Homework Assignment 2

Mapping Inequality

Important

You must earn a “Satisfactory” on all parts of the assignment to earn a “Satisfactory” on the assignment.

The assignment must be submitted through Gradescope.

Read each part of the assignment carefully, and use the check boxes to ensure you’ve addressed all elements of the assignment!

Learning outcomes

• build effective, responsible, accessible and aesthetically-pleasing maps
• practice manipulating vector and raster data to build multi-layer maps
• practice making maps in R, specifically using tmap

Instructions

1. Create a new GitHub repository.Be sure to include a README that follows these guidelines

2. Download data from here

3. Unzip data and place in repository

4. Add the data to your gitignore!

5. Create a Quarto document with responses

6. Render your quarto document as a pdf and submit on Gradescope

   • Make sure code-fold is not set to True! Your pdf should contain all your code and outputs!

Your repository should have the following structure:

EDS223-HW2
│   README.md
│   HW2.qmd
│   Rmd/Proj files    
│
└───.gitignore
     └───data
         └───ejscreen
         └───gbif-birds-LA
         └───mapping-inequality

Background

Present-day environmental justice may reflect legacies of injustice in the past. The United States has a long history of racial segregation which is still visible. During the 1930’s the Home Owners’ Loan Corporation (HOLC), as part of the New Deal, rated neighborhoods based on their perceived safety for real estate investment. Their ranking system, (A (green), B (blue), C (yellow), D (red)) was then used to block access to loans for home ownership. Colloquially known as “redlining”, this practice has had widely-documented consequences not only for community wealth, but also health.¹ Redlined neighborhoods have less greenery² and are hotter than other neighborhoods.³

Check out coverage by the New York Times.

A recent study found that redlining has not only affected the environments communities are exposed to, it has also shaped our observations of biodiversity.⁴ Community or citizen science, whereby individuals share observations of species, is generating an enormous volume of data. Ellis-Soto and co-authors found that redlined neighborhoods remain the most undersampled areas across 195 US cities. This gap is highly concerning, because conservation decisions are made based on these data.

Check out coverage by EOS.

Data details

EJScreen

Data file: ejscreen/EJSCREEN_2023_BG_StatePct_with_AS_CNMI_GU_VI.gdb

We will be working with data from the United States Environmental Protection Agency’s former EJScreen: Environmental Justice Screening and Mapping Tool. This tool is no longer operating, but you can find an unofficial version of it here

According to the former US EPA website:

The tool, when in existence, was intended to support research and policy goals. It was shared with the public to achieve the following:

• to be more transparent about how we consider environmental justice in our work,
  
• to assist our stakeholders in making informed decisions about pursuing environmental justice and,
  
• to create a common starting point between the agency and the public when looking at issues related to environmental justice.

EJScreen provided on environmental and demographic information for the US at the Census tract and block group levels. You will be working with data at the block group level that has been downloaded from the EPA site. To understand the associated data columns, you will need to explore the following in the data folder:

• Technical documentation: ejscreen-tech-doc-version-2-2.pdf
• Column descriptions: EJSCREEN_2023_BG_Columns.xlsx

HOLC Redlining

Data file: mapping-inequality/mapping-inequality-los-angeles.json

A team of researchers, led by the Digital Scholarship Lab at the University of Richmond have digitized maps and information from the HOLC as part of the Mapping Inequality project.

We will be working with maps of HOLC grade designations for Los Angeles. Information on the data can be found here.⁵

Biodiversity observations

Data file: gbif-birds-LA.shp

The Global Biodiversity Information Facility is the largest aggregator of biodiversity observations in the world. Observations typically include a location and date that a species was observed.

We will be working observations of birds from 2021 onward.

Tip

Make sure to check that these datasets have the same coordinate reference systems! If not, transform them to match.

Part 1: Legacy of redlining in current environmental (in)justice

Your first task is to explore historical redlining in Los Angeles and its legacy on present-day environmental justice.

