Forecasting acts using machine learning and data linkages

1 . A method, comprising:

filtering, by one or more processors coupled with memory, employment data and act data for a plurality of geographic locations, wherein the employment data comprises at least one of a type of employment, a type of industry, or an employment rate;

identifying, by the one or more processors using a machine learning model trained on a historic employment data for the plurality of geographic locations and historic act data for the plurality of geographic locations, a relationship between the employment data and the act data;

predicting, by the one or more processors based on the relationship between the employment data and the act data identified by the machine learning model, trends in acts for the plurality of geographic locations;

determining, by the one or more processors, a preventive measure to apply in a first geographic location of the plurality of geographic locations based on an effectiveness score for the preventive measure identified using the machine learning model trained with prior predicted trends in the plurality of geographic locations after application of one or more preventive measures;

generating, by the one or more processors, a request for the preventive measure in the first geographic location of the plurality of geographic locations; and

transmitting, by the one or more processors, an instruction to a security system, located at the first geographic location and remote from the one or more processors, to actuate a subsystem of the security system in accordance with the request for the preventive measure generated based on the predicted trends and the relationship between the employment data and the act data.

2 . The method of claim 1 , wherein the employment data comprises at least one of income of employees, unemployment rate, head count for industry type, gender, and age of employees within the plurality of locations.

3 . The method of claim 1 , further comprising:

generating, by the one or more processors, trending crime data from the employment data and act data; and

injecting the trending crime data into the machine learning model to refine the machine learning model.

4 . The method of claim 1 , further comprising providing a weight for the employment data based on an impact the employment data has on acts.

5 . The method of claim 1 , comprising:

determining, by the one or more processors, that a frequency of an attribute of the act data exceeds a threshold; and

predicting, by the one or more processors, the trends in acts for the plurality of locations responsive to the frequency exceeding the threshold.

6 . The method of claim 1 , further comprising generating, by the one or more processors, a remediation solution based on the trends.

7 . The method of claim 1 , further comprising reinjecting, by the one or more processors, the trends into the machine learning model to refine the machine learning model for future trends in crime for the plurality of locations.

8 . The method of claim 1 , further comprising generating, by the one or more processors, a score for the first geographic location of the plurality of geographic locations.

9 . The method of claim 1 , comprising:

identifying, by the one or more processors, a query for a trend related to acts; and

predicting, by the one or more processors, the trends in acts for the plurality of locations responsive to identifying the query.

10 . The method of claim 1 , wherein the request includes a generated remedial action based on the predicted trends.

11 . The method of claim 1 , wherein transmitting the instruction to actuate the subsystem of the security system causes the subsystem to at least one of:

lock a door at the location,

turn on a security camera at the location,

turn on a light source at the location,

turn on a security camera at the location, or

enable a motion or sound sensor at the location.

12 . A system comprising:

one or more processors coupled with memory, the one or more processors to:

filter employment data and act data for a plurality of geographic locations, wherein the employment data comprises at least one of type of employment, type of industry, or employment rate;

identify, using a machine learning model trained on a historic employment data for the plurality of geographic locations and historic act data for the plurality of geographic locations, a relationship between the employment data and the act data;

predict, based on the relationship between the employment data and the act data identified by the machine learning model, trends in acts for the plurality of geographic locations;

determine a preventive measure to apply in a first geographic location of the plurality of geographic locations based on an effectiveness score for the preventive measure identified using the machine learning model trained with prior predicted trends in the plurality of geographic locations after application of one or more preventive measures;

generate a request for the preventive measure in the first geographic location of the plurality of geographic locations; and

transmit an instruction to a security system, located at the first geographic location and remote from the one or more processors, to actuate a subsystem of the security system in accordance with the request for the preventive measure generated based on the predicted trends and the relationship between the employment data and the act data.

13 . The system of claim 12 , wherein the one or more processors are further configured to:

generate trending crime data from the employment data and act data; and

inject the trending crime data into the machine learning model to refine the machine learning model.

14 . The system of claim 12 , wherein the one or more processors are configured to provide a weight for the employment data based on an impact the employment data has on acts.

15 . The system of claim 12 , wherein the one or more processors are configured to:

determine that a frequency of an attribute of the act data exceeds a threshold; and

predict the trends in acts for the plurality of locations responsive to the frequency exceeding the threshold.

16 . The system of claim 12 , wherein the one or more processors are configured to generate a remediation solution based on the trends.

17 . The system of claim 12 , wherein the one or more processors are configured to reinject the trends into the machine learning model to refine the machine learning model for future trends in crime for the plurality of locations.

18 . The system of claim 12 , wherein the one or more processors are configured to generate a score for a first location of the plurality of locations.

19 . A non-transitory computer-readable medium, comprising instructions embodied thereon that when executed cause one or more processors to:

filter employment data and act data for a plurality of geographic locations, wherein the employment data comprises at least one of type of employment, type of industry, or employment rate;

identify, using a machine learning model trained on a historic employment data for the plurality of geographic locations and historic act data for the plurality of geographic locations, a relationship between the employment data and the act data;

predict, based on the relationship between the employment data and the act data identified by the machine learning model, trends in acts for the plurality of geographic locations;

determine a preventive measure to apply in a first geographic location of the plurality of geographic locations based on an effectiveness score for the preventive measure identified using the machine learning model trained with prior predicted trends in the plurality of geographic locations after application of one or more preventive measures;

generate a request for the preventive measure in the first geographic location of the plurality of geographic locations; and

transmit an instruction to a security system, located at the first geographic location and remote from the one or more processors, to actuate a subsystem of the security system in accordance with the request for the preventive measure generated based on the predicted trends and the relationship between the employment data and the act data.

20 . The non-transitory computer-readable medium of claim 19 , wherein the instructions cause the one or more processors to:

determine that a frequency of an attribute of the act data exceeds a threshold; and

predict the trends in acts for the plurality of locations responsive to the frequency exceeding the threshold.