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Method and System for Predicting and Assigning Emergency Personnel based on Input Sources

IP.com Disclosure Number: IPCOM000239182D
Publication Date: 2014-Oct-20
Document File: 2 page(s) / 27K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system is disclosed for predicting and assigning emergency personnel based on input sources.

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This is the abbreviated version, containing approximately 51% of the total text.

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Method and System for Predicting and Assigning Emergency Personnel based on Input Sources

Generally, an assignment of police, fire and emergency medical personnel is accomplished in many jurisdictions based on past precedent. The assignment is accomplished with paper-based modeling and may not dynamically respond to changes in the environment. Even when computer predictive models are used, the models are based solely on prior crime rates in the form of hot spots. Such predictive models do not take into account other factors or propose specific staffing actions. Generally, actual patrol routes and staffing decisions are left to humans. Thus, there may be a difficulty in preventing emergency events since predictable root causes are not used for assigning emergency personnel.

Disclosed is a method and system for predicting and assigning emergency personnel based on input sources. The method and system aggregates a variety of readily available sources of information to provide a flexible and predictive solution for emergency personnel assignment.

The method and system defines routes to be taken by patrolling staff/vehicles and positions to place standing staff/vehicles. The method and system also defines precincts, stations or locations which are most likely to be asked to respond to an event or condition. Further, the method and system also defines the number of staff/vehicles/responders required under expected conditions. The defined information can change day by day and perhaps even hour by hour, and staff is directed to respond accordingly. Actual dispatching events are compared to model predictions and staffing details are updated accordingly in real-time.

The method and system accomplishes staffing prediction by combining data from a variety of sources and applying analytics in real time to the combined data. The information can include one or more of, but not limited to, date in month/day in week/time of day, historical crime data, weather forecast, social media monitoring/text analytics, police patrol or beat reports, location of known criminals, location of traceable criminals, police activity in an adjacent area that may cause crime to move/spill over into another area, current population of homeless (in/out of shelters), sporting events, concerts, cultural activity, restaurant, night life, traffic, utility state (brownout/blackout conditions), proximity to known criminal hubs, opted-in sources of mobile data and hospital intakes information. The weather forecast can include moon phase and cloud-cover for luminosity calculations. The location of traceable criminals may be tracked by ankle band monitoring and hardware address monitoring. The opted-in sources of mob...