> Spatial association rule mining is a useful tool for discovering correlations and interesting relationships among spatial objects. Co-locations, or sets of spatial events which are frequently observed together in close proximity, are particularly useful for discovering their spatial dependencies.
> Boolean spatial events (features) describe the presence of spatial events at different locations in geographic space. Examples of such data include disease outbreaks, crime incidents, traffic accidents, mobile service types, climate events, plants and species in ecology, and so on.
See figure 1 in the paper for a visual. It sounds like the purpose of this is to find sets of events that occur close to each other, physically, using MapReduce over large amounts of data.
Here's the paper: http://shagunsodhani.in/locis/docs/paper/paper.pdf
> Spatial association rule mining is a useful tool for discovering correlations and interesting relationships among spatial objects. Co-locations, or sets of spatial events which are frequently observed together in close proximity, are particularly useful for discovering their spatial dependencies.
> Boolean spatial events (features) describe the presence of spatial events at different locations in geographic space. Examples of such data include disease outbreaks, crime incidents, traffic accidents, mobile service types, climate events, plants and species in ecology, and so on.
See figure 1 in the paper for a visual. It sounds like the purpose of this is to find sets of events that occur close to each other, physically, using MapReduce over large amounts of data.