Jackson Two

Tag: alteryx

  • Dying Out, Bee Colony Loss in US | #MakeoverMonday Week 18

    Dying Out, Bee Colony Loss in US | #MakeoverMonday Week 18

    Week 18 of Makeover Monday tackles the issue of the declining bee population in the United States.  Data was provided by BeeInformed and the re-visualization is in conjunction with Viz for Social Good.  Unfamiliar with a few of the terms – check out their websites to learn what Makeover Monday and Viz for Social Good are all about.

    The original visualization is a filled map showing the annual percentage of bee colony loss for the United States.  Each state (and DC) are filled with a gradient color from blue (low loss) to orange (high loss).  The accompanying data set for the makeover included historical data back to 2010/11.

    Original visualization | Bee Informed

    Looking at the data my goal was to capitalize on some of the same concepts presented in the original visualization, but add more analytical value by including the dimension of time.  The key component I was aiming to understand was that there’s annual colony loss, but how “bad” is the loss.  The critical “compared to what” question.

    My Requirements
    • Keep the map theme – good way to demonstrate data
    • Add in time dimension
    • Keep color as an indicator of performance (good/bad indicator) – clarify how color was used
    • Provide more context for audience
    • Switch to tile map for skill building
    • Key question: where are bees struggling to survive
    • Secondary question: which states (if any) have improved

    Building out the tile map and beginning to add the time series was pretty simple.  I downloaded the hexmap template provided by Matt Chambers.  I did a bit of tweaking to the file to change where Washington D.C. was located.  Original file has it off to the side, I decided to place it in-line with the continental US to clean up the final look.

    Well documented through the Tableau Community – the next step was to take the two data sources (bees + map) and blend them together.  Part of that process includes setting up the relationship between the two data sources and then adding them both to a single view:

    setting up the relationship between data sources
    visual cues – MM18 extract is primary data source, hexmap secondary

    To change to a line chart and start down the path of showing a metric (in our case annual bee colony loss) over time – a few minor tweaks:

    • Column/Row become discrete (why: so we can have continuous axes inside of our rows & columns)
    • Add on continuous fields for time & metric

    This to me was a big improvement over the original visualization (because of the addition of time).  But it still needs a bit of work to clearly explain where good and bad are.  This brought me back to a concept I worked on during Week 17 – using the background of a chart as an indicator of performance.

    forest land consumption

    In week 17 I looked at the annual consumption of carbon, forest land, and crop land by the top 10 world economies compared to the global footprint.  Background color indicates whether the country’s footprint is above/below the current global metric.  I particularly appreciate this view because you get the benefit of the aggregate and immediate feedback with the nice detail of trend.

    This led me down the path of ranking each of the states (plus DC) to determine which state had experienced the most colony loss between the years of the data (2010/11 and 2016/17).  You’d get a sense of where the biggest issues were and where hope is sprouting.

    To accomplish this I ended up using Alteryx to create a rank.  The big driver behind creating a rank pre-visualization was to replicate the same rank number across the years.  The background color for the final visualization is made by creating constant value bar charts for each year.  So having a constant number for each state based off of a calculation from 2010 vs. 2016 would be much easier to develop with.

    notice the bar chart marks card; Record ID is the rank

     

    Here’s my final Alteryx workflow.  Essentially I took the primary data set, split it up into 2010 and 2016, joined it back, calculated the difference between them, corrected for a few missing data points, sorted them from greatest decline in bee colony loss to smallest, applied a rank, joined back all the data, and then exported it as a .hyper file.

    definitely a quick & dirty workflow

    This workflow developed in less than 10 minutes eliminated the need for me to do at least one table calculation and brought me closer to my overall vision quickly and painlessly.

    Final touches were to be a little descriptive to eliminate the need for a color legend and to provide a first-time reader areas to focus on.  And picking the right color palette and title.  Color always leads my design – so I settled on the gold early on, but it took a few iterations to evoke the feeling of “dying out” from the color range.

    tones of brown to keep theme of loss, gold indicates more hope

    And here’s the final visualization again, with link to interactive version in Tableau Public.

    click to interact on Tableau Public

  • Alteryx Inspire – Day 1

    When I went to the Tableau Conference last year, I felt it was important to spend some time documenting my experience.  Anytime I go to a conference related to my professional aspirations I’m always taken by the wealth of knowledge that’s uncovered.

    The Alteryx Inspire conference is a pared down conference with about 2,000 attendees.  It is comfortably housed in the Aria hotel across 2 spacious and open floors.  There are escalators that split between level 3 and level 1 – there’s nice flow to it and plenty of natural light.  Events take place over three days: Monday, Tuesday, and Wednesday.  Monday is mostly a product training day and the bulk of sessions are the remainder of the week.  Opening keynote is Tuesday.

    This year – being my first – I was extremely fortunate to be able to attend and to do the product training track.  This gives me a firsthand opportunity to see how the product company sells and trains on its tool.  Facilitators are typically great at selling the ‘why’ and ‘how’ behind something.

    Today I sat for a full day going through the introduction to Alteryx Designer.  Not because it was my first time using the tool, but because I believe there’s something very powerful about origin stories.  There’s something you learn in the first 30 minutes that someone who doesn’t have the ‘formal training’ may never pick up.  That happened for me today and it was great to see everything in action.

