This week's data set was pretty straightforward. During #WatchMeViz I iterated through some maps, including joining to spatial files for London, and also lots of time series charts. Below are links to the resources, the video, and the final viz.

Back in March 2020 when COVID lockdown started in the UK, the streets were amazingly empty, the air got fresher and I saw more and more people outside. You could hear birds chirping on streets you never would have before. And we could ride our bikes right down the middle of the road since there were no cars. The lack of cars was glorious! (COVID isn't of course).

For 2021, #MakeoverMonday gets started with a simple graphic that compares pedestrian and bicycle counter stats for 2019 and 2020 at 31 counters across America. The data is collected by the Rails to Trails Conservancy, and you can learn more about the data here. 

ORIGINAL VISUALIZATION

WHAT WORKS WELL?

Sunday was the 43rd Iron World Championship. It was the first time I spent a lot of time watching it and I found it pretty cool. I figured I should pay more attention to how it all works given I'm doing Challenge Roth in July. I'm a bit daunted by the prospect of competing in an Ironman distance, but I know I can do it with proper training.

The viz to makeover is a simple table from Wikipedia:


What works well?

• The years are listed in order from most recent to oldest.
• Table can be sorted
• Including separate columns for each medal
• Including links to each athlete

Spring is trying to peek out here in London. I saw so many people cycling in Richmond Park yesterday that I thought it was a good time to look at the state of cycling in the UK, focusing on the economic value.

Here's the original chart:

What works well?

What could be improved?

My goals

A few weeks ago, I voluntold Luke Stoughton (Head of Data School Recruitment and good friend of mine) to join me for a 52 mile cycling sportive hosted by Evans, a UK-based chain of cycling shops. Now, there's one thing to keep in mind...Luke had only ridden up to about 20 miles on a single ride. He WAS NOT happy with me, but he went anyway.

Coach Carl joined us as well, so I met him at a train station on the way and we rode the rest of the way to the start at Osterley House, an incredibly beautiful National Trust park and house. We met Luke there and headed off on our trek, stopping at two feed stops (I ate about eight pieces of cake at the second), peddling up three steep hills (they weren't that bad, but Luke hated it and threatened me), before looping back to the start and hitting the pub for a couple well deserved refreshments.

Along the way, I was thinking about how I could visualize the data. Strava does a decent job (see my ride here) and I thought I'd use that as a basis for my viz. My goals:

1. Use a Mapbox map background as similar as I could to the Strava map (which is proprietary).
2. Use the Strava colors (I found the hex codes in their brand guidelines and added them to my preferences file).
3. Prep the data in Alteryx. There is a web data connector, but I like being able to create my own row level calculations and extract only the data I'm interested in. Plus it gives me an excuse to practice Alteryx more.
4. I like how the elevation timeline, but for me it's lacking context by not including speed. I decided to keep its wide and short style while creating a dual-axis chart to overlay speed on top of the elevation.
5. I included key moments as viz in tooltips. Hover over the gold ribbon (fastest speed) to see a picture of Luke and Carl speeding down the hill. That was fun! You can also see a selfie of us at the end.
6. Include interactivity like Strava. Basically link the map to the chart and vice versa.
7. Include speed and elevation on each point.
8. Everyone loves a good BAN! FYI, Strava calculations elevation climb differently.

While I was running to work today along the Thames toe path, I had an idea! I wanted to create a hex map or grid map or some type of grouping of stations to represent the stations in clusters rather than showing every station on a map.

I created a viz last year about American happiness, so decided to use a similar theme. What I did was group stations together based on their location. It takes two calculations:


You then makes the continuous dimensions and place them on the appropriate shelves (Round Lon on Columns and Round Lat on Rows).

I then created a calculation that ranks each "zone" by the number cycle hires and then places them into percentiles. I then take the percentiles and break them up into happiness quartiles.


I set the Location Happiness to discrete, placed it on the Shapes shelf and applied my emoticon shapes. I then duplicated the Round Lat field on the Rows shelf and moved the Location Happiness field to color, changed the shape to circle, moved the marks to the back and assigned colors.

Simple! I like how this turned out.

The weather has been wonderful here in London lately, with really warm temperatures expected later this week. I take advantage of the weather by cycling to work whenever I can. It not only helps the environment, it's good for my health, it's good for my mind, and cycling commuting saves me time and money.

I asked Eva to use data from Transport for London's open API about their cycle hire scheme. Data is available back to 2012 and I offered to prep it for her and upload it to Exasol...all 50M+ bike hires worth. I love the weeks when we get to use Exasol because I can ask and answer questions on massive data sets without any performance constraints.

The visualization to makeover this week comes from Sophie Sparks:

What works well?

What could be improved?

What I did

Having recently purchased a nice road bike for commuting to work and looking into registering it with the police, I ran across the great Stolen Bike UK website where people can report their stolen bike, see what's been reported and get more information about what's going on overall.

For this week's makeover, we're looking at the page where you can search a specific postcode. Given I ride to work, I naturally entered our work postcode and got this visualisation.

What works well?

What could be improved?

Questions I want to answer

Tough data set for me this week as I know nothing about cycling. In addition, I raced this clock this week and completed Makeover Monday in an hour with the Data School. We then all presented our work back to Eva.

If you're particularly bored, here's a recording of my screen for the whole hour.



Ok, still awake? Hopefully you enjoyed my Spotify playlist at least. This week's viz that we're reviewing is knomea.

What works well?

What could be improved?