Moran's I Spatial Autocorrelation Visualizer¶
Learning Objective¶
Students can interpret values of Moran's I by manipulating clustering strength, spatial dispersion, and outlier injection on a 10x10 grid of simulated county disease rates and observing how each manipulation changes the global Moran's I statistic and the shape of the Moran's scatter plot.
Specification¶
The full specification below is extracted from Chapter 17: "Data Science: Advanced Methods".
Type: microsim
sim-id: morans-i-visualizer
Library: p5.js
Status: Specified
Display a 10x10 grid of counties, each colored by a simulated disease rate
(five-color sequential scale, light yellow to dark red). Three sliders control
the simulation:
1. Clustering Strength (0 to 1): At 0, rates are random (Moran's I near 0).
At 1, a spatial smoothing algorithm creates strong regional clusters.
2. Spatial Dispersion (-1 to 0): Creates a checkerboard-like alternating
pattern (negative spatial autocorrelation).
3. Outlier Injection (0 to 5): Introduces N high-rate cells surrounded by
low-rate neighbors (spatial outliers).
A real-time Moran's I value (computed from the queen-contiguity spatial weights
matrix) updates in a large display panel to the right of the grid as sliders
change. Below the Moran's I value, display:
- Interpretation text (clustered / random / dispersed)
- A small Moran's I scatter plot (spatial lag on y-axis, value on x-axis, with
the four quadrant labels: HH, HL, LL, LH)
Clicking any cell highlights it and its queen-contiguous neighbors with orange
borders, displaying the local contribution to Moran's I in the side panel.