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Smart Public Safety: The Evolution of Community Oriented Policing to a Data Driven Police Force

Have you realized that smart is the new buzzword? If we put smart in front of it, it must be amazing. Realistically, it is just a single word that encompasses the use of both sensor and sensor data to be used to make life easier or to provide better information than before. Ergo, smart says all of that in one word.

Smart public safety is the adoption of those sensors and sensor data to create field intelligence and situational awareness, while at the same time providing historical trends that can be used for proactive and predictive policing as technologies continue to innovate.  

Smart public safety is typically a first step in a city’s migration to a smart city. It is the evolution of Community-Oriented Policing—a philosophy that promotes organizational strategies that support the systematic use of partnerships and problem-solving techniques to proactively address the immediate conditions that give rise to public safety issues such as crime, social disorder, and fear of crime.

Community-Oriented Policing has been around since 1994, and has been adopted by public safety departments across the United States and the world. Community-Oriented Policing was—and is—a police reform act that was needed at a time when police departments needed a kinder, gentler approach to the community.

But Community-Oriented Policing created a problem where police departments not only investigated crimes, but also became social workers, teachers, and BBQ grill masters. Research from George Mason University’s Center for Evidence-Based Crime Policy finds that Community-Oriented Policing has been an effective way to increase “citizen satisfaction and enhance the legitimacy of the police” but has not shown a clear connection to reduced crime rates.

Smart public safety includes the partnerships built by Community-Oriented Policing and requires them to go a step further. It incorporates the Enterprise of Things (EoT), a space where Internet of Things (IoT) sensors work both independently and cohesively to bring digital transformation and data driven policing to the forefront. Since smart public safety involves the convergence of information technology (IT), IoT, and operational technology (OT), these technologies must have a defined cybersecurity posture.



Smart public safety is possible today, and yet it is still in its toddler years with a very strong growth. Some cities have already begun to invest in data sensors that can be used in the Smart Public Safety application.

Technology Investment

Today, the most common technologies are body worn cameras, citywide surveillance systems, and digital evidence management platforms. A 2018 Bureau of Justice Statistics (BJS) report, which analyzed police department body-worn camera usage from 2016, found that 47 percent of general-purpose law enforcement agencies had purchased body-worn cameras. Eighty percent of large police departments had also purchased body-worn cameras.

Many of these departments purchased the cameras to improve officer safety, increase evidence quality, and reduce civilian complaints and agency liability, according to the BJS. But “research does not necessarily support the effectiveness of body-worn cameras in achieving those desired outcomes,” the bureau found. “A comprehensive review of 70 studies of body-worn cameras use found that the larger body of research on body-worn cameras showed no consistent or no statistically significant effects.”

Smart public safety is not another sensor or camera, however, but rather an amalgamation of sensors and platforms, policies, partnerships, and the adoption of these concepts to benefit the city while increasing the livability of the city. Successful smart public safety initiatives will require partners, both public and private. They will also require data customization, cybersecurity protections, and strong policies to be effective.  

Partnerships

Smart public safety is built upon partnerships, both public-public and public-private partnerships (P3s). These partnerships are typically made up of local community groups, religious non-profits, and hospitality associations.

Cities cannot pull off a fully functional smart city without help. Smart public safety relies on input and collaboration between all partners to effectively begin to reduce crime and increase the livability of the city. Smart public safety relies upon partnerships to share data and resources, including financial, real estate, and technology.



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Smart public safety is not a better camera or more police officers but creates a roadmap for shared data and resources to reduce crime and provide business intelligence to stakeholders. This may encourage the adoption of a fusion center instead of a real-time crime center, which many communities become partners in to share threat information and monitor risk in real time.

For either public-public or public-private partnerships to fully work, a top-level champion for smart public safety must help drive the adoption and implementation. Failure to gain this champion may result in an arduous uphill battle to gain internal and external stakeholders’ support to adopt this new concept.

The champion could be the mayor, city manager, or even an outspoken city council member who has pull with multiple city business units. The champion must be someone that has enough pull to impose action on behalf of public and private partner stakeholders.

Ecosystem of Data

Cities are seeing the rapid expansion of innovation and data availability. This is creating an Enterprise of Things (EoT) ecosystem where public safety and non-public safety IoT work together. An example of this is the adoption of citywide surveillance (public safety IoT) and parking sensors (non-public safety IoT) for both revenue generation and parking enforcement.

These changes will also pose ramifications and opportunities for the way police work is conducted. For instance, Police Chief Magazine predicts that the policing of the future will be carried out by connected officers. The concept of the connected officer is an EoT component where body-worn and police cruiser technologies become an extension of the network edge. This extension will be capable of collecting data, providing real-time data analysis, and producing proactive or predictive data as outputs to help save the lives of citizens and officers.



Police departments will also look to increasingly leverage cloud systems to reduce cost and offer a pathway to connect to partner provide data. An important note here is that any data that can be used as evidence must adhere to Criminal Justice Information Systems (CJIS) compliance to ensure that evidence is not compromised. More public and private cloud systems are becoming CJIS compliant, which means more departments will adopt and use them.

