Discovering Spatial Co-location Patterns
A New Methodology
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Spatial co-location patterns represent the subsets of events (features) whose instances are frequently located together in a geographic space. It resembles the association rules mining in many aspects. However, they are applied in completely different applications. The co-location patterns discovery presents challenges since the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships. In this book, we provide a study based on some previous approaches, the concepts that were used, and some of their limitations. We propose a methodology which o...
Spatial co-location patterns represent the subsets of events (features) whose instances are frequently located together in a geographic space. It resembles the association rules mining in many aspects. However, they are applied in completely different applications. The co-location patterns discovery presents challenges since the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships. In this book, we provide a study based on some previous approaches, the concepts that were used, and some of their limitations. We propose a methodology which overcomes the shortcomings of some other approaches. This methodology is based on a spatial access method (KD-Tree) with its basic operations and the apriori generation algorithm. The results of conducted experimentation show the correctness and completeness of our approach. The results also illustrate the effect of input data on the performance. An interface which displays the results graphically is provided.
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