1. A leaf node ranking method in decision trees, performed by a computer, for spatial prediction, comprising: a memory; and program instructions in the memory for: a spatial prediction data preparation operation that performs a geometric correction to a spatial event occurrence layer and a spatial event inducing environment layer such that locations of the spatial event occurrence layer and the spatial event inducing environment layer correspond to locations of the other layer within the same range, the spatial event occurrence layer and the spatial event inducing environment layer being prediction targets and data that a decision tree learns; a decision tree construction operation that constructs the decision tree using the spatial prediction data as training data, the decision tree including a root node, a parent node, and a plurality of child nodes associated with the parent node, a leaf node ranking operation that (i) performs a ranking result using the decision tree, the ranking result calculated from the root node on the child node, and (ii) calculates a rank using a number of event-occurring classes and a number of non-event-occurring classes included in each node on paths from the root node to all leaf nodes; and a prediction result obtaining operation that maps the rank of the leaf node to a location on a spatial coordinate, each node storing the event-occurring classes and non-event-occurring classes in which training data is classified, for computing the rank of the leaf node, wherein the leaf node ranking operation is performed using the equation: R ⁡ ( node. child ) = R ⁡ ( node. parent ) + n event ( n event + n non_event ) × d , where (a) R(node.child) represents the rank of the child node, (b) R(node.parent) represents the rank of the parent node, (c) n event represents the number of event-occurring classes, (d) n non event represents the number of non-event occurring classes, and (e) d represents a depth of a node in a tree.