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Hotspot/cluster detection methods(1) Spatial Scan Statistics: Hypothesis testing – Input: data – Using continuous Poisson model Null hypothesis H0: points.

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Presentation on theme: "Hotspot/cluster detection methods(1) Spatial Scan Statistics: Hypothesis testing – Input: data – Using continuous Poisson model Null hypothesis H0: points."— Presentation transcript:

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 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


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