HW #2: Visualizing FEMA NRI Data

Learning Outcomes

• identify which types of visualizations are most appropriate for your data and your audience
• prepare (e.g. clean, explore, wrangle) data so that it’s appropriately formatted for building data visualizations
• build effective, responsible, accessible, and aesthetically-pleasing, visualizations using the R programming language, and specifically {ggplot2} + ggplot2 extension packages

Description

In class, we’ve been discussing strategies and considerations for choosing the right graphic form to represent your data and convey your intended message. Here, you’ll apply what we’re learning while exploring natural hazards data, courtesy of the FEMA National Risk Index (NRI).

I. Background reading

Read more about FEMA’s NRI before continuing on (unfold the following note, which is collapsed to save space):

About the data

About FEMA’s National Risk Index (NRI) for Natural Hazards

FEMA (Federal Emergency Management Agency) is a government agency with a mission of helping people before, during, and after disasters. In 2021, FEMA launched the National Risk Index (NRI), “a dataset and online tool to help illustrate the United States communities most at risk for 18 natural hazards”.

Risk is defined as the potential for negative impacts resulting from natural hazards. It’s calculated using the following equation (and illustrated in this graphic; read more about determining risk):

\[Risk\:Index = Expected\:Annual\:Loss \times \frac{Social\:Vulnerability}{Community\:Resilience}\]

NRI provides hazard type-specific scores, as well as a composite score, which adds together the risk from all 18 hazard types. A community’s risk score is represented by its percentile ranking among all other communities at the same level for Risk, Expected Annual Loss, Social Vulnerability and Community Resilience – for example, if a given county’s Risk Index percentile for a hazard type is 84.32 then its Risk Index value is greater than 84.32% of all US counties. Each community is also assigned a risk rating, which is a qualitative rating that describes the community in comparison to all other communities at the same level, ranging from “Very Low” to “Very High.”

You can learn more about the NRI at hazards.fema.gov/nri.

Screenshot of the The National Risk Index’s interactive mapping and data-based interface

Accessing NRI Data

Data at the county- and census tract-level are available for download in multiple formats (including Shapefiles & CSVs) from NRI’s Data Resources page.

II. Create your eds240-nri-acs-viz GitHub repo

Create a public GitHub repository named eds240-nri-acs-viz and add the following:

• an informative and polished README
• a HW2.qmd file with the necessary YAML
• a folder named data/
• a .gitignore file

We’ll use this same repository for both HWs #2 & #3.

For this assignment, you’ll only be working with FEMA NRI data. During HW #3, you’ll be asked to incorporate American Community Survey (ACS) data (hence the repo name, eds240-nri-acs-viz).

III. Download data & add it to your repo

Next, let’s download the necessary data, and add it to our eds240-nri-acs-viz repo:

• 1. Download and unzip the data. You’ll use the All Counties - County-level detail (Table) (2023 Release; accessed on the NRI Data Resources page). Unzip the file, then drop the whole NRI_Table_Counties/ folder into a data/ folder in your HW repository.

• 2. Add your data/ folder to .gitignore, so you don’t accidentally push the data to GitHub!

IV. Build your visualization

Create a data viz that helps to answer the question, How do FEMA National Risk Index scores for counties in California compare to those in other states? This may require some data wrangling.

BEFORE you start wrangling / building your viz, be sure to…

• explore the metadata – NRI_Table_Counties/ contains a metadata file named, NRIDataDictionary.csv, which describes each of the variables found in the data file (NRI_Table_Counties.csv). This is a helpful place to start!
• identify / jot down your variables of interest and consider which data types they are
• use online tools like from Data to Viz to help determine appropriate graph types, given your variables
• roughly sketch out your plots by hand (see why this step matters!)

Your final visualization should:

• include data for the 50 US states only (no territories)
• include a title (short, descriptive) & subtitle (describes main takeaway) (see Fundamentals of Data Visualization, Ch 22 for an example), a caption (describes the data source, e.g. “Data: FEMA National Risk Index (2023 Release)”), and alt text (following the formula described in week 3 discussion; use the fig-alt code chunk option to apply your alt text)
• consider and implement strategies for highlighting trends / important information (e.g. arranging data, highlighting data, adjusting scales)
• use custom colors (if applicable), rather than ggplot defaults
• have an updated / polished theme (you’ll learn more about fine-tuning ggplot themes in week 4 discussion)

Is your plot looking squished in your rendered .qmd file? Adjust the aspect ratio!

You can adjust the aspect ratio of your plot (i.e. its width to height ratio) so that your data / groups are easy to read. Add the fig-asp option to your code chunk YAML, which makes adjusting aspect ratios for rendered outputs quite easy. Values > 1 make your plot taller and values < 1 make your plot wider.

There is not necessarily one right answer (but don’t build a map)!

There are a number of different chart types that could work for these data and question, however we do not intend for you to build a choropleth map (e.g. replicating what you see on the The National Risk Index’s interactive mapping and data-based interface).

V. Answer some questions

• 1. What are your variables of interest and what kinds of data (e.g. numeric, categorical, ordered, etc.) are they (a bullet point list is fine)?

• 2. How did you decide which type of graphic form was best suited for answering the question? What alternative graphic forms could you have used instead? Why did you settle on this particular graphic form?

• 3. Summarize your main finding in no more than two sentences.

• 4. What modifications did you make to this visualization to make it more easily readable?

• 5. Is there anything you wanted to implement, but didn’t know how? If so, please describe.

Rubric (specifications)

To recieve a “Satisfactory” on HW #2:

• Create one visualization that answers the question, “How do FEMA National Risk Index scores for counties in California compare to those in other states?” and fulfills the requirements listed in Part IV.
• All code should follow appropriate styling conventions and include clear, informative annotations. We will not enforce any particular code style guide (you may use conventions from prior courses if you prefer; we recommend following these conventions from the Tidyverse style guide if you’re unsure where to start). You don’t need to explain every line, but your annotations should make it easy to follow your thinking and understand important decisions in your code.
• Answer all five Part V questions. There are no strict length requirements for free-response questions, however we expect that you answer them thoughtfully and fully.
• Ensure that everything described above is included in your neatly-organized and polished HW2.qmd file – this includes appropriate document YAML (at a minimum, title, author, date), clearly labeled sections, appropriately-formatted prose, appropriate code chunk options (e.g. code should render and execute, but warnings and messages should be suppressed, long data frames should not be printed out, etc.)
• All your work is housed in an organized and polished eds240-nri-acs-viz GitHub repository. Your README includes all required information, as described in the MEDS README Guidelines. Your data folder should is added to your .gitignore.
• Render HW2.qmd as a PDF. Double check that all of your outputs, code, and prose are visible.
• Upload your PDF to Gradescope by 11:59pm on Wed 01/28/2026.
• Submit a Generative AI Statement of Use for HW #2 by 11:59pm on Wed 01/28/2026.