ericamarie9016 / fema_hazards

FEMA_Hazards

EDS220 Final Project

Exploring the Expected Annual Loss Due to Natural Hazards in the US

Student Authors: Alex Reed, Erica Dale, Michelle Lam, and Wen-Tien Wang

Objective

This notebook was composed for the "Working with Environmental Datasets" course of "Environmental Data Science" (EDS 220) at the University of California Santa Barbara in Fall 2022. It provides an introduction to using National Risk Index (NRI) data. This data is made available by the Federal Emergency Management Agency (FEMA) and is freely accessible. The purpose of this project is to assess the expected annual loss due to natural hazards affecting states across the United States. The notebook provides instruction and example code on how to pull in the NRI data directly from the source, create some basic visualizations, and shows how the data can be applied to specific use cases. Historic and current data acquisition is important for planners, emergency managers, and other decision makers. With improved understanding of natural hazard risk and the relative economic impact of them, communities can take action to reduce the risk specific to that community. With improved understanding of natural hazard risk, communities can take action to reduce the risk specific to that community. Data resources can be found here: https://hazards.fema.gov/nri/data-resources

Important Concepts

This notebook is intended for educational purposes. Student authors practiced the following skills and commands:

• exploring data resources
• dataset research
• data subsetting
• plotting
• data visualization

Setup

The Python code is wrapped in a Jupyter notebook for convenient explanations and code aside. The following packages were utilized during this analysis:

• geopandas
• requests
• zipfile
• io
• matplotlib
• matplotlib.colors
• seaborn
• pandas

How to run?

Below is a link to the Binder Environment for this project. It will allow you to run the contents of this notebook without generating a new environment locally.