Fire! Firing Inductive Rules from Economic Geography for Fire Risk Detection
Wildfires can importantly affect the ecology and economy of large regions of the world. Effective prevention techniques are fundamental to mitigate their consequences. The design of such preemptive methods requires a deep understanding of the factors that increase the risk of fire, particularly when we can intervene on these factors. This is the case for the maintenance of ecological balances in the landscape that minimize the occurrence of wildfires. We use an inductive logic programming approach over detailed spatial datasets: one describing the landscape mosaic and characterizing it in terms of its use; and another describing polygonal areas where wildfires took place over several years. Our inductive process operates over a logic term representation of vectorial geographic data and uses spatial predicates to explore the search space, leveraging the framework of Spatial-Yap, its multi-dimensional indexing and tabling extensions. We show that the coupling of a logic-based spatial database with an inductive logic programming engine provides an elegant and powerful approach to spatial data mining.
KeywordsBurnt Area Economic Geography Spatial Object Inductive Logic Programming Inductive Rule
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