A real-life case study – How Chicago Police Department reduced response time by 39%


In the United States, the Chicago Police Department sought to enhance operations in six police districts. The goal was to reduce response time for reported shootings and crime in these areas. At the time, operators were working with many disparate systems including a computer-assisted dispatch (CAD), 911 calls from residents, gunshot detection sensors, and other solutions. The department needed a platform that could combine these technologies to help them make faster, more informed and effective decisions.


Predictive versus Proactive How collaboration is the key to safer cities


‘Predictive policing’ is the new catchphrase


C


entral to this notion is that police departments or other crime fighting


units can detect and stop crime in a city before it happens. It suggests that a single solution can tell crime analysts and officers where and when a specific crime will take place, and sometimes who will commit it, based on data-crunching algorithms.


But is it realistic? In short, no. While there have been many advancements in security technology, this level of predictive policing is still far from actualisation. Technology isn’t a magic bullet that can stop crime before it happens; and it certainly cannot replace the knowledge, instinct and experience of frontline personnel.


That said, some intelligent solutions can enhance these strengths by delivering actionable data and enhancing inter-agency collaboration. This technology gives city agencies a better understanding of their environment, allowing them to shave valuable seconds off response times and determine when, where and how to best deploy their resources. That means, whether it’s securing a big city event or handling an unexpected situation, response teams are empowered throughout the entire mission.


How collaboration helps cities work smarter


The reality is that cities are still dealing with a fundamental challenge – siloed infrastructure and systems. Most frontline workers such as police officers, firefighters, call centre operators, and emergency response personnel are using older, disparate technologies. These city agencies lack the ability to effectively share information with each other. Every day, operators are also struggling to make sense of


4 © CI TY S ECURI TY MAGAZ INE – S PRING 2018


all the data that they receive from various sensors and solutions.


Forward-thinking cities understand that bringing multiple data sources together into a single pane of glass is the key to enhancing collaboration, improving response time to incidents, and ultimately, keeping cities safer. It’s why more cities are looking at technologies that unify information into a shared view. More than that, they want solutions that empower public safety departments to build a deeper, data-driven understanding of what’s happening every day.


These unified, decision-support systems keep all agencies on the same page. So, whether it’s securing a major stadium event or responding to a possible coordinated attack, everyone from call centre operators to paramedics and police officers relies on a shared view to access timely information. Together, they are then able to make better decisions and coordinate faster response. It is important to clarify – these decision- support systems will not predict where crime could happen next week. However, these systems can analyse historical data and give city agencies insights that they can act on.


This might include understanding that when a wall is tagged with graffiti in a certain area, the chances of a shooting goes up. Or, perhaps learning that car theft increases a few days prior to an annual public event in the surrounding area.


With this knowledge, public safety teams can allocate resources to minimise these threats. Following critical events, chiefs, commanders or managers can also review responses to either improve strategies or better train personnel. Ultimately, the focus for city agencies shifts from predictive analysis to tactical thinking.


One security solutions provider took on the challenge and created a decision-support system that correlates and consolidates data from many sensors and provides a dynamic, unified view to all dispatchers and responders, so they can make insight-driven decisions during any situation.


According to Jonathan Lewin, Chief of Technical Services for the Chicago Police Department, City of Chicago, “Response times, from dispatch to 'on-scene' arrival time, have been reduced by 39% and 24% respectively, in our two most at-risk districts. Shootings are down 22% in these districts compared to 2016, and the technology and process will be expanding to more districts. The system is now deployed in our Strategic Decision Support Centres, located in each corresponding police district facilities, and has helped us send a strong message to the community that crime and gun violence get a swift response.”


With a unified view and actionable alerts, city agencies can work together to effectively secure an event or protect any day. And, while these initiatives have been launched in the United States, the United Kingdom and mainland Europe are showing increased interest in and adoption of more collaborative technologies. This is mainly because these solutions offer the greatest potential to secure cities where higher population densities often mean heightened threat levels.


Preparing for the future of policing


Right now, predictive policing is a popular buzz word. What cities actually require are solutions that can effectively inform agencies, with actionable data. These teams need correlated data that can help them make fast and effective decisions when seconds count. They also need intelligence that can help them assess and determine the effectiveness of their procedures or campaigns. Pro-active response and planning will ultimately help cities stay one step ahead of threats and emergencies.


Giovanni Gaccione Justice & Public Safety Practice Leader. Genetec


www.genetec.com www. c i t y s e cur i t yma ga z in e . com


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