New technology will soon transform every aspect of urban life
New technology will soon transform every aspect of urban life
New technologies will change our cities in ways we cannot even imagine, but the key is to ensure that innovation is dedicated to building healthier cities.
Shifts are happening already. We hail Uber and Lyft vehicles from our phones, book Airbnb rooms through an app, and publicly share our thoughts and experiences on Twitter, Instagram and Facebook.
New machine-learning techniques developed at Ryerson are extracting powerful insights from social media content that are capable of detecting and mapping cases of food poisoning and deploying inspectors to hot spots.
Similarly, advanced mathematical models developed at the university are learning how to move more people through the street grid more efficiently and even predict how to improve the system in the future.
Perhaps biggest of all, quantum computing is poised to fundamentally disrupt almost every aspect of our daily lives.
Once considered science fiction, quantum computers are just now starting to become a reality with massive implications for data encryption, security and transmission of information. In the coming quantum computer world, health care, voting and many other urban issues will be impacted.
Ryerson researchers are telling companies, industries and governments how to stay ahead in the quantum world.
A better commute using math
Pawel Pralat is using the power of advanced mathematics to make sense of the chaos of people's urban movements.
The professor at the Department of Mathematics in Ryerson's Faculty of Science has developed mathematical models that understand and predict how people move through complex networks like an urban street grid, or large buildings like an airport, shopping mall or a busy railway station.
"We don't have to build an extra road or rearrange the roads and then see what happens," he says. "We are able to understand and predict what will happen before we actually build or rearrange the roads."
Pralat's models are incredibly valuable to everyone from city planners to industry partners in advertising who want to target campaigns at people who are more likely to be interested in certain products.
Recently, Pralat worked with Toronto-based marketing company Environics Analytics to create a synthetic population model that reproduced the behaviour of commuters from across the city and linked it with information, such as age, ethnicity, number of children, interests and work location.
"Advertising on the Internet is pretty simple … if I want to target people with special interests, it's easy to do," he says. "Surprisingly, [the understanding of] out-of-home billboard advertisements is very immature. If I want to target people who like hockey and have three kids or more, it's not clear where's the best intersection in Toronto to do that."
The movement models Pralat developed may also help direct autonomous vehicles through the city in the most efficient way, avoiding traffic and arriving at a selected destination quicker.
"Predicting the behaviour of one particle in water or one car in a big system is impossible, but predicting the behaviour of the system globally is certainly possible," he says.
"If you look at a system from a large distance you actually see lots of patterns."
Data security in the quantum age
Quantum computing is coming and when it does, Atty Mashatan is advising how companies and governments can keep people's data safe in this brave new world by planning for the transition from quantum-vulnerable to quantum-resistant technologies.
"Some of the standardized encryption functions that we use prevalently right now are vulnerable against a quantum computer," says Mashatan, director of Ryerson's Cybersecurity Research Lab, who also specializes in blockchain technology and Internet of Things (IoT) security.
Classical computers process information in the form of bits, which can be either a zero or a one. A quantum computer is a completely new type of machine that works in a fundamentally different way by using an entirely different type of physics, quantum mechanics, to store and process data.
This new approach to computing gives the quantum computer a huge advantage in solving some hard mathematical problems that can help protect the confidentiality of our sensitive data such as health records, social insurance numbers and financial transactions.
"During Y2K everyone was in panic mode, ICT managers didn't know what would happen and were bracing for an emergency. There was a lot of costs we could have saved by planning ahead of time. What we're talking about now is 'Y2Q' — years to quantum, and we are saying let's plan ahead to avoid cost and panic."
This all means that quantum computers will be incredibly powerful and able to potentially crack current encryption measures in moments. "The quantum computer achieves speedups against some security mechanisms while it completely breaks others by solving the underlying hard mathematics problem," she says.
Mashatan is advising companies and industries to get ahead of the game and keep people's private information such as social insurance numbers and health records safe using quantum-resistant cryptography.
So far uptake among private companies has been slow, but the federal government is taking action, she says.
"We're getting closer to making [quantum computing] a reality. No one has a crystal ball to say if it's going to be in 10 years, or 20 years, or 30 years. There are some engineering challenges ahead of an attack-capable quantum computer.
"Quantum resistance is insurance. We don't think the flood is going to take place tomorrow … but we all have insurance for our houses against it."
Public health lessons from Twitter
What if a city could learn from its citizens' social media posts?
Electrical engineering professor Ebrahim Bagheri, who holds the Canada Research Chair in Software and Semantic Computing, has developed systems that can analyze social media content such as tweets and pick out important trends that would otherwise go unnoticed.
Recently, Bagheri worked with the City of Chicago to scan tweets for language related to food poisoning — a post about feeling ill after eating at a particular restaurant, for example.
"We built machine learning-based classification techniques that listened to Twitter and classified every single tweet related to food-borne illnesses," he says.
The system linked the content of the tweet to the Unified Medical Language System, a controlled medical vocabulary, and created a map using the location data. Food inspection officials would then adjust their inspections with more targeted and focused inspection plans.
"The primary goal of social analytics is to build intelligent systems that consume user-generated data to extract actionable insights from the data."
Bagheri's machine learning system has a broad range of uses. It can identify tweets that contain medical symptoms and reply with helpful resources, and even detect suicidal language and learn from the person's social connections for predisposition to suicidal behaviour.
Beyond the medical world, the system has been used to gather information about what people think of certain products and services.
"These social platforms didn't exist 10 or 15 years ago … a lot of things that we do used to be done in social sciences based on field studies and questionnaires," he says.
"There is a lot of information within user-generated content that gives you a sense of what people are thinking about, their concerns, interests, thought processes, and all of those can go into the process of decision-making within the city," he says.
Privacy expectation on social media
Anatoliy Gruzd, a Canada Research Chair in Social Media Data Stewardship, is studying how people and organizations adapt to social media in various domains, and examines tensions between privacy and self-disclosure on social media networks.
"Social media by its nature is social and often very public," he says. "But when people share on platforms like Facebook or Instagram, there is a public that they know or can envision, but then there is the wider and broader public that may potentially include everybody."
"As part of our work at the Social Media Lab, we're trying to understand users' attitudes towards the collection and usage of social media data by third parties around sensitive topics such as health and employment that are often shared on social media."
Gruzd's research shows that people's expectations of privacy change based on who is using their information and for what purpose. It also makes a difference whether the information identifies the source or aggregates it with others.
In some parts of the world, especially in countries with repressive regimes, collecting people's social media data for study may expose them to danger, and so researchers must take this into account.
Recently, Gruzd and his colleagues looked at whether there was a "privacy paradox" on social media — that people willingly share personal information while also worrying about whether that information is kept private.
Participants were invited to use a service called Data Selfie that allowed them to see what Facebook might know about them as they use the platform and interact with friends and strangers. His team found that after discovering the amount of data and predictive analytics they were generating for companies like Facebook, many reported feeling increased concern for their privacy and were more likely to act to protect it.
These results have direct implications for developing information and data literacy programs, but also for researchers who want to learn about the world from social media content.
"We're trying to strike a balance to understand people's privacy concerns when they engage in social media, while still using social media to drive social change, and changes in policy.'