HW #3: Visualizing FEMA NRI x ACS Data

Assigned Wed 02/04/2026 | Due Wed 02/11/2026

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 to natural hazards and demographics data, courtesy of the FEMA National Risk Index (NRI) and the US Census Bureau’s American Community Survey (ACS).

I. Background reading

Refamiliarize yourself with the FEMA’s NRI and learn more about the ACS. (unfold the following note, which is collapsed to save space):

About FEMA’s National Risk Index (NRI) for Natural Hazards (repeated from HW #2)

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.

About the US Census Bureau’s American Community Survey (ACS)

The American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Unlike the Decennial Census (which counts every person in the US every 10 years for the purpose of congressional appointment), the ACS collects detailed information from a small subset of the population (~3.5 million households) at 1- and 5-year intervals. Learn more about the differences between these 1- and 5-year estimates.

Accessing ACS Data

The US Census Bureau provides a couple of tools for accessing their data, including:

  1. data.census.gov: a browser-based portal for exploring the many available data tables (e.g. Table B02001: Race)
  2. The Census Data API: a data service that enables software developers to access and use Census Bureau data within their applications

However, when working in R, the {tidycensus} package is arguably the easiest way to query and retrieve Census data – use the get_acs() function to obtain ACS data for specified geographies (e.g. counties or census tracts), tables (e.g. B02001), variables (e.g. B02001_002, B02001_003), years (e.g. 2023), states (e.g. CA), surveys (e.g. acs1, acs5), etc.

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

Make the following modifications to your eds240-nri-acs-vizrepository:

III. Import & save ACS data

Let’s download the necessary data using the US Census Bureau’s API via the {tidycensus} package and write a copy to file. We’ll then read that saved copy of our data back in for use.

The following three steps describe the code you see in the following chunk. You may copy this code into the top of your HW3.qmd file, where you’d normally read in your data (remember to load any necessary packages first, though, and clean up the code comments as necessary).

  1. Import ACS data using tidycensus::get_acs(): You’ll need your API key to use {tidycensus} functions (revisit week 2 pre-class prep instructions, if necessary).

  2. Write your data to .csv: It’s always a good idea to write your data (i.e. the race_ethnicity data frame, from above) to file, in case the Census Bureau’s API goes down. If you write your csv file to your data/ folder, it will automatically be ignored by .gitignore.

  3. Read in your saved ACS-race-ethnicity.csv file.

#....Step 1a: see all available ACS variables + descriptions.....
acs_vars <- tidycensus::load_variables(year = 2023,
                                       dataset = "acs1")

#..............Step 1b: import race & ethnicity data.............
race_ethnicity <- tidycensus::get_acs(
  geography = "county",
  survey = "acs1",
  # NOTE: you may not end up using all these variables
  variables = c("B01003_001", "B02001_002", "B02001_003", 
                "B02001_004", "B02001_005", "B02001_006",
                "B02001_007", "B02001_008", "B03002_012",
                "B03002_002"),
  state = "CA", 
  year = 2023) |>
  # join variable descriptions (so we know what's what!)
  dplyr::left_join(acs_vars, by = dplyr::join_by(variable == name)) 

#.................Step 2: write ACS data to file.................
readr::write_csv(race_ethnicity, here::here("data", "ACS-race-ethnicity.csv"))

#..................Step 3: read in your CSV file.................
race_ethnicity <- readr::read_csv(here::here("data", "ACS-race-ethnicity.csv"))
  1. At this point, you should comment out code from steps 1 & 2 (lines 1-20, above) so that you’re not pinging the API & overwriting your saved copy each time you render HW3.qmd. Comment these lines out, rather that delete them altogether, so that anyone checking out your work understands how you initially retrieved the data.

IV. Build your visualization

Create a data viz that helps to answer the question, How does climate hazard risk exposure vary across racial / ethnic groups in California? This will require some data wrangling first (including joining the NRI and ACS data).

  • 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:

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.

V. Answer some questions

Rubric (specifications)

To recieve a “Satisfactory” on HW #3: