52 Vis Week #2 Wrap Up

I’ve been staring at this homeless data set for a few weeks now since I’m using it both here and in the data science class I’m teaching. It’s been one of the most mindful data sets I’ve worked with in a while. Even when reduced to pure numbers in named columns, the names really stick with you…_”Unsheltered Homeless People in Families”_…_”Unsheltered Chronically Homeless”_…_”Homeless Veterans”_…_”Unsheltered Homeless Unaccompanied Youth”_. These are real people, really hurting.

That’s one of the _superpowers_ “Data Science” gives you: the ability to shed light on the things that matter and to tell meaningful stories that people _need_ to hear. From my interactions with some of the folks who submitted entries, I know they, too, were impacted by the stories contained in this data set. Let’s see what they uncovered.

(All the code & un-shrunk visualizations are in the [52vis github repo](https://github.com/52vis/2016-14))

Camille compared a point-in-time view of one of the most vulnerable parts of the population—youth (under 25)—with the overall population:

homeless_rates

The youth homelessness situation in Nevada seems especially disconcerting and I wonder how much better/worse it might be if we factored in the 25 & under U.S. census information (I’m really a bit reticent to run those numbers for fear it’ll be even worse).

Craine Munton submitted our first D3 entry! I ended up tweaking some of the JS & CSS `href`s (to fix non-sync’d files) and you can see the full version [here](https://rud.is/52vis/2016-14/cmrunton/). I’m going to try to embed it below as well (I’ll leave it up even if it’s not fully sized well. Just hit the aforementioned URL for the full-browser version).

Craine focused on another vulnerable and sometimes forgotten segment: those that put their lives on the line for our freedom and the safety and security of threatened people groups around the globe.

Joshua Kunst is an incredibly talented individual who has made a number of stunning visualizations in R. He used htmlwidgets to tell [a captivating story](https://rud.is/52vis/2016-14/jbkunst/) that ends in (statistically inferred) _hope_.

Hit the [full page](https://rud.is/52vis/2016-14/jbkunst/) for the frame-busted visualization.

Jake Kaupp took inspiration from Alberto Cairo and created some truly novel visualizations. I’m putting the easiest to embed first:

Jake has [written a superb piece](https://jkaupp.github.io/) on his creation, included an [interactive Shiny app](https://jkaupp.shinyapps.io/52vis_Homeless/) and brought in extra data to try to come to grips with this data. Definitely take time to read his post (even if it means you never get back to this post).

His small-multiples view is below but you should click on it to see it in full-browser view.

Jonathan Carroll (another fellow rOpenSci’er) created a companion [blog post](http://jcarroll.com.au/2016/04/10/52vis-week-2-challenge/) for his animated choropleth entry:

HomelessPopulation_optim

I really like how it highlights the differences per year and a number of statistical/computational choices he made.

Julia Silge focused on youth as well asking two compelling questions (you can read her [exposition](http://rpubs.com/juliasilge/170499) as well):

unnamed-chunk-7-1

unnamed-chunk-9-1

(In seeing this second youth-focused vis and also having a clearer picture of the areas of greatest concern, I’m wondering if there’s a climate/weather-oriented reason for certain areas standing out when it comes to homeless youth issues.)

Philipp Ottolinger took a statistical look at youth and veterans:

homeless_plot

Make sure to dedicate some cycles to [check out his approach](https://github.com/ottlngr/2016-14/blob/ottlngr/ottlngr/homeless_ottlngr.Rmd).

@patternproject did not succumb to the temptation to draw a map just because “it’s U.S. State data” and chose, instead, to look across time and geography to tease out patterns using a heatmap.

Rplot01

I _really_ like this novel approach and am now rethinking my approach geo-temporal visualizations.

Xan Gregg looked at sheltered vs unsheltered homeless populations from a few different viewpoints (including animation).

homelesslines

homelessmaps

sheltered-homeless-by-state

(Again, beautiful work in JMP).

### We have a winner

The diversity and craftsmanship of these entries was amazing to see, as was the care and attention each submitter took when dealing with this truly tough subject. I was personally impacted by each one and I hope it raised awareness in the broader data science community.

I couldn’t choose between Joshua Kunst’s & Jake Kaupp’s entries so they’re tied for first place and they’ll both be getting a copy of [Data-Driven Security](http://dds.ec/amzn).

Joshua & Jake: hit up bob at rudis dot net to claim your prize!

A $50.00 donation has also been made to the [National Coalition for the Homeless](http://nationalhomeless.org/) dedicating it by name to each of the contest participants.

Cover image from Data-Driven Security
Amazon Author Page

3 Comments 52 Vis Week #2 Wrap Up

  1. Pingback: 52 Vis Week #2 Wrap Up – grahn.xyz

Leave a Reply to Julia Silge Cancel reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.