AGT Blog

AGT’s StreetSMART Solution Provides Specialized Analytics and Real-Time Intelligence to Law Enforcement Officers to Improve Officer Safety and Efficiency and to Help Reduce Crime

admin | October 15, 2014

As cities continue to experience rapid growth, reducing crime and improving public safety are becoming an increasingly important priority. Safe cities are key to building vibrant communities with enhanced quality of life, fostering economic growth and driving innovation—and a large contributor to public safety is effective law enforcement. However, public safety agencies are facing challenges with shrinking budgets and personnel cuts, and require technological innovation to do more with fewer resources.

AGT_Web_StSmart_OfficerCloud

Information-sharing between patrol officers is often limited, and usually occurs at the end of a patrol officer’s day or shift instead of real-time. A recent U.S. study revealed patrol officers spend up to 25 percent of their shift time writing and filing reports, resulting in less time in the field and delaying access to information that could be critical.

Real-time, localized intelligence aggregated through data analytics ensures patrol officers automatically have access to the relevant information they need when they need it, helping them be their most effective at serving the public. Leveraging the Internet of Things (IoT), our StreetSMART solution delivers specialized analytics, data sharing and visualization capabilities that uncover insights for patrol officers to help fill this information gap, without the need for complex back-end system integration. StreetSMART in-field and cloud data analytics automatically identify and provide relevant information to the officers, thus improving efficiency by automating processes and creating a clearer, more intelligent situational picture.

StreetSMART combines wearable technologies, cloud-based analytics and applications to increase officer efficiency and effectiveness, as well as improve officer safety. The mobile app gathers and manages relevant data from wearable sensors, such as smart watches and wearable video cameras, and social media, while the supervisor app works with existing operations centers to relay information.

Leveraging the following capabilities, StreetSMART turns big IoT data into relevant insights to provide a localized, real-time intelligence environment:

  • AGT’s analytics identifies relevant intelligence extracted from StreetSMART cloud, social media and wearable sensors and push relevant intelligence to officers based on field situation (e.g. person, location, event type, etc.)
  • AGT’s specialized analytics and visualization capabilities provide automated, in-field multimedia reporting supplemented by text, images, video and audio to speed up reporting, improve the quality of evidence and increase conviction rate for guilty persons. This also reduces back office paperwork, providing cost savings and allowing the officers to maximize their time in visible policing in the streets.
  • StreetSMART’s supervisor application automatically monitors bio data from patrol officers based on wearable sensors to identify distress situations and provide early support, enhancing officer safety.
  • Using data provided by the wearable sensors, StreetSMART analyzes officers’ field activities, providing feedback to the officers and their supervisors to help ensure they are stationed where they are most needed, and that their actions are efficiently targeted.

AGT_Web_StSmart_FieldAnalytics

 

AGT’s StreetSMART solution unlocks the value of IoT by using IoT-specific analytics to create new information and monitor complex environments, increasing patrol officers’ preparedness and insight into potentially dangerous situations. Integrating wearable sensor data management, advanced analytics and visualization into existing police backbone systems improves the effectiveness of law enforcement by facilitated real-time intelligence and information-sharing, and helps make cities safer through crime reduction.

Share this:
Categories: , , , , | Tags: ,