    As an advocate for data-informed decision making the tool is indispensable.  Just by listening to the 100+ in my classroom, it’s scary to witness firsthand the youth that exists with businesses accessing data.  Yes, there have been really great strides, but so many people are just at the beginning.  I chuckle when I hear the typical ‘Excel’ analogies, but the overwhelming majority are nodding with how much they relate to the joke.

    I’ve always seen Alteryx as a natural companion for a data analyst.  For anyone out there trying to manage data it offers up a solution.  If only for the single act of being able to see a visual output of the thought process and work that went in to producing a data model.  A data model or report that can be shared, saved, printed (please don’t print), and most importantly: be communicated.  For someone doing data prep, blending, gathering – this is how you explain to your boss what you do.  This is the demonstration of what it takes to be the data wrangler.  This is how you share your critical thinking skills.

    I’ve just scratched the surface and have 2 more full days of Alteryx.  One that has already been peppered with amazing collaboration opportunities and sharing of enthusiasm.  The vibe is chill, the people are great, and the mission is achievable.

    Tomorrow is another day and an opportunity to take the building blocks and dream of skyscrapers.

  • Makeover Monday 2017 – Week 4 New Zealand Tourism

    Makeover Monday 2017 – Week 4 New Zealand Tourism

    This week’s Makeover was addressing Domestic and International tourism trend in New Zealand.  No commentary provided with the data set, the original was just 2 charts left to the user to interpret.  See Eva’s tweet for the originals:

    Going back to basics this week with what I like and dislike about it:

    • Titles are clear, bar chart isn’t too busy (like)
    • Not too many grid lines (like)
    • It’s easy to see the shape of the data and seasonality (like)
    • The scales are different between International & Domestic (dislike)
    • 3 years for easy comparison (like)
    • Eva chose this to promote her home country (like)

    I think this was a good data set for week 4.  No data story to rewrite, special attention was made by Eva to mitigate data misinterpretation, and she added on a bonus of geospatial data for New Zealand.

    My process really began with the geospatial part.  I haven’t yet had a chance to work with geospatial particulars developed and appended to data sets.  My experience in this has been limited to using Tableau’s functionality to manually add in latitude and longitude for unclear/missing/invalid data points.

    So as I got started, I had no idea how to use the data.  There were a few fields that certainly pointed me in the right direction.  The first was “Point Order.”  Immediately I figured that needed to be used on “path” to determine where each data point fell.  That got me to this really cute outlined version of NZ (which looks like an upside down boot):

    So I knew something additional needed to be done to get to a filled map.  That’s when I discovered the “PolygonNumber” field.  Throwing that onto detail, changing my marks to polygon and voila – New Zealand.  Here’s a Google image result for a comparison:

    Eva did a great job trying to explain how NZ is broken up in terms of regions/territories/areas, but I have to admit I got a little lost.  I think what’s clear from the two pictures is I took the most granular approach to dissecting the country.

    I’m super thrilled that I got this hands on opportunity to implement.  Geospatial is one of those areas of analytics that everyone wants to go and by including this – I feel much more equipped for challenges in the future.

    Next up was the top viz: I’ve been wanting to try out a barbell/DNA chart for a long time.  I’ve made them in the past, but nothing that’s landed in a final viz.  I felt like there was an opportunity to try this out with the data set based on the original charts.  I quickly threw that together (using Andy Kriebel’s video tutorial) and really enjoyed the pattern that emerged.

    The shape of the data really is what kicked off the path that the final viz ended up taking on.  I liked the stratification of domestic vs. international and wanted to carry that throughout.  This is also where I chose a colors.

    The bottom left chart started out it’s life as a slope chart.  I originally did the first data point vs. the last data point (January 2008 vs. April 2016) for both of the types of tourism.  It came out to be VERY misleading – international had plummeted.  When I switched over to an annual aggregation, the story was much different.

    International NZ tourism is failing!
    Things look less scary and International is improving!

    Good lesson in making sure to holistically look at the data.  Not to go super macro and get it wrong.  Find the right level of aggregation that keeps the message intact.

    The last viz was really taking the geospatial component and adding in the tourism part.  I am on a small multiples kick and loved the novelty of having NZ on there more than once.  Knowing that I could repeat the colors again by doing a dual axis map got me sold.

    All that was left was to add in interactivity.  Interactivity that originally was based on the barbell and line chart for the maps, but wasn’t quite clear.  I HATE filter drop downs for something that is going to be a static presentation (Twitter picture), so I wanted to come up with a way to give the user a filter option for the maps (because the shading does change over time), but have it be less tied to the static companion vizzes.  This is where I decided to make a nice filter sheet of the years and drop a nice diverging color gradient to add a little more beauty.  I’m really pleased with how that turned out.

    My last little cute moment is the data sourcing.  The URLs are gigantic and cluttered the viz.  So instead I made a basic sheet with URL actions to quickly get to both data sets.

    A fun week and one that I topped off by spelling Tourism wrong in the initial Tweet (haha).  Have to keep things fun and not super serious.

    Full dashboard here.