Homework Assignment 1

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 GitHub Classrooms. Each student receives one “free pass” for not submitting assignments via specified channels, after which you will receive a “Not Yet” mark.

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

Clone repository from GitHub Classrooms
Download data from here
Unzip data and place in repository
Edit Quarto document with responses
Push final edits before deadline

Your repository should have the following structure:

EDS223-HW1
│   README.md
│   HW1.qmd
│   Rmd/Proj files    
│
└───data
     └───easter-island
     └───ejscreen

Part 1: Easter Island from land to sea

In this week’s discussion section, you practiced making a map of Easter Island. We will continue by extending our map into the sea by including bathymetry and seamounts.

Description

Create a map of Easter Island, including the following:

the island’s border and road network
elevation of the island (indicated in meters above sea level)
location of the island’s volcanoes (indicating their height in meters)
bathymetry surrounding the island (indicated in meters below sea level)
surrounding seamounts (indicating their height in meters)

Data details

You will use the following new datasets:

Seamounts from the Pacific Data Hub
Bathymetry (or seadepth) from NOAA’s marmap API

The following code is provided to read in each dataset:

library(sf)
library(here)
library(terra)

# read in major points on the island
ei_points <- sf::read_sf(here::here("data", "easter_island", "ei_points.gpkg"))
# subset points to volcanoes
volcanoes <- subset(ei_points, type == "volcano")

# read in island elevation
ei_elev <- terra::rast(here::here("data", "easter_island", "ei_elev.tif"))

# read in island border
ei_borders <- sf::read_sf(here::here("data", "easter_island", "ei_border.gpkg"))

# read in island road network
ei_roads <- sf::read_sf(here::here("data", "easter_island", "ei_roads.gpkg"))

# read in bathymetry
ei_bathymetry <- terra::rast(here::here("data", "easter_island", "ei_bathy.tif"))

# read in seamounts
ei_seamounts <- sf::read_sf(here::here("data", "easter_island", "ei_seamounts.gpkg"))

Setting a bounding box for the map

To make sure that all the bathymetry and seamount data are included in the map, create a bounding box that includes both.

# define larger plot bbox that is buffered around both of 
# the two largest layers to display all 4 seamounts in view 

bbox_seamount <- st_bbox(ei_seamounts) # seamount bounding box
bbox_bathymetry <- st_bbox(ei_bathymetry) # bathymetry bounding box
bbox_largest <- st_bbox(c(xmin = min(bbox_bathymetry[1], bbox_seamount[1]),
                         ymin = min(bbox_bathymetry[2], bbox_seamount[2]),
                         xmax = max(bbox_bathymetry[3], bbox_seamount[3]),
                         ymax = max(bbox_bathymetry[4], bbox_seamount[4])))

Now that you’ve defined the bounding box, make sure to use it!

Part 2: Exploring environmental (in)justice

As many of us are aware, environmental degradation, pollution, and hazards is not felt equally by all people. Environmental justice has many definitions, but the United States Environmental Protection Agency defines it as follows:

“Environmental justice” means the just treatment and meaningful involvement of all people, regardless of income, race, color, national origin, Tribal affiliation, or disability, in agency decision-making and other Federal activities that affect human health and the environment so that people:

are fully protected from disproportionate and adverse human health and environmental effects (including risks) and hazards, including those related to climate change, the cumulative impacts of environmental and other burdens, and the legacy of racism or other structural or systemic barriers; and

have equitable access to a healthy, sustainable, and resilient environment in which to live, play, work, learn, grow, worship, and engage in cultural and subsistence practices

For far too long, environmental inequities have been invisible, and thus ignored. Mapping environmental inequities can be a powerful tool for revealing injustices. We will be working with data from the United States Environmental Protection Agency’s EJScreen: Environmental Justice Screening and Mapping Tool.

According to the US EPA website:

This screening tool and data may be of interest to community residents or other stakeholders as they search for environmental or demographic information. It can also support a wide range of research and policy goals. The public has used EJScreen in many different locations and in many different ways.

EPA is sharing EJScreen with the public:

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.

Description

For this assignment, you will explore an environmental justice topic of your choosing. You should select a region, community, or environmental issue that matters to you.

You must complete the following:

create two maps that communicate an environmental justice issue
write a brief paragraph explaining what your maps communicate

Data details

EJScreen provides 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

You should also explore the limitations and caveats of the data.

The following code provides examples for reading and manipulating the EJScreen data. You MUST update this code to suite your problem of interest.

library(tidyverse)
library(sf)
library(here)

# read in geodatabase of EJScreen data at the Census Block Group level
ejscreen <- sf::st_read(here::here("data", "ejscreen","EJSCREEN_2023_BG_StatePct_with_AS_CNMI_GU_VI.gdb")) 

# filter to a state you are interested in
california <- ejscreen %>%
  dplyr::filter(ST_ABBREV == "CA") 

# filter to a county you are interested in
santa_barbara <- ejscreen %>%
  dplyr::filter(CNTY_NAME %in% c("Santa Barbara County"))

# find the average values for all variables within counties
california_counties <- aggregate(california, by = list(california$CNTY_NAME), FUN = mean)

Rubric (specifications)

You must complete the following to receive a “Satisfactory” on the assignment:

All maps must include the following elements:
- an informative title
- legends with legible titles, including units
- indication of scale and orientation (i.e. graticules/gridlines or scale bar and compass)
- color scales that are accessible (i.e. make intuitive sense) and appropriate to the data (i.e. discrete vs. continuous)
EJScreen maps and text must communicate about an issue
- it is not sufficient to make two unrelated maps
The rendered Quarto doc must show all required elements in a professional style
- see examples of professional and unprofessional output on the Assignments page