What exactly is spatiotemporal clustering?

Spatiotemporal clustering is a data mining technique that identifies groups of events or objects that are concentrated together in both geographical space and a specific period of time.

Why is spatiotemporal clustering important for analysis?

It's crucial because it reveals patterns, hotspots, or outbreaks that might be missed by analyzing spatial or temporal data independently, enabling more accurate predictions and targeted interventions.

What are common applications of spatiotemporal clustering?

Typical applications include detecting disease outbreaks, identifying crime hotspots, analyzing traffic congestion, tracking seismic activity, and spotting unusual patterns in financial transactions or social media.

What kind of data is needed for this type of analysis?

The primary data required are geographical coordinates (like latitude and longitude) and a precise timestamp for each event or observation. Additional attributes can also be incorporated.