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Tableau Dashboard Challenge – Day 2

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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Introduction

On the second day of the week-long dashboard challenge, we were asked to explore a large database containing information on the number of fire locations around the world from the website called Fire Information Resource Management System (FIRMS). The website is https://firms.modaps.eosdis.nasa.gov/download/. To make things easier, it was suggested to focus on a country and specific periods of time to find interesting insights. I decided to focus my story on the country of Brazil, as I wanted to explore the seasonal trends along with fire locations that occured from 2017 to 2022.

 

Using Alteryx to prepare the data

The data was downloaded from the FIRMS website for the fire locations in Brazil from 2017 to 2022. I initially wanted to explore the full dataset which had 10 years of data and contained more than 3 million records which was a lot of data to go through and started to put a strain on my laptop’s processing speed, so I decided to start with 5 years of data instead. The data was very clean, and only the date field needed to be reformatted to the standard date time format. I created a new column that categorised the fire radiative power (FRP) score, which is a measure of the fire intensity.

Visualising the data using Tableau

For the tableau dashboard, a brief explanation on the who and why the fire location data is collected and the overall goal of the dashboard. The first insight was a line chart showing the seasonal trends in the number of fire locations recorded in Brazil. The number of fires increases during the dry season, a period that is typically hot, dry, and windy as compared to the wet season or the rainy season where higher rainfall makes it more difficult for fires to start. Next, the number of fire locations by area, FRP category, and the day/night cycle was analysed. Key insights are fires categorised as low FRP account for the highest number of fire locations at approximately 76% and fires from vegetation burning account for the highest number of fire types at ~98%.

 

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