Frequencity: A data-driven tool for the resilience of delta urbanism
A Study conducted by Sridhar Subramani at IAAC, Barcelona, Spain.
The design study investigates the development of a data-driven tool to anticipate and meet urban users’ service demands through water spaces and movable floating structures. By utilizing statistical time-use surveys and a reinforcement learning process the tool can evaluate the frequency of use, user demand, and geographic distribution of floating services over time and improve development to meet varying demand locations. Through the evaluation of the se-densification factor, proximity, and availability of floating structures, the tool can provide specific implementation strategies and transitions for resilient urban development. The case study location is Rotterdam, Netherlands.
In response to the growing demand for urban services and the need for resilient urban developments, this study aims to investigate the potential for water spaces and movable floating structures to meet the demands of urban users in a timely and efficient manner. The goal is to develop a data-driven tool that can anticipate and respond to these demands, with a focus on enhancing urban resilience in the face of climate change.
The study is centered on the city of Rotterdam, in the Netherlands, which has been at the forefront of experimentation with floating structures. The first phase of the research involves creating a time survey that represents the frequency, demand, and geographic distribution of floating services over time. This is done using statistical data to capture user activities in a given area on a particular day.
The second phase of the research involves the use of reinforcement learning to improve the deployment time of floating structures to different demand locations. The tool can learn and adapt to better meet user demands by iteratively analyzing the data from the time survey.
In the third phase, the tool evaluates the de-densification factor, proximity, and availability of different floating objects through time. It also compares the performance of different locations and floating objects and provides specific implementation strategies for resilient urban developments in the face of climate change.
Ultimately, the research aims to provide decision-makers with a tool that can anticipate and respond to the demands of urban users in a timely and efficient manner. By leveraging the power of data-driven analysis, the tool can help to ensure that cities are resilient and adaptive in the face of changing environmental conditions.
