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Dashboard Week Day 2: NASA FIRMS

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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Our second day of Dashboard Week required us to connect to the Global FIRMS Database to make a dashboard to show a story we found.

My first thought was to look at the relationship between the size, brightness, fire radiative power and type of fire across different countries and years. I also wanted to bring in El Niño-Southern Oscillation data to see if El Niño and La Niña events affected the nature of fires.

 

In Alteryx

Firstly, I downloaded and extracted the zipped folders of each year’s data split by country. I then used a batch input macro to import all the data from the directory and found that there were 98 million records. I decided to focus on the top 5 countries most affected by fires (Australia, Brazil, Canada, Russian Federation & United States) so I filtered this data (which was still 25 million records) and used some formulas to clean and prepare the data. I also joined the data to a lookup table for each country’s seasons to include this in the dashboard.

 

Additionally, I obtained El Niño-Southern Oscillation data from Golden Gate Weather. This data required a bit of cleaning and manipulation, and I finally categorised the different ONI measurements into El Niño, La Niña and Normal, and their strengths according to the official NOAA standard.

 

In Tableau

I decided to use a scatterplot to investigate the relationship between Brightness and FRP for different ENSO types & strengths. Next, I wanted to see if the different types of fires were more likely to be caused with different ENSO types & strengths so I created a diverging Gantt chart to easily show any shift. Then, I thought it is important to see the average number of events per month in each of the different ENSO types, which then made me consider to toggle between different countries to compare. Finally, I realised that it would be helpful to have a line chart showing the way the ONI has varied from 2001-2022 to better visualise the ENSO categories. I then organised these charts and added some important information for a new user to understand what is on the dashboard and how to use it and I was done. I hope you enjoy the result!

 

 

Dashboard Week Day 2 – NASA FIRMS

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