Spoooooooooky "Ghost" Photos by City/State

The thing about superstitious beliefs is that it's hard, REALLY HARD, to get a set database to work from to try to sketch it out visually. That being said the closest thing I was able to find as far as any sort of database of spooky events with significant numbers I could use to visualize creepy activity is the Ghosts of America site.

I wanted to scrape their entire archive for every single city but I realized that I'm almost SURE their site is hand-written so I wasn't able to get data extractors to crawl it successfully. Instead I figured out the best laid our part of their site for scraping purposes would also be the part I would want to use. So I went ahead and scraped the "photos" section of the Ghosts of America.

The state results are as follows:

For a city-level look here:

As you may have been able to tell at this point there are lot of "questionable" ghosts photos out there... ;-) As usual if you have any questions or concerns hit me up on twitter @wjking0 and I'd be happy to talk them out! In the meantime just remember most ghosts are quite what they seem....

Kentucky WIC Usage 2000-2013

As a non-native Kentuckian I wasn't sure what WIC usage looked like in this state. My assumption was generally that WIC was something you'd see more of in large developed cities. It turns out I was wrong.

The data used came from the following:

• RAW WIC Data for Kentucky: http://datacenter.kidscount.org/rawdata.axd?ind=1299&loc=19
• Total Population Numbers came from the 2000 and 2010 census.

For the calculations I applied the numbers to total calculation and not to subgroups for women or children under 18 so usage percentages for those may be higher but I don't have the WIC info regarding numbers of mothers vs children utilizing services so I didn't want to further muddy the numbers.

Also for these calculations I applied the 2000 census amounts to the 2000 WIC numbers and then for the 2006-2013 WIC numbers I used the closer 2010 census numbers as populations estimates for most regions were fairly stable over that time period.

As you can see, the large urban areas of Lexington and Louisville (Fayette and Jefferson Counties respectively) have fairly low usages of WIC (<2%) while areas particularly in eastern KY you can see have fairly high/consistent usage. I haven't done cost analysis yet but once the USDA fixes their website and I can get some more in-depth numbers I should have some more data to play with.

As usual hit me up at @wjking0 if you have any questions or concerns or just want to talk about public data!

EDIT: I've added the second dashboard/story as there was a request to look at the comparison of WIC % to Median Household Income so I crunched that out real quick:

Can We Build An Ethical Car?

This post is a little bit removed from my normal dataviz stuff but given Tesla's announcement yesterday now is the time for this post to come out! Besides, it's finally a chance for me to get to use my Philosophy degree!

I've been thinking for the last several years what the driverless-car revolution would really be like. When the first couple of driverless cars completed DARPA's car course in 2005 and the future became much more real an article came out (which I've sadly lost the link to) that made some very salient points which I'll try to summarize now: 

• The majority of cars spend their time idle/parked.
• If the price of driverless cars is prohibitive, wouldn't it be easier to spread that cost out with some neighbors since the car is only occupied for a brief time by each person per week?
• The downside of that is most people need cars are certain times to get to work on time, home, to pick up the kids from school, etc.
• Instead of splitting the cost of 'your' car with the neighborhood, what if instead you subscribed to a car 'service' (much like you currently subscribe to Netflix vs owning all your movies now)?

The ultimate point is that, in the somewhat near future, I think we can all agree that a large portion (if not all) cars will become driverless. They will be controlled by certain AI and algorithms that will enhance their safety features and reduce car-caused fatalities by a VAST number. We can already see that the initial rollout of driverless cars from Google have been in accidents where other drivers are at fault.

Here's the thought experiment I want you to conduct:

There are  two autonomous cars are next to one another on a bridge. The unthinkable happens and a large item falls off the back of a semi truck landing directly in the path of one of the cars. Let's say both have at least 1 person in them and there is a 100% of ONE of the vehicles inhabitants not surviving the crash (I think we've all seen Final Destination...).

OK, so who gets to live? Software has to make that decision. Let's assume that both cars have the same AI/decision-making software in them (we'll get to a different idea in a sec). We can assume that in this point in automated driving cars would communicate with one another for enhanced safety such as warning about large obstructions, potholes, etc. What if one of the vehicles that was going to crash had a single person in it and the other vehicle had a family of four?

We would think at that point the cars would do a math to calculate that > lives = better! What if the single individual was someone working on ground breaking research into cancer treatments? Do we want cars to rank our lives? If you recall this type of software biasing for life/death was one of the crucial turning point (spoilers) for Will Smith's character in the movie iRobot.

