Delft University of Technology
Date: 25 April 2022
Title: Designing Equitable Cities at Scale
Abstract: In the last decade, technological advancements have enabled us to embed systems of large-scale sensor networks into the built environment. Consequently, urban data has emerged as an excellent stream of constant, real-time, and accurate information about all urban activities. This big data revolution, coupled with the capacity of infrastructure to be “smart”, has enticed cities worldwide to participate in machine-learning based decision making for improving the quality of life in cities. But city planning has largely been fragmented and instituted around loosely coupled organizations within municipal and regional governments, transport, water and energy operators. While some communities have enjoyed the benefits of policies based on the use of big data, machine learning and AI, many more have suffered disproportionately by being pushed to the physical and technological periphery of continual development in cities. The objective of this session is to elaborate on the current state of spatial inequalities that galvanize as a result of among other factors, piecemeal development driven by data and technology. Through examples of public values and accessibility, I will illustrate how designing infrastructure at scale for a better quality of life requires us to think in an integrated and systemic manner, while using transdisciplinary and participatory approaches.
Speaker Biography: Trivik Verma is an Assistant Professor at the faculty of Technology, Policy and Management in Delft University of Technology. He leads the Centre for Urban Science & Policy at the department of Multi-Actor Systems. He is the director of the TPM AI Lab and also an active member of the Dutch Network Science community and the 4TU Centre for Resilience Engineering.
His research focuses on tackling challenges of urbanization in an equitable and just manner. He is particularly interested in understanding the processes that drive and shape urbanization and inequalities from a computational perspective. He focuses on using methods in spatial data science, complex network analyses and participatory mapping to develop computational tools for advancing the theories and practices of urban science.