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Home Machine Learning

Showcasing Soaring Wildfire Counts With Streamlit and Python: A Powerful Approach | by John Loewen, PhD | Jan, 2025

AiNEWS2025 by AiNEWS2025
2025-01-19
in Machine Learning
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Showcasing Soaring Wildfire Counts With Streamlit and Python: A Powerful Approach | by John Loewen, PhD | Jan, 2025
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Analyzing historical wildfire trends in Canada with public data

John Loewen, PhD

Towards Data Science

11 min read

·

12 hours ago

CL-415 In Action (in Italy). Image By Maarten Visser — Wikimedia commons

Python Streamlit is terrific for creating interactive maps from a GIS dataset.

Interactive maps that allow input from your audience can be used for deeper analysis and storytelling.

Python Streamlit is the right tool for the job. It can be used alongside the pandas for easy data frame creation and manipulation.

Let’s test this out with a deep and detailed dataset on a very prescient issue — the seeming escalation of wildfires. There is a terrific public wildfires dataset available on the site managed by Natural Resources Canada.

With this detailed dataset let’s take a modular approach to our data analysis and create:

  • A static map that shows all forest fires in Canada for a period of time (ie. a particular year).
  • An interactive map that allows the user to select a shorter period of time (ie. a dropdown menu by year) to view more granular data.
  • A bar chart that shows more granularity —the number of fires at a provincial level.

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#Showcasing #Soaring #Wildfire #Counts #Streamlit #Python #Powerful #Approach #John #Loewen #PhD #Jan

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