Spatio-temporal forecasting of future risk from past events

Abstract

Computational processes and their associated data structures representing past events of interest in a geographic area and recent time period, contextual information such as terrain data, and labeled space-time probability fields are continuously executed to generate and update a spatial probability field that conveys the risk of similar such events occurring in the near future at given locations in the area of interest. The invention specifies two computational processes operating in shared data structures, one tracing back in time known past events to probable origin locations while accounting for movement constraints and location preferences, the other projecting event risk forward in time from likely origin locations, accounting for movement constraints and targeting preferences. The invention further specifies that these two processes may tune each others' parameters through the evaluation of the accuracy of the recall of past events, thus generating more accurate future event risk forecasts.

Dependencies are inferred from the claim text.