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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of forecasting future risk from past events, comprising the steps of: receiving and storing, in a computer memory, information regarding one or more previous events that occurred in a region of interest, the information including the spatio-temporal coordinates of each event; providing a computer programmed to access the memory and automatically perform a hindcasting process wherein the previous events are traced through a first set of time-indexed spatial probability distributions to determine possible geospatial and temporal origins of the previous events; and automatically performing a forecasting process by projecting, from the possible origins of the previous events through space and time, a second set of time-indexed spatial probability distributions to determine whether an event similar to one or more of the previous events will occur in the region. 2. The method of claim 1, wherein each event is defined by latitude and longitude coordinates and the time of occurrence. 3. The method of claim 1, wherein the region of interest is a geographical area and the event involve human actions or interactions. 4. The method of claim 1, wherein the spatial probabilities associated with one or both of the hindcasting and forecasting processes take into account constraints or preferences. 5. The method of claim 1, wherein the first and second sets of time-indexed spatial probability distributions form virtual cones in a spatio-temporal volume. 6. The method of claim 1, including the step of characterizing an event by a scalar measure of magnitude and other descriptors (e.g., labels). 7. The method of claim 1, wherein, if events in an area of interest are growing over time, the estimate of event risk also evolves to account for new events. 8. The method of claim 1, wherein: the first set of time-indexed spatial distributions includes the likelihood, for any given point in time in the past, that a given geographic location is the origin for one or more future events, and the second set of time-indexed spatial distributions includes the likelihood, for any given point in time in the past or near future, that an event may occur at that geographic location. 9. The method of claim 8, wherein the distributions are constructive simulations of an entity's geographic movement. 10. The method of claim 8, wherein the distributions represent the constrained propagation of units of probability through space and time. 11. The method of claim 1, wherein: the hindcasting process traces the possible movements of event perpetrators back from event locations to estimated origin locations to establish a spatio-temporal “event origin” probability field over the area of interest from a hindcast horizon to an index of “now”; the forecasting process traces, from the “event origin” probability field, the possible movements of the event perpetrators forward from estimated event origins to forecast event risk locations to establish a spatio-temporal “event risk” probability field that spans the entire temporal volume over the area of interest from the hindcast horizon, through the present time, to a “near future” distribution; and wherein the process outputs the “event risk” spatial distribution temporally indexed as the “near future” distribution. 12. The method of claim 11, wherein both the hindcasting and forecasting processes take perpetrator movement preferences or constraints into account. 13. The method of claim 11, including the step of using one or more atemporal reasoning processes to refine the “event origin” field. 14. The method of claim 11, including the step of using one or more atemporal reasoning processes to refine the “event risk” field. 15. The method of claim 1, wherein the hindcasting and forecasting processes are continuously optimized to accurately replicate the pattern of recent events and thereby maximize the confidence in the event probability distribution generated for the near future. 16. The method of claim 1, including the step of defining a polyagent model with a plurality of agents, each agent being implemented as an autonomously executing software thread with a changeable internal state and a set of behavioral rules conditioned on that state, including a population of persistent avatar agents that manage a population of short-lived ghost agents; and wherein: each past event is represented by an event avatar that continuously creates and places tracer ghosts at the geographic and temporal location of the event; as part of the hindcasting process, each tracer ghost moves back in time, emulating a single trajectory that perpetrating entity may have taken from an origin location to the event location; and as part of the forecasting process, projector ghosts from any possible event origin emulate the movements of the perpetrating entities from that origin to possible event locations. 17. The method of claim 16, wherein movements emulated by the tracer and projector ghosts adhere to movement constraints and preferences. 18. The method of claim 16, wherein the tracer ghosts begin at the event location and move backwards in space and time. 19. The method of claim 16 wherein, from each respective current spatial location, each tracer ghost moves through space and back in time by picking a new location in its neighborhood that would have had the highest likelihood of having been the origin of that step to the current location. 20. The method of claim 19, wherein the highest likelihood is determined by applying all constraints and preferences from all neighboring locations. 21. The method of claim 16 wherein: as each projector ghost moves through space and forward time, it measures its distance to actual events and contributes to an internal measure of “confidence” amounts that are inversely proportional to that distance; and at the end of its execution, each projector ghost reports back to its avatar its parameter settings and the level of confidence it has accumulated. 22. The method of claim 21, wherein the avatar modifies the probability distributions over valid parameter values such that values that resulted in higher ghost confidence have an increasingly higher likelihood of being selected in the creation of subsequent projector ghosts. 23. The method of claim 16 wherein, if a tracer or projector ghost carries additional event attributes inherited from the event avatar, that ghost may contribute to specialized “event origin” sub-fields that are labeled with these attributes.