Disclosed herein is a visual analytics system and method that provides a proactive and predictive environment in order to assist decision makers in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In the disclosed approach, a suite of natural scale templates and methods are provided allowing users to focus and drill down to appropriate geospatial and temporal resolution levels. The disclosed forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method applied in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. A novel kernel density estimation technique is also disclosed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving an input, the input comprising a geospatial natural scale template and a temporal natural scale template; subdividing the geospatial natural scale template based on a defined criteria to produce a plurality of geospatial sub-divisions; generating a time series historical signal for each of the plurality of sub-divisions within the geospatial natural scale template, the time series signal defined by the temporal natural scale template and based on historical activity data; generating a set of forecast results from each of the time series historical signals; and providing the set of forecast results as visual output on an electronic display to a user. 2. The method of claim 1, wherein the visual output comprises a graphical representation of the geospatial subdivisions and an indicator of the forecast result for each of the geospatial subdivisions, the indicator displayed within a corresponding geospatial subdivision. 3. The method of claim 2, the indicator comprising a color, the color dependent on the forecast result. 4. The method of claim 2, wherein the visual output comprises a choropleth map. 5. The method of claim 2, wherein the visual output comprises a heat map. 6. The method of claim 1, wherein the geospatial natural scale template defines a geospatial region having an incident activity level above a first threshold. 7. The method of claim 1, wherein said incident activity level corresponds to crime incidents. 8. The method of claim 1, wherein said incident activity level corresponds to health care need incidents. 9. The method of claim 1, wherein the temporal natural scale template defines a time period having an incident activity level above a first threshold. 10. The method of claim 1, wherein the time series signal comprises historical event incidence vs. time step signals. 11. The method of claim 1, wherein the time series signal comprises kernel value vs. time step signals. 12. The method of claim 1, wherein said generating a set of forecast results is determined based on seasonal trend decomposition using loess. 13. The method of claim 1, wherein said defined criteria are law enforcement jurisdictions. 14. The method of claim 1, further comprising: providing visual feedback to the user on a display to indicate if the historical activity data is insufficient to produce forecast results having an accuracy above an accuracy threshold. 15. The method of claim 1, wherein the geospatial natural scale template or temporal natural scale template is based on a predetermined confidence interval. 16. A system, comprising: a computer processor; a memory; an input device; and an electronic display wherein the computer processor is configured to: receive an input, the input comprising a geospatial natural scale template and a temporal natural scale template; subdivide the geospatial natural scale template based on a defined criteria to produce a plurality of geospatial sub-divisions; generate a time series historical signal for each of the plurality of sub-divisions within the geospatial natural scale template, the time series signal defined by the temporal natural scale template; generate a set of forecast results from each of the time series historical signals; and provide the set of forecast results as visual output on the electronic display to a user. 17. The system of claim 16, wherein the visual output comprises a graphical representation of the geospatial subdivisions and an indicator of the forecast result for each of the geospatial subdivisions, the indicator displayed within a corresponding geospatial subdivision. 18. The system of claim 17, the indicator comprising a color, the color dependent on the forecast result. 19. The system of claim 17, wherein the visual output comprises a choropleth map. 20. The system of claim 17, wherein the visual output comprises a heat map. 21. The system of claim 16, wherein the geospatial natural scale template defines a geospatial region having an incident activity level above a first threshold. 22. The system of claim 16, wherein said incident activity level corresponds to crime incidents. 23. The system of claim 16, wherein said incident activity level corresponds to health care need incidents. 24. The system of claim 16, wherein the temporal natural scale template defines a time period having an incident activity level above a first threshold. 25. The system of claim 16, wherein the time series signal comprises historical event incidence vs. time step signals. 26. The system of claim 16, wherein the time series signal comprises kernel value vs. time step signals. 27. The system of claim 16, wherein said defined criteria are law enforcement jurisdictions. 28. The system of claim 16, wherein the computer processor is configured to: provide visual feedback to the user using the display to indicate if the historical activity data is insufficient to produce forecast results having an accuracy above an accuracy threshold. 29. The system of claim 16, wherein the geospatial natural scale template or temporal natural scale template is based on a predetermined confidence interval.