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Showing posts with label transport for london. Show all posts

December 17, 2018

Makeover Monday: London Bus Safety Performance

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I was hit by the 237 bus in Brentford on September 22. The driver pinned me against the curb and I nearly went under the bus. The driver was an asshole about the whole thing and now the bus companies has stopped communicating with me.

So, to get back at them, I gathered all of the bus injury data from Transport for London and gave it to all of you, the Makeover Monday Community, to help make their scorecards better. What does the scorecard look like now?


What works well?

  • The line charts are easy to understand.
  • All charts have good titles.
  • The donut chart is clear and simple.
  • The colors work well together (except the pie).

What could be improved?

  • The pie chart could be made into a bar chart.
  • The charts could use more context.
  • The data is horribly delayed (it's December 17th as I write this and the data only goes through June).

What I did

  • Give the user an opportunity to explore the data; every chart has action filters
  • Use TfL colors
  • Provide context on some of the charts, like injuries per month

With that, here's my Makeover Monday week 51.

June 26, 2018

Makeover Monday: Where are London's happiest bike pickup zones?

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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.

June 25, 2018

Makeover Monday: When are bicycles hired in London?

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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?

  • The small multiple layout works great or showing cyclical patterns (see what I did there?).
  • The diverging color scale helps accentuate the peak periods.
  • The shading under the lines makes the viz feel more full and complete.
  • Shading the weekends helps separate them from the rest of the weekdays.
  • Putting the word When in red in the title to match the peak period.

What could be improved?

  • I would remove the section at the top that says "Boris Bikes" and the image.
  • Include some sort of insight as a subtitle.
  • There's no indication of what the y-axis means. I assume it's the number of bikes hired, but it could just as easily be something else.

What I did

  • First, I rebuilt Sophie's viz because I like it.
  • I wanted to focus on the weekday and hourly patterns in the data.
  • Use the TFL blue as a single color for the viz.
  • Provide some interactivity so that people could see when the peaks and troughs in the data are for a specific year or month.

Click on the image for the interactive version.

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