Big Data for Public Security
The recent tragedies in Paris, France and Brussels, Belgium have spurred government agencies worldwide to analyze their public security systems to better anticipate and respond to coordinated criminal acts.
In June 2013, I published an article in Lianhe Zaobao, the most widely read Chinese-language newspaper in Singapore, explaining that the time has come for public officials to maintain security and social order by harnessing the power of Big Data analytics. Big Data platforms, such as the PRISM surveillance program used by the U.S. National Security Agency (NSA), provide the capability to share information with multiple departments quickly and precisely in response to such crises.
For national security authorities around the world, resolving the following challenges is top of mind:
- Preventing lone wolf and coordinated attacks by monitoring the communication and behaviors of suspicious individuals
- Accessing the plans of suspects by collecting and analyzing behavior patterns
- Sharing relevant and timely intelligence across departments
- Intercepting and disrupting anonymized and encrypted communications used to cloak organizational structures and operating modes
Big Data technologies open up many possibilities for resolving these difficult issues. By analyzing data from multiple sources, security authorities are able to find correlations between seemingly unrelated data points. Though sometimes, and often quite quickly, the data points are only connected after the fact, the goal remains to predict and prevent the occurrence of all such large-scale calamities.
Police officers accustomed to manually laboring over deep intelligence archives will no doubt encounter a big learning curve with Big Data technology.
In a 2015 interview, Ronen Horowitz, former Head of the Information Technology Division of the Israel Security Agency (ISA), said that the ISA had widely used Big Data analytics to track extremist leaders and take preemptive action to protect their national interests.
The Fire Department of the City of New York (FDNY) is another good example of how Big Data is saving lives and reducing property losses.
Responsible for maintaining inspections for over 330,000 buildings, including commercial properties and apartment complexes, the FDNY had long relied on an antiquated catalogue system to stay up to date. Each building in the city was assigned a card, which included occupancy, square footage, construction materials, and year built. Each of the 341 unit commanders was responsible for assigning each card a letter, A through E, indicating how often each building should be inspected.
In 2008, FDNY implemented a Risk-Based Inspection System (RBIS) that has since been refined to meet today’s challenges. Built on a data-analytics algorithm called FireCast and using 7,500 weighted risk factors, each district chief is presented with a daily report of the buildings at highest risk of experiencing a fire that day.
Updated in 2015, FireCast 3.0 sorts data that has been collected by 17 city agencies and the New York City’s 311 non-emergency phone reporting system, including building specifications, trash violations, and noise complaints. In the past, analyzing such large volumes of data would take months. The computational process for FireCast 3.0 takes no more than 90 minutes. Every night, powerful computers at FDNY head-quarters perform a statistical analysis that assigns each building a fire risk score based on three years of historical data. Buildings with the highest risk scores are placed at the top of a to-do list for building inspections.
Enhancing Public Security
Government 4.0 describes a rising trend in European countries that forecasts connecting government agencies with public facilities, assets, and services through Internet of Things (IoT)-based networks for access by all citizens. Government 4.0 is an echo of Industry 4.0, which is a collective term that embraces the integration of automation, data exchange, and manufacturing technologies.
The existence of Government 4.0 networks will radically change the way in which public services are designed, managed, and consumed. With digital labels, each public service product will be uniquely identified, available at any time day or night, record its own history, display and report real-time status, and suggest optional routes to achieve its target state. Data capture devices, such as cameras and sensors, will be embedded ubiquitously into public facilities and secure infrastructures. IoT technologies will link public service products with real-time management systems that are able to track from the moment an order for public services is generated and captured to final delivery.
Security is the most important public service product provided by governments. The PRISM and FireCast 3.0 systems are just two examples of how public services are delivered within a Government 4.0-style framework. Big Data has long been used by the U.S. and member states of the European Union to serve the largest public good, and we expect that China will soon be capable of equal breadth and utility in service to the public sector.