Predictive analytics for emergency detection and response management
Disclosed are systems, methods, and media capable of generating emergency predictions. The systems, methods, and media generate spatiotemporal emergency communication predictions, carry out data augmentation, detect emergency anomalies, optimize emergency resource allocation, or any combination thereof.
1. A method comprising: obtaining, by an emergency prediction system (EPS), emergency data comprising emergency type, emergency location, and emergency time for a plurality of emergency communications; augmenting, by the emergency prediction system, the emergency data with additional data associated with the plurality of emergency communications based on at least one of emergency time, emergency location, and calling identity; generating at least one spatiotemporal emergency prediction based on the emergency data; and providing the at least one spatiotemporal emergency prediction and a recommended allocation of emergency resources based on the at least one spatiotemporal emergency prediction to an emergency dispatch center serving a defined geographic area to display on an emergency dispatch center display.
2. The method of claim 1 , wherein augmenting the emergency data with additional data comprises: matching at least a subset of the additional data with at least a subset of the emergency data; and combining information from the at least a subset of the additional data and the at least a subset of the emergency data to form augmented data.
3. The method of claim 1 , wherein providing the at least one spatiotemporal emergency prediction comprises: displaying a set of predicted kernel density estimates on a digital map.
4. The method of claim 1 , wherein generating the at least one spatiotemporal emergency prediction comprises: generating a plurality of subset spatiotemporal emergency predictions for subsets of the defined geographic area; and aggregating the plurality of subset spatiotemporal emergency predictions to obtain the at least one spatiotemporal emergency prediction.
5. The method of claim 1 , further comprising: generating, by the emergency prediction system, at least one prediction model; and generating the spatiotemporal emergency prediction using the at least one prediction model.
6. The method of claim 5 , wherein generating the at least one prediction model comprises: generating a prediction model using a point cloud comprising points sampled from at least one of emergency data, proprietary emergency data, and a proprietary data stream.
7. An emergency prediction system (EPS) comprising: a network component, operative to receive emergency data; and a processor, operatively coupled to the network component, the processor operative to: obtain emergency data comprising emergency type, emergency location, and emergency time for a plurality of emergency communications; augment the emergency data with additional data associated with the plurality of emergency communications based on at least one of emergency time, emergency location, and calling identity; generate at least one spatiotemporal emergency prediction corresponding to a defined emergency type, a defined geographic area, and a defined time period; and provide the at least spatiotemporal emergency prediction and a recommended allocation of emergency resources based on the spatiotemporal emergency prediction to an emergency dispatch center serving the defined geographic area to display on an emergency dispatch center display.
8. The emergency prediction system of claim 7 , wherein the processor is further operative to: generate at least one prediction model using a point cloud comprising points sampled from at least one of emergency data, proprietary emergency data, and a proprietary data stream.
9. The emergency prediction system of claim 7 , wherein the processor is further operative to: augment the emergency data with the additional data by matching at least a subset of the additional data with at least a subset of the emergency data, and combining information from the at least a subset of the additional data and the at least a subset of the emergency data to form augmented data.
10. The emergency prediction system of claim 7 , wherein the processor is further operative to: execute an allocation algorithm, operative to generate the recommended allocation of emergency resources based on the at least one spatiotemporal emergency prediction.
11. A method, comprising: obtaining, by an emergency prediction system, emergency data for emergency communications comprising emergency time and emergency location; monitoring the emergency communications to identify a cluster of emergency communications; identifying an emergency anomaly based on the cluster of emergency communications; and providing, by the emergency prediction system, the cluster of emergency communications and the emergency anomaly based on the cluster of emergency communications to an emergency dispatch center serving a defined geographic area to display on an emergency dispatch center display.
12. The method of claim 11 , further comprising: predicting an emergency type and an emergency priority for the cluster of emergency communications using a multi-class classifier.
13. The method of claim 11 , wherein obtaining the emergency data comprises: obtaining labeled incident data, unlabeled incident data, and augmented incident data.
14. The method of claim 11 , further comprising: identifying the emergency anomaly substantially in real-time.
15. The method of claim 11 , wherein identifying the emergency anomaly comprises: identifying the emergency anomaly using a cluster detection model.
16. The method of claim 11 , wherein monitoring the emergency communications to identify a cluster of emergency communications, comprises: monitoring clusters of emergency communications that correspond to the defined geographic area, a defined time period, and a defined emergency type.