hotspot/cluster detection methods(1)
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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