Example Segregation Note

A compact Quarto note showing the structure for a research note that combines prose, R code, output, and interpretation.

Question

This starter blog note shows the publication pattern for short empirical notes. A real blog post should begin with a substantive question about segregation, mobility, or stratification, then keep the statistical machinery close enough for readers to audit.

Data and Measurement

Use this section to state the data source, unit of analysis, population, time period, and measurement choices. For spatial work, name the scale because the same empirical pattern can look different across neighborhoods, municipalities, commuting zones, or schools.

Analysis

set.seed(42)
places <- data.frame(
  place = paste("Area", 1:12),
  exposure = runif(12, 0.15, 0.75),
  mobility = runif(12, 0.2, 0.9)
)

model <- lm(mobility ~ exposure, data = places)
summary(model)

Call:
lm(formula = mobility ~ exposure, data = places)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.36111 -0.17365  0.08604  0.18960  0.26282 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)   0.5470     0.2846   1.922   0.0836 .
exposure      0.1941     0.5222   0.372   0.7179  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.252 on 10 degrees of freedom
Multiple R-squared:  0.01363,   Adjusted R-squared:  -0.08501 
F-statistic: 0.1382 on 1 and 10 DF,  p-value: 0.7179

Interpretation

The prose should do more than repeat the table. Explain what comparison is being made, what uncertainty remains, and which interpretation is justified by the design.

Sensitivity

Good candidates for sensitivity checks include alternate spatial scales, different definitions of exposure, influential places, and changes in model specification.