Week 01 · August 24–26
Frame the study.
Begin with a consequential environmental-policy problem, test whether accessible data can support it, and establish a reproducible place to record the project’s decisions.
Mon & Wed · 12:30–1:45 PM South 227B
Note
Office hours: Mondays and Wednesdays, 1:45–3:15 PM, in South 227B—immediately after class.
This week
Research question
- Identify two or three environmental-policy questions.
- Name the affected people, places, institutions, and possible mechanism.
- Separate descriptive, predictive, and causal ambitions.
Data feasibility
- Find two candidate open-data sources.
- Record unit, time, geography, provenance, and access method.
- Identify the first important coverage or quality limitation.
Reproducible setup
- Open the course R project and a research log.
- Keep source data separate from analysis-ready data.
- Record every consequential choice as the project changes.
Before class
- Read the course syllabus.
- Browse the Build one coherent data stack and Potential environmental data sources sections of the Data Lab.
- Bring a laptop with R and RStudio ready to use.
- Arrive with one environmental issue, place, resource, or policy that you would genuinely like to understand.
Lecture slides
TipLecture slide shortcuts
Click inside the slide preview, then use:
- ←/→ or ↑/↓ to page through the slides;
- M to open the menu;
- F to enter fullscreen;
- E to enter or exit PDF export mode;
- Ctrl + Shift + F to search within the slides;
- Esc to exit the menu or fullscreen; and
- T to switch between dark and light mode.
Class focus
Across the week, we will:
- review the course arc, logistics, and evaluation;
- move from broad environmental concerns to researchable policy questions;
- identify stakeholders, mechanisms, and possible data sources;
- establish a reproducible project structure and research log;
- inspect a manageable file or API response for its unit of observation, variables, coverage, missingness, and provenance; and
- create one quick diagnostic figure and compare candidate data paths.
Use familiar tools—readr, dplyr, and ggplot2—for the initial data inspection. Spatial and machine-learning tools come later, after the question and data structure are clear.
By the end of Week 1
Prepare a brief Question + Data Scan containing:
- two or three candidate research questions;
- one preferred direction and why its policy stakes matter;
- two candidate datasets with source links;
- the likely unit of observation, time span, geographic coverage, and provenance of each; and
- one initial plot or data-quality screenshot, plus the most important limitation it reveals.
This is a formative working artifact, not a new graded course requirement. It becomes the starting point for Week 2’s literature and evidence work.