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_neighbors then 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. SatScan results

9. DBSCAN result 5 clusters found

10. DBSCAN result 3 clusters found

11. DBSCAN result 6 clusters found

12. DBSCAN result 6 clusters found