@30daychartchall.bsky.social #Day30 #30DayChartChallenge Final chart/prompt. GHDx offers a human-factors lens on Brazil-Europe long-haul flights and "human clocks". The asymmetry is an eye-catcher: Brazil -> Europe: 13% of arrivals inside the Window of Circadian Low (WOCL). Europe -> Brazil: 30%.

For #Day27 #30DayChartChallenge `animation` prompt, we used a **split-screen corridor clock** . We walk over the busiest airport-pair corridors in our study; columns show `UTC` hours, the lower step line shows sum of corridor traffic. Take-away: Brazilian and European networks "tick differently".

For today’s `Space` prompt #Day25 #30DayChartChallenge, we used taxi-out as a ground-capacity lens. Thus, `Space` = occupied runway/apron system during departure taxi ops. `Uncertainty` = extra taxi-out minutes above the reference expectation. Different from our normal practice ... interesting!

For #Day26 #30DayChartChallenge we worked out the trend of additional taxi-out times: * most Brazilian airports moved left = reduced add. time * most European airport moved right = higher add. time In other words, average taxi-out trended in opposite directions across the two study regions.

For #Day24 #30DayChartChallenge we pulled together a small dataset on the delivery and operation of China's C909 and C919 aircraft. The main input was from COMAC and referenced in the South China Morning Post. This gives a good view on the growth rate for the new aircraft.

#Day20 #30DayChartChallenge: Global change. Taxi-out time tells a quiet operational story: Europe drifts upward from 2023 to 2025, while Brazil remains broadly steady. We tried to combine "result" panels with a line chart and struggled to make {ggtext} and {patchwork} work nicely together.

#Day19 of #30DayChartChallenge: Evolution. The Recovery Arc: daily air traffic in Brazil and Europe, indexed to each region's 2019 median. Both regions collapsed in 2020, but the recovery paths differ: Brazil rises above baseline earlier; Europe returns with stronger seasonal waves.

For the "trade" prompt #Day14 #30DayChartChallenge , we looked into whether any delay is absorbed between successive flights. The European system is characterised by systemic delays that ripple also into the sequence of flights ... ultimately amplifying (transferring) delay to the next departures.

#Day13 of the #30DayChartChallenge: Ecosystem We framed airports as local ecosystems: richness = destinations served, exposure = international share, health = average delay. The chart suggests that larger, more outward-facing airport "ecosystems" tend to carry more delay.

#Day12 of the #30DayChartChallenge: Flowing Data. This chart looks at air transport services between Europe and Brazil in 2025 as a flow chart. Our study covers 74% of services airports at both ends. Main connecting airports emerge nicely. This one needed a lot of iteration on labels.

#30DayChartChallenge #Day11 We dived one more time into ridgelines and interpreted (average daily) delay as a mass. A clear pattern emerges when comparing Brazil and Europe. The latter facing systemic delay for the past years.

#Day10 #30DayChartChallenge It took us a while to come up with a "poppy" idea ... but rumour has it that the Millennium Falcon was sighted operating flights between Brazil and Europe in 2025 ;). It is a classical ggplot chart showing the shares of the main wide-body workhorses.

To stay in our aviation thread, we interpreted "wealth" as the inequality of days adding delay (and possibly unhappiness of passengers). We show this with a Lorenz curve of the Gini index. It appears delays are more system in Europe than in Brazil. #30DayChartChallenge #Day9

#30DayChartChallenge #Day8 Day 8 - circular. We could not get a chord diagram going, so we reverted to {ggdraw} and used a network visualisation in "circular" layout. It shows the flight connections between our study airports in Brazil and Europe and close links between a subset of airports.

BRA-EUR contribution for Day 6 #30DayChartChallenge #Day6, we used a curated dataset to show the press freedom score per region across the years 2022 through 2025.

#30DayChartChallenge #Day2 "pictogramming" average punctuality for air traffic movements at the 12 study airports in Brazil and Europe with {ggplot2},{fontawesome}, {ggtext}, and {showtext} doing the heavy lifting. Green covers the interval of -5/+5 minutes of the planned movement time.

Can somebody advice newbies where to find the data for the data days (i.e., the upcoming doctors without borders) . Thanks #30DayChartChallenge

Late starting team from Brazil and Europe! For the next iteration of an operational performance comparison report we would like to use the challenge to refresh and practice our {ggplot2} skills. The following shows how the perspective of shares changes for "air traffic". #30DayChartChallenge #Day1