216 – Geovisualization
Identifying and Visualizing Spatiotemporal Clusters on Map Tiles
Markus Loecher
Berlin School of Economics and Law
Scoring unusual events in space and time has been an active and important field of research for decades: How do we (i) distinguish normal fluctuations in a stochastic count process from real additive events, (ii) identify spatiotemporal clusters where the event is most strongly pronounced ? and (iii) how do we efficiently graph these clusters in a map overlay ? Supervised learning algorithms are proposed as an alternative to the computationally expensive scan statistic. The task can be reduced to detecting over-densities in space relative to a background density. We frame the relative density estimation as a binary classification problem. In the light of recent advances of embedding map tiles in statistical software via the library RgoogleMaps we have developed an integrated hotspot visualizer. We can now efficiently identify and visualize spatiotemporal clusters in one environment.