In the beginning of the film Will Smith's character has a car accident and a robot AI jumps into a freezing lake as his car is sinking pulling him (and NOT his young son) to safety despite his protests that the robot should save the son instead. Will we get to choose? Will future cars ask for our preference for these types of ethical situations? Could I say, 'In the event that my car is spinning and going to collide with an object please make it on my side vs that of my daughter' ? Could we value high numbers of lives over our own or will the software choose for us?

Additionally... let's return to the idea of differing software. Would different manufacturers have different ethical applications running in their cars? If two cars were on a bridge from different auto-makers would they fight over whom gets to live?

Anyway... I know it's not my normal dataviz thing but I wanted to post this out here to get at least a small number of you all thinking about what driverless cars mean for robotic ethics. It's a HUUUUGE deal (in my opinion) and I figured I should open up a dialogue about it! Comments are always welcome on my twitter @wjking0 so shoot me your thoughts and let's have a discussion about car AI ethics!

Quick Viz for Bluegrass 10K 2015

I don't have a lot of commentary on this but I just threw it together real quick to see if there were any inconsistencies or specific gender differences in runners.

Here are the relevant links:

• Bluegrass 10K 2015 info
• Bluegrass 10K 2015 data raw
• Bluegrass 10K 2015 data scrape by Maptastic

Here's the viz you can click around in and check out, filter by Division, Gender, etc.

Kentucky's Gay Marriage Denial by Zip Code (Updated 6-30-2015)

Gay Marriage Denials in KY (by Zip) and "Small-town-ness"

I was working on a little something else for another blog post but given the prevalence of the state of Kentucky in the News I figured I should crunch these numbers real quick to see if what I'd been suspecting has been true.

While I'm still going to publish my ultimate "Churches vs Stoplights" viz blog post at some point I won't show all my cards for that one. Where I grew up in WV we used to joke that the measure of a "Small-town" was if it had twice as many churches as stoplights (as mine did). So I'm using that as my "small-town" base. Then I used the current list of counties denying marriage licenses to couples since the ruling from the SCOTUS and broke those down into their applied zip codes. The results are as follows!

If you'd like to see how your zip code fares do a search for it here:

So it turns out that most "Small Towns" (at least by my own made-up definition) are actually OK with the SCOTUS ruling! This wasn't the result I was expecting at ALL! I'm not saying that these places are accepting or even friendly to my LGBT brothers and sisters but I'm saying that the places listed are at least adhering to national law so please take all this with a grain of salt!

If you have any questions/comments/concerns shoot me a message @wjking0 on Twitter or comment here on the blog!

University of Kentucky Salary Data (2014)

So I like malleable things... like playdoh. As most of you know I was out with a broken leg based on one of my prior blogs. While I was out I worked with a LOT of public data sets. Finally I sat down and scraped UK's Salary data from several different public resources.** It was the 2014 data but I figured it would still let me do some fun things with the data.

So let's get right to it! The fields that were available were as follows:

• Department
• Description ("Job Code", Professional, Exec, Faculty, etc)
• Title (Job Title)
• Status (Full Time / Part Time)
• Salary
• Start Date (BIG NOTE, this is their start date at the University NOT their start date for that particular position!)
• Years Worked (which I derived from Start Date)

Without any further chatter let's look at what you came here for! This first dashboard is just something pretty I whipped up to be able to visualize EVERY employee at the University of Kentucky. Continue scrolling down for further explanation of each dashboard and how it works!

This second Dashboard is just to look at the salary vs years worked for employees.

Finally this is the big one! This third dashboard is a list of every Title by Department regarding Status and Description by Salary and Years Worked. You'll notice that off to the right-hand side of the viz you'll see Average and Median's for both Salary and Years Worked. These are dynamic and what you see currently is the Averages and Median's for EVERY employee at the University of Kentucky. What makes this Viz very powerful is the ability to click on Title (Job Title) to see the median/averages for that particular job. Interested to see where you fall in the scale of your particular position? You can know that now! See how your length of service at the University compares to your peers but please keep in mind that the years worked DOES NOT mean years worked in that particular job, just total at UK!

What do you all think!? Please hit me up on twitter @wjking0 or comment on the blog and if you like what you saw here I have TONS more public data coming from my two months of FMLA leave sitting around with a broken leg! Subscribe via your RSS readers!

**Also I'd just like to note that all this data was scraped publicly using freely available tools such as the AMAZING Import.io and some URL builders while I was at home on FMLA so none of UK's time/resources went into these viz's.

So. Much. Data. *heavy breathing*

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.