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A �ؿ�ζSM researcher is working with Edmonton Fire Rescue Services and TELUS, through its , to predict when emergencies are likely to occur in Alberta’s second largest city.

The tool being developed by Alberto Leon-Garcia, a professor in the Edward S. Rogers Sr. department of electrical and computer engineering in the Faculty of Applied Science & Engineering, and the two partners leverages data to more efficiently allocate municipal emergency resources and help first-responders. 

Leon-Garcia says many emergency events can be predicted because people’s behaviours tend to follow certain patterns.

“The pulse of the city is driven by people and their activity,” he says, “and their activity exhibits seasonality.”

Leon-Garcia’s platform uses data from 11 years of emergency calls, which provide the time and approximate location of each event as well as the type of emergency – house fire, medical emergency, traffic accident and so forth – in addition to other relevant data points.

“For the city of Edmonton, we look at the neighbourhood level, at demographics, land use, transportation capabilities, population density,” says Leon-Garcia. “We consider the timing of the events, how they vary by season, month, day of the week, hour.

“This can allow you to predict the rate of events in the vicinity of each fire station in the next week or month, for example. Right there, that’s a valuable input to resource allocation – how many trucks, how many people you assign and where.”

Creating the model required collecting the necessary data and then refining it so it was free from errors and standardized, possibly transformed or aggregated. Next, researchers needed to determine the most useful way to analyze it.

“Deep neural networks were not appropriate in this instance,” says Leon-Garcia, referring to the machine learning techniques behind such tools as ChatGPT. “You can try – and we did – but we did not have the volume of data to train a neural network.”

Instead, he turned to “well-established advanced analytics.”

The data analysis will generate various graphs, heat maps and other tables that display the type and mixture of emergency events that the model considers normal in and around Edmonton for a given time and place while taking into account variables such as weather.

By following events in real time and comparing them to what is anticipated, researchers can detect anomalies and potential vulnerabilities in the model.

“For example, one time we noticed that the fire event numbers in a neighbourhood didn’t correspond to the models,” says Leon-Garcia.

“It was later confirmed that an arsonist was active during that period.”

Over the years, Leon-Garcia has applied his predictive models to various road transportation systems, including in Toronto and the San Francisco Bay Area. He has also applied his anomaly detection systems to detect faults in computer networks and cyberattacks.

Given that each partner in such a project typically has its own goals and unique data collection processes, Leon-Garcia says it’s critical to take a collaborative approach.

“You can’t come in and say, ‘I have this neat platform, you have to change the way you do things,’” he says. “It doesn’t work that way. You have to pull together, factor in their long-term goals, their privacy concerns, their flexibility. They generally see the usefulness of the approach and [then] it’s more a question of how you get from here to there.”

Professor Deepa Kundur, chair of the electrical and computer engineering department, says Leon-Garcia has consistently demonstrated how data streams hold the key to creating smarter, safer cities.

“His partnership with Edmonton FRS and TELUS has the potential to greatly enhance life-saving initiatives and will, no doubt, serve as a catalyst for future collaborations.”