“The power of this developing technology is the power of crowdsourcing. Each person is a sensor on his own, and from all those sensors, we can gather so much useful information. We refer to this as ‘human-as-a-sensor’.”
Moving toward urban reasoning
Assem is shifting his attention to an emerging generation of cognitive technologies that focus on reasoning rather than simple prediction. “Urban reasoning,” as he calls it, relies for input on other traditional fields like environmental engineering, civil engineering, network engineering, transportation and sociology in the context of urban spaces. Using that input in an AI model can help city managers and businesses fine-tune their plans.
“Building new technologies for the aim of predicting and detecting events across cities is very interesting, but we can really have an impact once we’re able to understand the reasons for the patterns we detect,” he says.
A telco operator, for example, might be able to predict that a certain part of the city will be at its crowd peak between 9 a.m. and 4 p.m. on weekdays, but allocating a high level of resources to meet that crowd density might not make sense.
“If we know that the reason for the crowd pattern there is because it’s a business district, then we also know that most people in that area at that time are at work, where they have access to Wi-Fi, so crowd size doesn’t correlate to a high demand for network availability,” says Assem.
“On the other hand, if we know that the reason for an increased crowd size is because it’s an area where there are a lot of entertainment activities, then it makes sense to allocate more resources to meet the needs of that crowd.”
Inversely, planners could also use that kind of knowledge to spread those entertainment activities across a wider swath of the city, spreading out the stress on the city’s infrastructure as well.
“Urban computing aims to help us understand the nature of urban phenomena and even predict the future of cities,” Assem explains. “Urban reasoning aims to extend this vision with a main focus on providing insights about the reasons for the major challenges that our cities face, such as crowd congestion, increased network demand, air pollution and water resources management.”
For a deeper dive into Assem’s work, read “Spatio-Temporal Clustering Approach for Detecting Functional Regions in Cities,” published in Tools with Artificial Intelligence (ICTAI), 2016 IEEE 28th International Conference.