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

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

    Team_AIBS NewsBy Team_AIBS NewsJanuary 18, 2025No Comments1 Min Read
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    Analyzing historic wildfire traits in Canada with public information

    Towards Data Science

    CL-415 In Motion (in Italy). Picture By Maarten Visser — Wikimedia commons

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

    Interactive maps that permit enter out of your viewers can be utilized for deeper evaluation and storytelling.

    Python Streamlit is the fitting device for the job. It may be used alongside the pandas for simple information body creation and manipulation.

    Let’s take a look at this out with a deep and detailed dataset on a really prescient situation — the seeming escalation of wildfires. There’s a terrific public wildfires dataset out there on the location managed by Pure Sources Canada.

    With this detailed dataset let’s take a modular strategy to our information evaluation and create:

    • A static map that reveals all forest fires in Canada for a time period (ie. a selected 12 months).
    • An interactive map that permits the person to pick a shorter time period (ie. a dropdown menu by 12 months) to view extra granular information.
    • A bar chart that reveals extra granularity —the variety of fires at a provincial degree.



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