The 2011 riots in London, England, were an incredibly chaotic time. There were more than 20,000 emergency calls to police, a 400 percent increase from a normal day; and almost 2,200 calls to the London Fire Brigade, which is 15 times the normal amount.

To help catch those involved, the London Metropolitan Police crowdsourced the identities of 2,880 suspects using a smartphone application. The police asked citizens to download the Face Watch ID app and help identify the persons through images taken from closed-circuit television footage. If an image was known to them, citizens entered the name or address of the person, which was sent to the police immediately and confidentially. This enabled the police to effectively apprehend suspects and led to charges being filed against 1,000 perpetrators.

The targeted intervention concept is gaining steam in the human services field. For more than a decade, New York City’s Justice Mapping Center has used computer mapping and other graphical depictions of quantitative data to identify hidden patterns and trends, and direct social interventions based on those findings. By using geospatial analytics to direct human services, the Center maximizes program effectiveness.

For instance, in one of its programs, the Center tracks the residential addresses of inmates in various prison systems—the address that they gave when they went into prison. The center found that offenders often are concentrated in particular census blocks, some of them costing state and local governments more than $1 million a year in incarceration costs alone. Such findings are spurring cities to design re-entry initiatives for specific neighborhoods, with services such as transitional housing and job training for ex-offenders. By targeting these services towards high-risk areas, they can provide more tailored services more efficiently.

The Risk Assessment and Sentencing Tool (RAST) is a sophisticated data analytics engine that helps classify offenders as low-, medium-, and high-risk and makes targeted sentencing recommendations based on a host of case-specific factors. The RAST canvasses large data repositories across multiple states and jurisdictions, accounting for both static and dynamic factors. Static factors are unchangeable circumstances related to crimes and offenders, such as offense type, current age, criminal history, and age at first arrest. Dynamic factors, sometimes called criminogenic factors, can be mediated by interventions and include attitude, associates, substance use, and antisocial personality patterns.

The RAST is more advanced and more useful to judges, juries, and parole boards in three specific ways. First, since the Department of Justice’s National Institute of Justice administers it at the federal level, it relies on an exceptionally large, nationwide data set. Second, the data is continually reassessed for its predictive validity: It is reviewed annually to determine how often RAST correctly classifies offenders, accounts for static and dynamic factors, and makes effective sentencing decisions as measured by the rate of recidivism. Finally, RAST differs from traditional risk assessment tools because it takes into account more than answers to questionnaires. Static and dynamic factors are used in combination with specific, real-time data such as an offender’s behavior and location.

In a city of more than four million, and with a crime rate that rose in all categories in 2015, the Los Angeles Police Department knew that it needed to act. To help tackle crime, Los Angeles piloted a new tool incorporating some of the top Smart Security thinking: PredPol. The mission of PredPol is simple: Place officers at the right time and location to give them the best chance of preventing crime.

The tool, which has been piloted in the Los Angeles and Santa Cruz police departments, uses three data points—past type, place, and time of crime—to predict criminal behavior. These data points are fed into a unique algorithm, which incorporates criminal behavior patterns. Law enforcement then receive customized crime predictions, automatically generated for each shift in their jurisdiction. These predictions are highly specific and lay out the places, mapped to 500 by 500 feet squares, and times where crimes are most likely to occur. While still only a pilot, PredPol has already brought down property crimes by 13 percent in one of the divisions.

Chicago’s chief data officer Brett Goldstein is attempting to prevent violent crimes in the city before they happen. Goldstein’s predictive analytics unit runs spatial algorithms on 911 call data to identify where and when violent crimes or robberies are most likely to happen. As Goldstein puts it, “Different parts of the city behave in predictable ways—beyond a city of neighborhoods, Chicago is a city of blocks, and these blocks are part of an ecosystem. We can create mathematical models with this ecosystem that are statistically significant and give us leading indicators for when an expected level of a given behavior is likely to happen.”

Australia’s Cyber Security Centre (ACSC) tries to ensure that Australian networks are among the world’s most secure. Australia’s program combines threat data from multiple entities to strengthen collective intelligence between private sector, state and territory governments, academia, and international partners. The results of intrusion attempts are uploaded to the cloud, giving analysts from multiple agencies a larger pool of attack data to scan for patterns.

This collective intelligence revealed its value during the 2001 fight against the Lion worm, which exploited a vulnerability in computer connections. A few analysts noticed a spike in probes to port 53, which supports the Domain Name Service, the system for naming computers and network servers organized around domains. They warned international colleagues, who collaborated on a response. Soon, a system administrator in the Netherlands collected a sample of the worm, which allowed other experts to examine it in a “sandbox,” a protected testing environment. A global community of security practitioners then identified the worm’s underlying structure and built a program to detect it. In just 14 hours, they publicized their findings widely enough to defend computers worldwide.

In Albuquerque, New Mexico, summertime is accompanied by a rise in violent crimes, such as shootings, stabbings, and burglaries. To help cut down on the summer crime increase, the Albuquerque Police Department set up mobile surveillance cameras in parks around the city. But these weren’t your average surveillance cameras. Police officers could access their cameras from their mobile devices to view live images and remotely control the cameras, employing them during time-sensitive, critical situations such as negotiations with hostage-takers or other Special Weapons and Tactics (SWAT) emergencies.

In less urgent situations, the cameras come with a 4G wireless signal, sending images and videos back to the Real Time Crime Center for further analysis, and combining their footage with the over 100 traffic cameras and 300 private cameras positioned throughout the city. The surveillance units were also equipped with flood lights and a public address system, enabling the police to interact in real-time with any would-be troublemakers and prevent crime virtually. Along with the new technology came clear governance around camera usage, which has led to strong public support and privacy protections for citizens.