A system and method for predicting or forecasting rare events, such as instances of violent crime within a spatial region, in combination with other correlative variables, such as weather data. One or more machine learning algorithms is employed in order to predict rare events in conjunction with geospatial information and one or more correlative variables. By employing a combination of a downsampling of the rare event data, followed by the application of an ensemble of machine learning algorithms, increased precision of the predictions of future occurrences of the rare events may be achieved.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for forecasting a likelihood of future occurrence of a rare event within predetermined spatial and temporal units, the method comprising the steps of: storing first historical data of past occurrences of events of the same category as that of the rare event; storing second historical data of at least one predictor variable previously found to correlate to occurrences of the rare event; joining the first historical data and the second historical data into an aggregate database; downsampling the aggregate database to remove at least some imbalances from the aggregate database; applying a machine learning algorithm to the downsampled aggregate database in a training phase of the machine learning algorithm; obtaining predictor value data of the at least one predictor value; and applying the machine learning algorithm to the predictor value data to obtain forecasts of the likelihood of future occurrence of the rare event within the predetermined spatial and temporal units. 2. The method according to claim 1, wherein the rare event comprises occurrences of at least one of crime, calls, and incidents. 3. The method according to claim 1, wherein the machine learning algorithm comprises an ensemble of machine learning algorithms. 4. The method according to claim 1, wherein the machine learning algorithm comprises an ensemble of neural networks. 5. The method according to claim 1, wherein the predictor variable is selected from the group comprising temperature, precipitation, relative humidity, and wind speed. 6. The method according to claim 1, wherein the spatial unit is a census tract. 7. The method according to claim 1, wherein the temporal unit is an hour. 8. The method according to claim 1, wherein the step of downsampling comprises the steps of: dividing the aggregate database into a positive event dataset and a negative event dataset; creating a quantity n samples of the negative event dataset each by randomly removing, with replacement for each new n sample, entries from the negative event dataset; and joining all of the positive events to each of the n samples of the negative event dataset. 9. A rare event forecasting system for forecasting a likelihood of future occurrence of a rare event within predetermined spatial and temporal units, comprising: a database storing first historical data of past occurrences of events of the same category as that of the rare event and storing second historical data of at least one predictor variable previously found to correlate to occurrences of the rare event; and an analysis unit that: joins the first historical data and the second historical data into an aggregate database; downsamples the aggregate database to remove at least some imbalances from the aggregate database; applies a machine learning algorithm to the downsampled aggregate database in a training phase of the machine learning algorithm; obtains predictor value data of the at least one predictor value; and applies the machine learning algorithm to the predictor value data to obtain forecasts of the likelihood of future occurrence of the rare event within the predetermined spatial and temporal units. 10. The system according to claim 9, wherein the rare event comprises occurrences of at least one of crime, calls and incidents. 11. The system according to claim 9, wherein the machine learning algorithm comprises an ensemble of machine learning algorithms. 12. The system according to claim 9, wherein the machine learning algorithm comprises an ensemble of neural networks. 13. The system according to claim 9, wherein the predictor variable is selected from the group comprising forecasted temperature, precipitation, relative humidity, and wind speed. 14. The system according to claim 9, wherein the spatial unit is a census tract. 15. The system according to claim 9, wherein the temporal unit is an hour. 16. The system according to claim 9, wherein, in downsampling the aggregate database to remove at least some imbalances from the aggregate database, the analysis unit: divides the aggregate database into a positive event dataset and a negative event dataset; creates a quantity n samples of the negative event dataset each by randomly removing, with replacement for each new n sample, entries from the negative event dataset; and joins all of the positive events to each of the n samples of the negative event dataset.