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Dashboard Week – Day 3 ‘Web scraping Airplane Crash Database’ – Analyzing the Number of Crashes Since Covid-19

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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I’m 13 weeks in and this was the long-awaited ‘dashboard week’. On our third day, we were supplied with a data set from the Plane Crash Database. For today’s post, I will go over my approach and how I ended up with my current dashboard. I will focus on how I planned ahead and then how I ended up building my dashboard.

The Approach -The Plan

Webscraping/Regex is one my biggest weaknesses topics in data school program. I had a quick glimpse of the data and I knew instantly I needed to use a smaller dataset. The data ranged from 1920 to 2021, which is almost over a century of plane crash data. And then it will each year has two levels of access. i.e 2021 Year –> Flight based on Dates. I’ve always had an interest in looking at data that Covid has affected. Especially since there was a blanket ban on air travel. So it got me curious, why would there still be plane crashes even though there was a ban on air travel? And so this was my take on approach for day 3, I wanted to analyse the air crashes that occurred during or since Covid started.

Next Steps – My idea

My idea was to try to build an infographic dashboard. I had the idea in mind because I always wanted to produce a dashboard of that calibre. I began with the sketch and I knew that since these were flight routes, a flight path of some sort could be integrated. From then, I built and cleaned the data as needed.

Final Steps – The execution

The first thing I did was pull the data into alteryx. I always go for one successful data pull and then I will scale it up. I managed to get that done, but unfortunately, I was not getting the geo spatial data I was intending…
And so the 2nd part of the flow I integrated google geo code API and I was able to get Lats and Longs of all the Origin and Destination cities of the flights. From then I was able to build up Makeline and Distance in Tableau. The only problem I had was with two of the Sudan cities, they were linking them up to Cities in the US. To counter this I made calculations that made their Origin Cities and Destination cities the same.

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