Abstract
As urban complexity grows and societal challenges are increasingly complex to address, urban resilience becomes a key factor to enhance our cities. This is particularly relevant when cities are analyzed under the lens of smart approaches to urbanism and Artificial Intelligence (AI) and data-driven city models. The adoption of urban resilience approach leads to communities with a better quality of life and improved environmental conditions toward a general sustainable development of cities. This chapter addresses the notion of urban resilience through the concepts of smart, data-driven, and sustainable cities. As a multi-faceted notion, urban resilience is broken down into three complementary approaches. First, urban resilience is presented as a quantifiable entity, discussed through advanced spatial approaches using AI, cyber security, and Machine Learning (ML) techniques. This part also includes the distinction between physical and digital elements of community resilience. Second, it is considered in its more original sense, that of response to extreme events in lifelines conditions with examples involving Geographic Information Systems (GIS), Building Information Modeling (BIM), and network analysis approaches. Finally, urban resilience is considered more technically a concerted approach to react to climate change/action, and natural disasters where new technologies and infrastructure management procedures are needed. Each part is supported by practical examples to help readers understand concrete applications in spatial terms. Through this three-pronged approach, this chapter proposes a possible definition of urban resilience, focusing on ways in which cities can improve on their urban performance, with the desire to provide strategies and solutions for urban problems to provide a higher quality of human life.
Original language | American English |
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Title of host publication | The Routledge Companion to Smart Design Thinking in Architecture & Urbanism for a Sustainable, Living Planet |
Pages | 138-148 |
Number of pages | 11 |
ISBN (Electronic) | 9781040107775 |
DOIs | |
State | Published - 1 Jan 2024 |
All Science Journal Classification (ASJC) codes
- General Engineering
- General Environmental Science
- General Arts and Humanities