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

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

SatScan Result

1 clusters foundBut insignificant

DBSCAN results: CSR

2 clusters found

DBSCAN results: CSR

6 clusters found

DBSCAN results: CSR

7 clusters found

Results from SatScan and DBSCAN

SatScan results

DBSCAN result

5 clusters found

DBSCAN result

3 clusters found

DBSCAN result

6 clusters found

DBSCAN result

6 clusters found