Week 3 notes

Key Concepts Learned

Anscombe’s Quartet and the limits of summary statistics

- Depending on the way information is visualized, it can show completely different patterns.

- “How to lie with stats/maps”

- Outliers may represent important communities

- Relationships aren’t always linear

Exploratory Data Analysis is detective work

Data → Aesthetics → Geometries → Visual

- Data: Your data set (census data, survey responses, etc.)

- Aesthetics: What variables map to visual properties (x, y, color, size)

- Geometries: How to display the data (points, bars, lines)

- Additional layers: Scales, themes, facets, annotations

Coding Techniques

ggplot(data = your_data) + aes(x = variable1, y = variable2) + geom_something() + additional_layers()

Aesthetics map data to visual properties:

• x, y - position

• color - point/line color

• fill - area fill color

• size - point/line size

• shape - point shape

• alpha - transparency

• left_join() - Keep all rows from left dataset

• right_join() - Keep all rows from right dataset

• inner_join() - Keep only rows that match in both

• full_join() - Keep all rows from both datasets

Important: Aesthetics go inside aes(), constants go outside

Questions & Challenges

Pattern: Smaller populations have higher uncertainty

Ethical implication: These communities might be systematically under counted

1. Report the corresponding MOEs of ACS estimates - Always include margin of error values
2. Include a footnote when not reporting MOEs - Explicitly acknowledge omission
   
3. Provide context for (un)reliability - Use coefficient of variation (CV):
   • CV < 12% = reliable (green coding)
   • CV 12-40% = somewhat reliable (yellow)
   • CV > 40% = unreliable (red coding)
4. Reduce statistical uncertainty - Collapse data detail, aggregate geographies, use multi-year estimates
5. Always conduct statistical significance tests when comparing ACS estimates over time

Connections to Policy

Research finding: Only 27% of planners warn users about unreliable ACS data

- Most planners don’t report margins of error

- Many lack training on statistical uncertainty

Common problems in government data presentation:

- Misleading scales or axes

- Cherry-picked time periods

- Hidden or ignored uncertainty

- Missing context about data reliability

Reflection

Reminder to study through definitions more closely.