Systems and methods for autonomous first response routing

A device, comprising:

• one or more processors configured to:
• receive data associated with a geographical area,
• wherein the data includes emergency data, traffic data, network performance data, crime data, and gunshot data;
• determine a location sizing parameter that defines a size of a location within the geographical area as a quantity of addresses within the geographical area;
• determine whether a risk classification timer is satisfied;
• identify, based on the location sizing parameter, the risk classification timer being satisfied, the data, and using a risk classifier model, the location;
• determine, based on applying weights to incidents identified by the data, and using the risk classifier model, a risk level for the location,
• wherein the weights indicate that a weight of a criminal incident, identified by the crime data, is greater than a weight of a network performance incident, identified by the network performance data;
• identify, based on the risk level, the traffic data, and the network performance data, and using a deployable location model, an autonomous vehicle to deploy to the location;
• determine, based on the traffic data and the network performance data, and using the deployable location model, a route for the autonomous vehicle to travel to the location;
• and
• perform one or more actions based on the autonomous vehicle and the route.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive data associated with a geographical area,
• wherein the data includes emergency data, traffic data, network performance data, crime data, and gunshot data;
• determine a location sizing parameter that defines a size of a location within the geographical area as a quantity of addresses within the geographical area,
• wherein the location is one of a plurality of locations within the geographical area that has the quantity of addresses;
• identify, based on the data and the location sizing parameter, and using a risk classifier model, the plurality of locations;
• determine, based on applying weights to incidents identified by the data, and using the risk classifier model, a plurality of risk levels corresponding to the plurality of locations,
• wherein the weights indicate that a weight of a criminal incident, identified by the crime data, is greater than a weight of a network performance incident, identified by the network performance data;
• identify, based on the traffic data and the network performance data, and using a deployable location model, a plurality of autonomous vehicles to deploy to the plurality of locations;
• determine, based on the plurality of risk levels, the traffic data, and the network performance data, and using the deployable location model, a plurality of routes for the plurality of autonomous vehicles to travel to the plurality of locations;
• and
• perform one or more actions based on the plurality of autonomous vehicles and the plurality of routes.