where to get all this stuff and what you can create with it! Hit me up on twitter @wjking0 if you have any comments/suggestions/etc.
I'll fully admit that this Viz is a rush-job. I wanted to go ahead and throw it together quickly and I'll update it later as I crunch more data into the map. The last Gallery Hop of the season is tonight in Lex so here's a little map to see what's going on (that I may even get a chance to update once or twice before tonight). I'd also like to include non-official locations so if you know of any please hit me up and let me know about them!
While I'm not making #DataViz out of #WeirdData I work as a Sys Admin for the University of Kentucky. One of the most neat and data-rich departments I get to support is our student-run radio station WRFL 88.1 FM. There are over 300,000 MP3 files stored on their server from their over 30,000 CDs and well over 30,000 vinyl records. At this point a good chunk of the CDs have been ripped and provides us with a rich dataset. It allows us to ask all sorts of neat questions of a unique set of data:
Want to know the average length of a Rock song? 3 minutes, 23 seconds
How many songs on the server have the word "Kentucky" in the title? 57
How many songs does Johnny Cash have on the server? 916
All that being said, I know that this data (because radio station employees can upload their own data to it) is not the most "clean" set of data. Do I believe there was a song from 1675 on the server? Probably not. Eventually also I'll clean up the "Genre" category a lot more over time. I expect I'll be updating this particular dataset somewhere in the 4-6 month range.
ALSO! I was going to throw in a logo and decided instead to throw a live-stream player built into the DataViz which refreshes when the track playing changes allowing people to search out that particular artist/album to see what else is there.
Let me know what you think and suggestions for how to improve the viz at @wjking0.
I got this data from the Lexington, KY Health Department and while I hoped to have this constantly update-able I don't know that is going to be the case. I had to do quite a bit of data-teasing before I could actually do some work (particularly with location data). Additionally I had to create a file that contained all the Health Code violations and definitions so I knew which were "Critical" violations and what each code means. So this may be a one-off data viz.
I wanted to look originally if zip-code (and thusly socio-economic status) of an area had anything to do with food quality but I quickly realized another trend. That "Marts" and grocery stores tended to fall towards the end of the spectrum. Also interestingly every "school" is listed as well... use the search function to search for schools or other clusters of dining places to see if you notice any trends and you can shoot me a message @wjking0!
Search or click on the map and drag to select multiple locations and scroll down for more info about your selection!
