Cities are complex ecosystems that are evolving fast and — like citizens — becoming increasingly digital. By 2025, the total number of global internet users is expected to reach 6.2 billion; the total number of connected devices is expected to surpass 100 billion by the same year. These connected devices will generate 180 ZB of data per year— a fivefold increase from today. City management teams are under tremendous pressure to work faster and more efficiently as the digital infrastructure of cities rapidly evolves. They are also expected to make use of the networks of devices and the data generated, to cater to the needs of digital natives.
The creation of Smart Cities has been a major focus area of governments in recent years. However, the concept of the Smart City must evolve in tandem with the changes affecting city ecosystems. Cities typically have three core characteristics that make them smart or intelligent:
• Awareness: Smart Cities have real-time awareness of rapidly changing conditions such as their environment (weather, pollution levels), traffic (congestion, accidents), and public safety status (emergencies, crime incidents). Much slower or periodically shifting conditions such as population or demographic changes and levels of economic activity (trade, retail sales, tourism, travel, and manufacturing) also factor into the operations and development of Smart Cities.
• Response: Great levels of awareness, coupled with quick response times, make a city truly smart. In city operations, efficient responses can prevent dangerous situations escalating, avert disasters, and save lives.
• Prediction: The ability to predict and proactively respond to events is integral to Smart Cities. Such an ability was considered the realm of science fiction only two decades ago, but with the amount of data captured and supported by Artificial Intelligence (AI) algorithms today, we are closer to this becoming reality.
Over time, city operations teams have tried to develop these characteristics by capitalizing on various technological advancements. For example, closed-circuit television cameras, sensors in heating, ventilating, and air conditioning systems, and building management systems have all been used to significantly increase city awareness. However, the siloed nature of these electronic systems has limited awareness and responses remain manual. Their limitations have slowed the shift of city operations from reactive to proactive (they will eventually become predictive). In short, the lack of integration between these individual technology elements in city ecosystems has hindered the transformation of cities into truly intelligent entities, even though the different elements evolved on their own.
Scientists believe that mankind became the dominant species on Earth because of a period of rapid brain development in the evolutionary process. Cities are going through a similar period of “cognitive revolution”— a period of rapid “city brain” development. While this revolution was triggered by the rapid buildup of sensory systems — the network of connected things — it is fueled by data.
The biggest challenge that city management teams face in data-driven city operations is deriving value from the increasing volume, velocity, and variety of data — also known as big data. To address this challenge, city operations teams are creating data models that can visualize the complex operations of city functions in real-time. These models are known as “digital twins.” Dr. Michael Grieves, one of the concept’s pioneers, defines digital twins as having three parts: physical products in real space, virtual products in virtual space, and connected data that ties the physical and virtual together.
These data models are live digital blueprints of physical assets, processes, and entire ecosystems, such as cities. What differentiates digital twins from traditional visualizations is their dynamism and real-time capabilities.