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    Home»Artificial Intelligence»Analyze Tornado Data with Python and GeoPandas | by Lee Vaughan | Jan, 2025
    Artificial Intelligence

    Analyze Tornado Data with Python and GeoPandas | by Lee Vaughan | Jan, 2025

    Team_AIBS NewsBy Team_AIBS NewsJanuary 28, 2025No Comments1 Min Read
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    QUICK SUCCESS DATA SCIENCE

    Insights from NOAA’s public area database

    Towards Data Science

    A map of the contiguous United States showing tornado touchdown locations as red dots. State and county boundaries are outlined in blue and light gray, providing geographic context. The red dots are scattered unevenly, with higher concentrations in areas known as Tornado Alley, including parts of Texas, Oklahoma, Kansas, and Nebraska.
    Reported tornadoes with an assigned EF magnitude from 1950–2023 (by the creator)

    For those who’re excited by finding out tornadoes, the Nationwide Oceanic and Atmospheric Administration (NOAA) has a beautiful public-domain database to make your day. Extending again to 1950, it covers twister beginning and ending areas, magnitudes, accidents, fatalities, monetary prices, and extra.

    The info is accessible by way of NOAA’s Storm Prediction Center. Along with the uncooked information, this website provides many charts and maps that slice and cube the information in a number of methods. One in all my favourite maps is the 20-year common of extreme climate watches. It makes it appear like Oklahoma’s borders are largely based mostly on dangerous climate!

    A colorful map of the contiguous United States shows counties colored by annual severe weather watches. The largest number of watches is colored yellow; the minimum number is white to light blue. State and county boundaries are outlined in black and light gray, providing geographic context. The yellow counties are concentrated in Oklahoma and seem to follow its state boundary.
    Oklahoma (circled) experiences greater than its share of dangerous climate (NOAA)

    These prefab maps are helpful, however they will’t tackle each state of affairs. There will probably be occasions when you could make customized maps, both to reply a particular query or to include outdoors information.

    On this Fast Success Information Science mission, we’ll use Python, pygris, GeoPandas, Plotly Specific, and a part of NOAA’s intensive database to map twister occurrences, graph their…



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