Description

For this assignment, you must complete the following:

1. Create a map of historical redlining neighborhoods, including:

• neighborhoods colored by HOLC grade
• an appropriate base map
• a clear title and legend

2. Create a table summarizing:

• the percentage of census block groups that fall within each HOLC grade
• Also include the percent of census black groups that don’t fall within a HOLC grade
• Hint: The HOLC data contains the grades and the EJScreen data contains the census blocks, so you will need to combine the data spatially before doing summary statistics. Once you combine and no longer need the geometries, you can use st_drop_geometry().

3. Create at least two visualizations summarizing current conditions (from the EJScreen data) within HOLC grades using the mean of the following variables (you may combine variables or create separate plots):

• % low income
• percentile for Particulate Matter 2.5
• percentile for low life expectancy
• Use ggplot for your visualizations! You will first need to calculate mean of each variable grouped by HOLC grade.

4. Write a brief paragraph reflecting on these results

• Interpret the patterns you observe in your results
• Discuss potential relationships between historical redlining grades and current environmental/socioeconomic conditions

Part 2: Legacy of redlining in biodiversity observations

Your second task is to explore the legacy of historical redlining in Los Angeles on the collection of bird observations.

Description

For this assignment, you must produce the following based on observations from 2021-2023:

1. A figure summarizing the percent of observations within redlined neighborhoods within each HOLC grade

   Create a visualizations that shows:

   • The percentage of bird observations within each HOLC grade
   • Include an appropiate title, axis labels, and legend
   • Hints: Ensure the bird observations and HOLC dataset have matching CRS’, then perform a spatial join to assign each bird observations to a corresponding HOLC grade.

2. Spolier alert!! Our results don’t match the findings from Ellis-Soto et al. 2023! Read the abstract of the study. Why might we have obtained different results in our analysis? What did the paper consider that we did not?

Rubric (specifications)

Assignments will be deemed “Satisfactory” based on the following criteria:

Data analysis

• Code must produce expected output based on correct data manipulation
• Spatial operations must be performed correctly with appropriate verification (e.g., CRS matching, appropriate join methods, checking results)
• Multiple approaches can be used to reach the correct output. Analysis must demonstrate critical interrogation of the approach used by showing justification and verification of intermediate steps. “Correct” answers are not sufficient. Justification and verification of approach should be demonstrated using the following:
  • custom warning and error message (e.g. warning() and stop(); resources from EDS 221)
  • informative comments (resource from EDS 220)
  • BONUS: unit tests (e.g. using {testthat}; resources from EDS 221)

Plots, tables, and maps

• All plots, tables, and maps must be clear, accurate, and effectively convey the intended information
• All plots and maps must include the following elements:
  • an informative title
  • legends with legible titles, including units
  • color scales that are accessible (i.e. make intuitive sense) and appropriate to the data (i.e. discrete vs. continuous)
  • for maps: indication of scale and orientation (i.e. graticules/gridlines or scale bar and compass), and an appropiate basemap included where necessary
• All tables should be rendered with legible titles and styling (e.g. {gt})

Written reflections

• Reflections must be clear, accurate, and demonstrate a deep understanding of the analysis performed.

Professional Output

• The rendered Quarto doc must show all required elements in a professional style with explanation of each step of analysis
  • see examples of professional and unprofessional output on the Assignments page

Footnotes

1. Gee, G. C. (2008). A multilevel analysis of the relationship between institutional and individual racial discrimination and health status. American journal of public health, 98(Supplement_1), S48-S56.

2. Nardone, A., Rudolph, K. E., Morello-Frosch, R., & Casey, J. A. (2021). Redlines and greenspace: the relationship between historical redlining and 2010 greenspace across the United States. Environmental health perspectives, 129(1), 017006.

3. Hoffman, J. S., Shandas, V., & Pendleton, N. (2020). The effects of historical housing policies on resident exposure to intra-urban heat: a study of 108 US urban areas. Climate, 8(1), 12.

4. Ellis-Soto, D., Chapman, M., & Locke, D. H. (2023). Historical redlining is associated with increasing geographical disparities in bird biodiversity sampling in the United States. Nature Human Behaviour, 1-9.

5. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, accessed October 17, 2023, https://dsl.richmond.edu/panorama/redlining/