1. A leaf node ranking method in decision trees, performed by a computer, for spatial prediction, comprising: a spatial prediction data preparation operation that performs a geometric correction to a spatial event occurrence layer and a spatial event inducing environment layer in order that locations of the layers correspond to locations of each other with the same range, the layers being prediction targets and being data that the decision tree learns; a decision tree construction operation that constructs a decision tree with one root node, in which each parent node has multiple child nodes, using the spatial prediction data as training data; a leaf node ranking operation that reflects a ranking result calculated from a root node on a child node using the decision tree, and calculates a rank using the number of event-occurring classes and of non-event-occurring classes, the number of classes being included in each node on paths from the root node to all leaf nodes; and a prediction result obtaining operation that maps the rank (priority) of the leaf node to a location on a spatial coordinate, each node storing the number of event-occurring classes and of non-event-occurring classes in which training data is classified, for computing the rank of the leaf node.