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Hotspot/cluster detection methods(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

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Hotspot/cluster detection methods(1)

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  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

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