Speaker
Description
The temporal dimension of geographic data is often ignored such that outcomes from analysis of geographic objects or events are often misleading. Researchers often apply spatial analysis to identify hot/cold spots of geographic events, such as crime or diseases occurrences. However, hot/cold spots do not remain constant over time. There may be hot/cold spots that are just emerging or slowly diminishing. Alternatively, hot/cold spots may be stubborn through time, or some locations may never be hot/cold spots of the events.
As the importance of temporal dimension embedded in geographic data slowly gaining attention, a number of quantitative methods have been developed for finding spatiotemporal clusters of geographic objects or events.
This presentation will highlight some of these newly developed methods and their possible applications.
