Spatial Pattern Analysis
Definition
Spatial pattern analysis investigates how points, lines, and areas are arranged in space, determining whether distributions are clustered, dispersed, or random and at what scales. Methods include nearest-neighbor analysis, quadrat counts, Ripley’s K/L functions, kernel density estimation, tessellations, and spatial scan statistics. For polygons and rasters, landscape metrics quantify fragmentation, patch shape, and edge density. Multi-scale thinking is essential because patterns can switch from clustered to dispersed across scales.
Application
Ecologists assess habitat fragmentation; criminologists study hotspot dynamics; retailers analyze customer locations; epidemiologists examine disease clusters; and urban morphologists quantify street and block structures. Pattern insights guide interventions—from reserve design to police deployment to store placement.
FAQ
Why does scale selection change conclusions about clustering?
Processes act at different ranges; what looks clustered at 100 m may appear dispersed at 10 km. Functions like Ripley’s K inspect multiple radii to reveal scale-dependent behavior instead of a single summary.
How can boundary and edge effects bias results?
Points near study edges have fewer neighbors within the window; corrections (guard zones, toroidal wraps) reduce undercounting that would falsely suggest dispersion.
What is the value of Monte Carlo simulation in pattern analysis?
Simulated random (or constrained) patterns form a null model. Comparing observed statistics to the simulated distribution yields significance rather than relying on asymptotic assumptions.
How do you communicate pattern findings responsibly?
Report the scales tested, methods used, and uncertainty bands; avoid deterministic language that might imply causation when only association is shown.