Hotspot/cluster detection methods(1)

1. Hotspot/cluster detection methods(1) • Spatial Scan Statistics: Hypothesis testing • Input: data • Using continuous Poisson model • Null hypothesis H0: points are randomly distributed (CSR) • Alternative hypothesis H1: points are clustered in zone Z • Enumerate all the zones and find the one that maximizes likelihood ratio • L = p(H1|data)/p(H0|data) • Test statistical significance: Monte Carlo simulation • Generate the data for 1000 times and see how many times can we get a higher L

2. Hotspot/cluster detection methods(2) • DBSCAN: Density-based spatial clustering of application with noise • Input: data, radius, min_neighbors • For each data point P: • If neighbors<min_neighborsthen mark P as noise • eles • Add P to a new cluster C • Expand P by looking at points P’ in the current neighborhood of C • If P’ is not in any cluster then add P’ to C • If neighbors of P’> min_neighbors then add P’s neighbor to C’s neighborhood

3. SatScan Result 1 clusters found But insignificant

4. DBSCAN results: CSR 2 clusters found

5. DBSCAN results: CSR 6 clusters found

6. DBSCAN results: CSR 7 clusters found

7. Results from SatScan and DBSCAN

8. SatScanresults

9. DBSCAN result 5 clusters found

10. DBSCAN result 3 clusters found

11. DBSCAN result 6 clusters found

12. DBSCAN result 6 clusters found