重口味SM

U of T researcher鈥檚 data-driven platform aims to predict when emergencies will happen

Alberto Leon-Garcia is collaborating with Edmonton Fire Rescue Services and TELUS to support first responders in Alberta's second largest city
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(photo by Matthew Tierney)

A 重口味SM researcher is working with Edmonton Fire Rescue Services and TELUS, through its , to predict when emergencies are likely to occur in Alberta鈥檚 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鈥檚 behaviours tend to follow certain patterns.

鈥淭he pulse of the city is driven by people and their activity,鈥 he says, 鈥渁nd their activity exhibits seasonality.鈥

Leon-Garcia鈥檚 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 鈥 house fire, medical emergency, traffic accident and so forth 鈥 in addition to other relevant data points.

鈥淔or the city of Edmonton, we look at the neighbourhood level, at demographics, land use, transportation capabilities, population density,鈥 says Leon-Garcia. 鈥淲e consider the timing of the events, how they vary by season, month, day of the week, hour.

鈥淭his 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鈥檚 a valuable input to resource allocation 鈥 how many trucks, how many people you assign and where.鈥

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.

鈥淒eep neural networks were not appropriate in this instance,鈥 says Leon-Garcia, referring to the machine learning techniques behind such tools as ChatGPT. 鈥淵ou can try 鈥 and we did 鈥 but we did not have the volume of data to train a neural network.鈥

Instead, he turned to 鈥渨ell-established advanced analytics.鈥

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.

鈥淔or example, one time we noticed that the fire event numbers in a neighbourhood didn鈥檛 correspond to the models,鈥 says Leon-Garcia.

鈥淚t was later confirmed that an arsonist was active during that period.鈥

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鈥檚 critical to take a collaborative approach.

鈥淵ou can鈥檛 come in and say, 鈥業 have this neat platform, you have to change the way you do things,鈥欌 he says. 鈥淚t doesn鈥檛 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鈥檚 more a question of how you get from here to there.鈥

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.

鈥淗is 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.鈥

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