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Explored Wildfire data in the United States from 1985-2019 using Python/Jupyter Notebook

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Setting The Data World Ablaze: The Data Stories Behind Wildfires

Background

When initially researching for this project, we wanted to find a topic that was relevant to the present day while also something we were genuinely interested in the results of the research and seeing if there was an increasing trend in wildfires. According to FEMA, a wildfire is an unplanned fire that burns naturally (ready.gov). It is important to note that residential fires are not included in this data - this only accounts for wildland fires.

With the recent fires highlighted in the media and the talks of increased wildfires due to climate change, we wanted to see if wildland fires are actually increasing as much as we think they are. Looking into these wildfires, we wanted to research based on historical, geographical, and financial trends to see if there are noticeable trends or effects. For the project we decided to use data from the National Interagency Fire Center (NFIC), which is a governmental organization that supports and they organize and allocate emergency resources to different federal, state, and local government agencies, such as the National Park Service and Departments of Agriculture and Interior.

Historical Trends

we graphed the number of fires and acres burned since 1985 and found the following information: The number of fires fluctuates over the years with a general trend of decreasing as per the below graph.

Screenshot (110)

Geographic Trends

The data set used observed 527 wildfires since 1985 that burned over 100,000 acres in one burn. it is hard to compare the burned acreage of a small state like Rhode Island to a large state like Alaska. To solve this, we took the burned acreage and divided that by the state’s landmass to find a percentage burned as per the below:

Screenshot (111)

Financial Trends

On the financial side, we looked at the suppression costs of extinguishing the fire.

Screenshot (112)

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Explored Wildfire data in the United States from 1985-2019 using Python/Jupyter Notebook

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