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Understanding the relationships between urban space, perceptions and behaviours using urban embeddings

The physical urban space affects people's activities and daily patterns. Vice versa, people's activities affect the design and evolvement of the urban space. Moreover, the physical urban space and how people perceive this space are correlated but may not be the same.

This project aims to deepen our understanding of the innate relationships between the (physical) urban space, people's urban perceptions of the urban space, and human activities in urban space. Recent advances in machine learning combined with the availability of new data sources, such as text reviews of places and street-level photos, offer opportunities to study these complex relationships at unprecenteded levels of depth and scale.

In the first study, we investigate associations between the number of people encountered in urban spaces and the characteristics of those spaces. To this end, we have developed a new method to collect and process millions of street-level images. We analyse the collected images using computer vision models to count the number of people and identify urban space characteristics, such as vegetation and land use. Finally, we regress the number of people with the characteristics of the urban space. In line with behaviour intuition, the results of our first study show that people tend to be in places with more food places and bikes.

Our subsequent studies aim to develop structural causal models that identify causal relationships rather than associations. Ultimately, this project strives to generate insights that contribute to the design of cities better in tune with their citizens' needs.

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