How Data-driven Cities are Revolutionizing Urban Living
Synthetic data revolutionizes urban planning, predicting needs for smarter, citizen-centric cities.
Delve into urban transformation, explained by James Hill, CRO at Mindtech. Uncover the impact of synthetic data on city planning, predicting needs, and crafting a sustainable, citizen-centric future.
At its core, a smart city is designed to improve its citizens’ lives and drive sustainability through technology. The streets we use and our homes are adapting to people’s needs, becoming safer, more efficient, environmentally friendly, and inclusive. And this adaptation is being advanced by the rising use of synthetic data.
For example, four in five journeys made by Londoners happen on our streets. They are at the center of how we move around and connect. How we use them and how they are designed is increasingly down to data. Maps tell us the best route to travel, sensors control street lights, and monitors assess pollution levels. The more technology becomes connected, the more detailed data paints the picture of how our cities work.
What links all of these city features together? From air quality and traffic sensors to autonomous vehicles and mobile phones, the Internet of Things (IoT) connects all of these various devices, allowing them to communicate and form an interconnected source of data.
This data hub is exponentially expanding, but this growth can lead to data bottlenecks that hinder operations and lead to scaling issues. Real-world data alone cannot carry the burden. And this is giving rise to synthetic cities – a whole new world of accessible data that reflects the real world and can even predict our needs.
Data in action
Data fuels our cities. The more we can harness it with technology, the more we can make cities citizen-centric. TfL’s Healthy Streets is a scheme investing in “high-quality, appealing public spaces, healthy residents, and efficient transport networks.” The goal is to encourage people to get out and use sustainable modes of travel by making streets cleaner, safer, and more attractive. To do this, decision-makers need data to understand how streets are used and people travel. This then helps them design infrastructure and monitor any changes in real-time.
More widely, the 15-minute city idea has been picking up speed. It revolves around the notion that everything a person needs – for work, shopping, healthcare – is within a 15-minute walk or cycle from anywhere in the city. This aims to encourage exercise and reduce emissions, amongst other benefits.
But the idea is also divisive. Parts of the plans, such as the emergence of Low Traffic Neighborhoods and ULEZ, have been met with praise and disapproval. These schemes encourage active travel like cycling, walking, and cleaner air, but if implemented incorrectly, they can also cause traffic bottlenecks and more pollution in other areas.
Data is key to introducing such initiatives successfully. But in some cases, it might not be possible to gain enough of it to understand the impact of any changes. How do you test for scenarios where data in the real world is small or inconsistent? How do you try schemes to find the optimum ways of introducing them?
See More: Guide to 5G: Building Future-Ready Smart Cities
The Matrix Effect: The Role of Synthetic Cities
Computers artificially generate synthetic data to match and resemble real-world environments statistically. In these ‘synthetic cities’, urban planners can test out a range of circumstances where real-world data may be too scarce, costly, time-consuming, or unsafe to gather. For example, it is estimated there are over one million potholes in the UK – a huge number, but this is just an estimate. There is a lack of tangible insight into the true scale of the problem.
Real-world data is still fundamental for training synthetic data models, and combining the two is the ultimate mix for successful innovation. But synthetic data allows us to fill in the gaps, ensure data privacy, and scale much more widely. The more data generated for any edge case required, the greater the potential to deliver datasets that accurately resemble the real world.
It starts with simple but impactful everyday scenarios. For instance, recording street damage in the real world can be time-consuming, inefficient, and cause more congestion. With synthetic models, planners can create a range of scenes and scenarios, varying lighting and weather conditions to test common occurrences and less common use cases, such as worn road markings and discarded items.
This can be extended to outdoor spaces such as parks, squares, and sports grounds. Synthetic data can replicate congestion management, incident identification, customer support, and lost equipment in these large public settings. Ultimately, this data helps make spaces more inclusive and accessible, a vital goal of a smart city.
A Connected Future
Our cities have so many interactions, with physical infrastructure and street furniture intertwined with our mobiles and the internet, creating a connected world through the IoT. The more data we have to hand, the more we can understand the relationships between our homes, streets, devices, and how we travel.
Real-time sensors detect street objects, monitor pollution levels, and provide data on waste management and energy efficiency. Buses communicate with bus stop signs, mobiles, and maps to create a connected web of live travel information. Data on how this affects accessing local support services, businesses, and bookings in restaurants and accommodation is gathered. The appeal of our streets also has a direct impact on our homes. For example, how do our public spaces affect how long people stay in and how much energy they use?
For these systems to be trained effectively, they need exhaustive data supplies to test a range of scenarios. With synthetic data, they are set up to deal with any data anomalies and rarer edge cases, such as extreme weather and traffic incidents. It provides a wealth of data needed to deliver an increasingly connected future that can be scaled widely and enhance our daily lives.
A smart city depends on data to meet its goals. And real-world data, of course, remains integral to operations. But despite an abundance on offer, there can be problems with physically accessing enough of it and testing alternative real-world scenarios. Synthetic data can allow us to replicate the real world, helping us transform urban living and predict future needs. The more data-driven our cities, the smarter they can serve their citizens.
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Image Source: Shutterstock
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