By capturing the spatio-temporal organization of the data using a graph, GraphScan avoids the challenges associated with trying to “fit” incoming data into moving windows of predefined shapes and sizes. Whereas the popular space-time permutation scan statistic [1] attempts to find clusters within spacetime volumes of predefined shape, GraphScan employs no such preconceptions about the form of the clusters. Instead, clusters are allowed to “evolve” freely to better reflect the structural properties of the data. Moreover, GraphScan is capable of tracking possible causal relationships between spatio-temporal events.
Objective
This paper proposes an efficient and flexible algorithm applicable to spatio-temporal aberration detection in public health data.