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Mohammed Sarrif2020-07-07 1819
Public authorities have always aimed to make cities livable. A livable city has many characteristics, including robust transport infrastructure, healthcare and education facilities, culture and environment, and safety and sustainability. In recent times, however, another characteristic has become increasingly critical: resiliency. Broadly speaking, resiliency is a city’s capacity to anticipate, resist, absorb, accommodate, adapt to, transform, and recover its social, economic, and technological systems and infrastructures, from the effects of a potentially disruptive event, and do so swiftly and efficiently.
Today, a city runs on digital infrastructure and depends on billions of connected devices that generate zettabytes of data. To create resilient cities in the digital age, city managers need to look at the concept of resiliency from a fresh perspective, since these cities are now defined by their overarching emphasis on two key areas: emergency management and data sovereignty.
One of the key characteristics of a modern Smart City is its ability to handle emergencies, from natural disasters, such as earthquakes, floods, and epidemics, to man-made threats, including fires, riots, and acts of war. In 2020, cities across the world are facing the largest pandemic in recent history, elevating the demands on cities to an entirely new level. The COVID-19 pandemic has tested the digital infrastructure of cities, putting communication networks under pressure (as more people are forced to work from home) and increasing cyberthreat levels.
Such scenarios put city managers under immense pressure to work faster and more efficiently. Fortunately, city managers have already found an answer to this challenge: data. Today, city managers are sitting on unprecedented amounts of data derived from diverse sources, including Internet of Things (IoT) sensor networks, social media, biometric applications, video networks, and numerous consumer and enterprise applications. Deriving value out of this data is a challenge and the forward-thinking Smart Cities of the world are tackling this by building ‘urban predictive operations centers’ that harness the power of IoT, Big Data Analytics (BDA), Artificial Intelligence (AI), and Machine Learning (ML).
Urban predictive operations centers — Intelligent Operation Centers (IOCs) — are essentially real-time intelligence centers that centralize the vast array of data that cities generate. They also convert such data into information that various city departments can use to respond quickly and efficiently to emergencies. These IOCs amalgamate the human and technological expertise shared across various departments, from police and fire departments to road and transport authorities, hospitals, and national security agencies. They also use various technological tools to unearth patterns that may not be detectable otherwise. Such patterns can be particularly vital in disease outbreak situations.
The real-time intelligence center of the digital age thrives on a set of technologies. These technologies work together performing various tasks, from data collection and integration, to analysis and visualization, often even triggering an automated response. The key technologies essential for a real-time intelligence platform to perform predictive operations are:
IoT: The extent of data availability forms the foundation of intelligence centers. The IoT network forms the sensory system of a city, enabling it to become aware.
Cloud: For a city to be truly resilient, data should flow seamlessly from the network of data collection devices to the intelligence center, and eventually to the various agencies that take action. A cloud-enabled network becomes essential for such a system to work.
AI and ML: The feature that makes a city predictive, rather than reactive, is its capability to unearth patterns from its plethora of data. Big data analytics platforms powered by AI and ML have advanced data analysis from being human-led to being machine-led.
Platforms: Real-time intelligence centers must bring together data from a myriad of systems from different agencies. These government agencies and their numerous departments feature systems and applications, old and new, creating an integration nightmare. As a result, interoperability is critical for predictive operations to work and open platforms are key enablers.
Figure: Predictive Operations through Real-Time Intelligence Centers
Longgang District in Shenzhen, China, is a good example of how a real-time intelligence center can be developed. Authorities in the district embarked on a journey to build the world’s first ‘city with a brain’ as early as 2013. The authorities built an IOC that has access to district-wide information systems. This center can manage all city applications via a single map, enabling administrators to monitor the entire district with one click. The Longgang IOC makes use of big data, IoT, and cloud platforms that bring together data from 60 municipal departments. Altogether, the center handles 2000 types of municipal data resources as well as 400 million municipal and 20 billion district data records.
Citizens, businesses, and governments share sensitive data with each other, enabling efficient city operations and better experiences for their residents. Technologies such as BDA, AI, and ML help Smart Cities turn the huge volumes of generated data into meaningful insights that improve sustainability and livability, especially in areas such as transportation, public safety, and health. However, this also raises concerns about data security. As city operations are becoming increasingly data-centric, the safety and security of data and digital infrastructure becomes a key factor to ensure city resiliency.
As more data and processes are digitized, data loss due to natural disasters or man-made threats becomes more disruptive than ever. A model resilient city requires collaborative effort, leadership, and preparedness in terms of Disaster Recovery (DR) and business continuity of all physical and virtual infrastructure and connectivity components in the data lifecycle. The resilience of businesses is vital to the resilience of the communities they serve, which in turn is part and parcel of the resilience of the city itself.
Despite its evident importance, the lack of DR and business continuity procedures and plans — as well as the ensuing data loss, downtime, and communications failure — still means that up to two-thirds of businesses never reopen after a major disaster. This is an issue that could be avoided, or at least reduced simply by ensuring that preventative measures — data backup technologies and recovery management protocols and objectives, ranging from simple cloud backup to full overseas recovery data centers — are in place, documented, tested, and rehearsed as part of business continuity planning.
The key challenge for city managers and government authorities is finding the perfect balance between enabling data sharing and protecting that data, or enabling at the very least the protection of the data that supports their city’s resilience. Government authorities need to have proper classification of data sets based on their criticality and usage. These classifications will also form the basis of data governance policies, as well as where the data should reside, be it in a public cloud, government cloud, or on-premises.
Cities looking to improve safety and sustainability as well as predict emergencies and technology breaches must build ‘city brains’ that harness data. Data should flow seamlessly across the various elements of city operations, as this will create value and help various departments make informed decisions and take quick action. A real-time IOC that relies on digital infrastructure will become the key enabler of a resilient city in the digital age. As city operations thrive on data sharing, city managers must also take a comprehensive approach toward security and privacy. Cities should consider their ‘digital trustworthiness’ as a key metric, and implement shared principles and standards for data use.