Additionally, Data-Driven Policing—which incorporates machine learning and deep learning algorithms to implement predictive analytics—will also be used. These analytics can help cities analyze crime data, identify actionable patterns, and determine where to deploy officers. IoT sensor data along will also be incorporated with historical data to provide holistic situational awareness to increase the efficiency and effectiveness of police officers. This data may come from camera sensors, along with environmental sensors based on weather patterns, time of day, and more. For instance, a system like this might detect that there is heavy fog incoming which could result in more traffic crashes. Police officers and emergency personnel could be put on alert to be ready to respond more quickly to incidents based on that information.

Still, there is an argument that predictive analytics today do not work well. That is not entirely wrong. Predictive analytics rely on the data ingested; either directly at the sensor, or indirectly through outside inputs. Bad, suggestive, or limited data in will create subjective outputs. As Will Douglas Heaven writes in MIT Technology Review, today’s predictive analytics tools are typically either location-based or demographic based. Data based on just this information will always be skewed, and it can be used to profile and at times discriminate against individuals, neighborhoods, or whole demographic segments.

For instance, Heaven explains that while a predictive policing algorithm did not use race as a predicator, “other variables, such as socioeconomic background, education, and zip code, act as proxies,” which can lead to discriminatory outcomes.

Discrimination and the creation of bad data have forced camera analytics companies to adopt the term “Ethical AI.” It has been used both to define how the artificial intelligence inference has been trained, and it has been used to define the rules engine behind how the analytic can be used.  Prior to implementation, technologies should always be vetted to ensure they do not inherently bring in bias. Data-Driven Policing still requires the adoption of processes and must be used in a non-subjugative approach.

Data-Driven Policing will not use just camera sensors, analytics, or threat modeling based on prior arrest records; it will also use data from a multitude of purpose-built sensors deployed throughout the city to provide information to make police departments more efficient and cities safer. It will involve partnerships with the public; and it should require some level of oversight. 

The rollout of 5G and increased broadband capabilities will allow these types of systems and smart public safety to move forward. As 5G technologies, new low-band IoT communications (such as LoRaWAN), and the integration of these with traditional communications technologies such as fiber expand, a city covered with data sensors providing real-time actionable data will quickly become reality.

Cybersecurity Required

The EoT is not infallible. In fact, it is quite the opposite. Each device that is added to the network to implement a smart public safety program adds another possible vulnerability and access point for malicious actors. A smart public safety initiative cannot be successful without first having a strong cybersecurity posture—especially as ransomware actors continue to target municipalities and the systems they rely on to operate.

A strong cybersecurity posture is going to be different for every city. Where many cities struggle is that there is typically a centralized IT department, but then each business unit may have its own IT department; creating an adverse process. A cybersecurity risk assessment should be performed to determine the attack surface.  Both internal and external resources should be leveraged by cities to build a holistic cybersecurity posture that’s evaluated at least yearly.

One of the most prominent cybersecurity frameworks adopted by cities has been the NIST Cybersecurity Framework (CST), which includes five tenets: 

  • Identify cybersecurity risk to systems, assets, data and capabilities

    • This includes needing a strong physical security posture to prevent an on-premise cybersecurity attack. According to Verizon, breaches in 2021 stemmed from: external attacks (73 percent), partner incidents (39 percent), and internal incidents (18 percent).

    • Determine what risk threshold is acceptable.
  • Protect the organization from identified risks through controls to limit or contain the impact of a potential cybersecurity event.
    • This includes the implementation of both a Zero Trust framework and micro-segmentation.
  • Detect potential cybersecurity events in a timely manner
    • According to a 2022 IBM report, it took an average of 287 days to identify a data breach.
  • Respond to cybersecurity events, including having a response plan and performing activities to eradicate the incident and incorporate lessons learned into new strategies.
    • According to the 2022 IBM Report, the average time to contain a breach was 80 days.
  • Recover from cybersecurity events through actions to restore impaired capabilities or services.
    • The risk of cyberattacks is many times transferred by purchasing cyber-liability insurance.  While cities are many times self-insured, they can purchase cyber insurance help provide “financial, technical, and legal resources,” according to Marc Pfeiffer in StateTech magazine.

Proper Policies

While data and sensors are not infallible, neither are people. All the best technology in the world is useless if the wrong process is in place or the right process is not followed. A recent of this can be found in Detroit where facial recognition was used to identify a shoplifting suspect, but the process for investigating the theft was not followed and the wrong person was arrested.  

Policies and best practices should be planned for at the onset, with stakeholder partners as part of the process. Failure to do this will complicate the adoption of specific sensors or jeopardize the entire smart public safety initiative before it has a chance to start. With the proper policies and best practices, also leave room to grow those policies for existing and new technologies and uses. 

Smart public safety is where every police department should be heading. It is the evolution of police reform that works for the city, for internal and external partners, and for the police department.  It is the easiest digital transformation that a smart city will recognize, and can jumpstart other smart initiatives within the city. 

Jon Polly, PSP, IC3PM, is the chief solutions officer for ProTecht Solutions Partners, a security consulting company focused on smart city surveillance. He has worked as a project manager and system designer for city-wide surveillance and transportation camera projects in Raleigh and Charlotte, North Carolina; Charleston, South Carolina; and Washington, D.C. He is a member of the ASIS International Security Applied Sciences Community.

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