Delineating a ‘15-Minute City’: An Agent-based Modeling Approach to Estimate the Size of Local Communities

With progressively increased people living in cities, and lately the global COVID-19 outbreak, human mobility within cities has changed. Coinciding with this change, is the recent uptake of the ‘15-Minute City’ idea in urban planning around the world. One of the hallmarks of this idea is to create a high quality of life within a city via an acceptable travel distance (i.e., 15 minutes). However, a definitive benchmark for defining a ‘15- Minute City’ has yet to be agreed upon due to the heterogeneous character of urban morphologies worldwide. To shed light on this issue, we develop an agent-based model named ‘D-FMCities’ utilizing realistic street networks and points-of-interest, in this instance the borough of Queens in New York City as a test case. Through our modeling we grow diverse communities from the bottom up and estimate the size of such local communities to delineate 15-minute cities. Our findings suggest that the model could be helpful to detect the flexibility of defining the extent of a ‘15-minute city’ and consequently support uncovering the underlying factors that may affect its various definitions and diverse sizes throughout the world.

Qingqing Chen
Qingqing Chen
PhD Candidate | Data Scientist

Ph.D. candidate focuses on critically understanding urban space by leveraging big data, combined with data science and machine learning techniques.

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