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Although research performed in cities will not uncover new evolutionary mechanisms, it could provide unprecedented opportunities to examine the interplay of evolutionary forces in new ways and new avenues to address classic questions. However, while the variation within and among cities affords many opportunities to advance evolutionary biology research, careful alignment between how cities are used and the research questions being asked is necessary to maximize the insights that can be gained. In this review, we develop a framework to help guide alignment between urban evolution research approaches and questions. Using this framework, we highlight what has been accomplished to date in the field of urban evolution and identify several up-and-coming research directions for further expansion. We conclude that urban environments can be used as evolutionary test beds to tackle both new and long-standing questions in evolutionary biology.
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This content will become publicly available on December 1, 2025
Digitizing cities for urban weather: representing realistic cities for weather and climate simulations using computer graphics and artificial intelligence
Abstract Due to their importance in weather and climate assessments, there is significant interest to represent cities in numerical prediction models. However, getting high resolution multi-faceted data about a city has been a challenge. Further, even when the data were available the integration into a model is even more of a challenge due to the parametric needs, and the data volumes. Further, even if this is achieved, the cities themselves continually evolve rendering the data obsolete, thus necessitating a fast and repeatable data capture mechanism. We have shown that by using AI/graphics community advances we can create a seamless opportunity for high resolution models. Instead of assuming every physical and behavioral detail is sensed, a generative and procedural approach seeks to computationally infer a fully detailed 3D fit-for-purpose model of an urban space. We present a perspective building on recent success results of this generative approach applied to urban design and planning at different scales, for different components of the urban landscape, and related applications. The opportunities now possible with such a generative model for urban modeling open a wide range of opportunities as this becomes mainstream.
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- PAR ID:
- 10552802
- Publisher / Repository:
- Computational Urban Science
- Date Published:
- Journal Name:
- Computational Urban Science
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2730-6852
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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