Putting data on the beat—Public safety intelligence led policing
While most law enforcement agencies operate in a fog of big data, it doesn’t have to be this way. There are approaches to improving data integration and analytics that substantially enhance policing. Broadly these initiatives fall into three categories:
Knowing before:
Hindsight, as they say, is 20/20. But a retrospective view only gets you so far in terms of applicable intelligence. Machine learning can be your force multiplier—because it offers the possibility of actual foresight in real-time situations. Using predictive, cloud-based analytics, it is possible to identify subtle patterns in data streams that lead to advanced awareness of crimes about to be committed or emergencies about to occur. In this way, the sort of intuition that a seasoned police officer has can be extended to provide an always-on view. For example, individual activities that seem innocuous might collectively trigger suspicion or flag an increased risk when aggregated and analyzed by machine learning algorithms—such as shifts in travel or purchase patterns, or social media activity.
Knowing in the moment:
No doubt every first responder wishes they had an omniscient 360-degree view of the scene they are encountering. Well, today sensors coupled with real-time data ingestion and analysis (performed in a secure cloud environment) can greatly enhance this situational intelligence, coupled with geo-spatial information allows first responders to correlate and execute an appropriate response. The relevant technologies include:
- Connected devices: Synchronized feeds from CAD; RMS; body cameras and in-vehicle camera systems; CCTV; chemical, biological, radioactive, nuclear sensors (CBRN); (ALPR); acoustic listening devices; and open-source intelligence (OSINT) all help to capture a detailed picture of the event.
- Geo-spatial awareness: Event information, as well as objects of potential interest nearby, is mapped, providing an enhanced view of the environment. For example, additional sensors are monitored and nearby schools and businesses identified, along with egress routes, traffic patterns, and hospitals.
- Other relevant information and histories: By using address-specific identity and licensing data, past calls for service, and other active calls in the area, pertinent information about the residence (such as any weapons or chemicals on the premises) can be instantly surfaced. In the event of fire, chemical, or environmental disasters weather information can be overlaid to help predict at-risk areas.
Knowing after:
As any seasoned detective can attest, reconstructing events afterwards can be a time-consuming process, with the potential to miss key evidence. Highly integrated data systems and machine learning can significantly reduce the man-hours required to uncover evidence buried across disparate data pools.
The promise of technology—what’s next?
Learn more about the future of digital policing and law enforcement in the twenty-first century.
