February 27, 2020 - People have a hard time agreeing on things these days, but one thing people all agree on is that safer cities are better cities. It’s no surprise then that public safety spending has been increasing over the past few decades. Today it is a major budget item for local governments – accounting for more than 30-40% of local operating budgets in many of the US’s largest cities. These trends raise many questions including, “Should cities continue to increase public safety budgets or is there a better way?” Artificial intelligence (AI) and machine learning (ML) could be that better way.
Is a trade-off between public safety spending and development programs necessary?
The daily news is filled with an endless stream of examples of gun violence, opioid overdoses, domestic terrorism, hate crimes, and school shootings. There seems to be more threats than ever and the burden falls to the public safety teams to address the issues. To combat those threats, cities and states have expanded their police forces and invested in weapons, surveillance technology, and jails.
The results seem to indicate that that spending has been effective. As crime spending has increased over the past 30 years, crime rates have dropped. Not everyone agrees, however, that the drop is attributable to the increased police spending. In fact, some cities, including Chicago, have seen a sharp increase in crime rates even as spending as increased. Some argue that the downside of increased police spending is that it takes money away from economic development and social programs that could more directly address the source of criminal activity. Is there a way to continue these economic development and social programs while still continuing to make police forces more efficient?
AI, ML and standards - based smart city platforms meet the challenge
One possible solution is AI and ML. No longer science fiction, AI and ML technologies are now commonplace and are projected to dramatically change the people/technology equation in virtually every industry. When it comes to public safety, these technologies are present in video and sound analytics that today help cities enforce traffic rules and get alerted to threats as they happen and even before they happen. These technologies can literally become the eyes and ears of a police force, enabling cities and police departments to scale up their operations and provide greater protection to the public without increasing their public safety teams.
As with other technologies, over time the price of these technologies should decrease while their effectiveness increases. AI and ML become even more cost-effective when paired with standards-based computing platforms, such as CIMCON’s NearSky platform, which eliminates the need for costly, specialty hardware and allows cities to deploy more than one solution from a single platform. Using NearSky to run AI/ML algorithms, therefore, can lower the cost of deploying these technologies, allowing cities to put that money back into economic development and social programs that can address crime at its source.
For a demonstration of the video and sound analytics solutions available with NearSky, check out these webinars:
- Public Safety and NearSky: Objects Left/Taken, Wrong Way Drivers
- Gunshot and Noise Detection with NearSky
The reasoning behind cities turning to technology is simple. It’s impossible to have eyes everywhere and, even if a city could deploy cameras everywhere, how would a city hire and pay enough people to continually monitor all that goes on in the city? AI, ML, and standards-based smart city platforms can sift through all the data and can either address the issue automatically, such as sending a fine in the mail to a perpetrator, or by alerting the appropriate person in the city when manual action needs to be taken.
It’s important to highlight that AI/ML solutions do not have to impact people’s privacy. Certainly some public safety solutions do look at personal information such as a license plate number, but not all do. NearSky, for example, can process all video and sound information at the edge and send back only counts or alerts of violations without sending private information, such as full-length videos or sound recordings, to the cloud. A police team could simply be alerted that an offense is taking place and then dispatch officers to respond to the incident.
If you want to learn more about this type of technologies, take a look into the white paper Technical Overview of the NearSky Smart City Platform