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Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships. A large fraction of the computation time is devoted to(More)
  • Shashi Shekhar, Ranga Raju Vatsavai, Mete Celik
  • 2008
Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial(More)
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the(More)
Zonal co-location patterns represent subsets of feature-types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However , discovering these patterns with dynamic parameters (i.e., repeated(More)
Mean streets represent those connected subsets of a spatial network whose attribute values are significantly higher than expected. Discovering and quantifying mean streets is an important problem with many applications such as detecting high-crime-density streets and high crash roads (or areas) for public safety, detecting urban cancer disease clusters for(More)
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the(More)
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as planning battlefield tactics, and tracking predator-prey interactions. However, determining suitable interest measure thresholds is a difficult(More)
Sustained emerging spatio-temporal co-occurrence patterns (SECOPs) represent subsets of object-types that are increasingly located together in space and time. Discovering SECOPs is important due to many applications, e.g., predicting emerging infectious diseases, predicting defensive and offensive intent from troop movement patterns, and novel predator-prey(More)
Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships. A large fraction of the computation time is devoted to(More)