Spatial Anomaly Detection
Definition
Spatial anomaly detection identifies locations or times where patterns deviate from expected spatial structure. Techniques range from local indicators of spatial association (LISA) and scan statistics to density‑based clustering (DBSCAN/ST‑DBSCAN) and graph outlier detection. Analysts build baselines that account for seasonality, exposure, and autocorrelation, then flag hotspots, coldspots, or isolated outliers for investigation.
Application
Public health spots disease clusters early. Fraud teams find suspicious transactions or permits in unusual places. Transportation monitors unexpected crash spikes. Utilities detect leaks from pressure anomalies and consumption patterns. Ecology flags invasive species sightings outside known ranges.
FAQ
How do you avoid labeling high‑volume downtowns as anomalies just because they are busy?
Normalize by exposure—population, traffic, or transactions—so anomalies reflect rate change rather than raw counts. Incorporate expected spatial gradients into the baseline.
When is a space‑time scan preferable to a static hotspot map?
When clusters appear briefly (e.g., outbreaks or construction seasons). Space‑time windows detect bursts that a yearly aggregate would smooth away.
What validation steps confirm anomalies are not data artifacts?
Check for geocoding errors, reporting lags, sensor outages, and boundary changes. Cross‑validate with independent sources like social media, 311 calls, or field checks.
How should flagged anomalies be communicated to non‑experts?
Provide context (‘increase relative to expected baseline’), confidence levels, and clear next steps for investigation rather than definitive claims.