Implementing Data-Driven Strategies in Smart Cities is a guidebook and roadmap for practitioners seeking to operationalize data-driven urban interventions. The book opens by exploring the revolution that big data, data science, and the Internet of Things are making feasible for the city. It explores alternate topologies, typologies, and approaches to operationalize data science in cities, drawn from global examples including top-down, bottom-up, greenfield, brownfield, issue-based, and data-driven. It channels and expands on the classic data science model for data-driven urban interventions – data capture, data quality, cleansing and curation, data analysis, visualization and modeling, and data governance, privacy, and confidentiality. Throughout, illustrative case studies demonstrate successes realized in such diverse cities as Barcelona, Cologne, Manila, Miami, New York, Nancy, Nice, São Paulo, Seoul, Singapore, Stockholm, and Zurich. Given the heavy emphasis on global case studies, this work is particularly suitable for any urban manager, policymaker, or practitioner responsible for delivering technological services for the public sector from sectors as diverse as energy, transportation, pollution, and waste management.
Explores numerous specific urban interventions drawn from global case studies, helping readers understand real urban challenges and create data-driven solutions
Provides a step-by-step and applied holistic guide and methodology for immediate application in the reader’s own business agenda
Presents cutting edge technology presentation with coverage of innovations such as the Internet of Things, robotics, 5G, edge/fog computing, blockchain, intelligent transport systems, and connected-automated mobility
Urban policymakers, public administrators, city managers, data scientists or consulting companies managing smart city interventions and data-driven urban transformation projects. Early career researchers and graduate students from smart cities, urban research, urban planning, geography, transport, and economics interested in smart city design
Table of Contents
1. From smart city to data-driven city 2. Governance, decision-making, and strategy for urban development 3. Data Science technologies 4. Roadmap to develop a data-driven city 5. Enabling technologies for data-driven cities 6. Data analysis, modeling, and visualization in smart cities 7. Data-driven policy evaluation
Didier Grimaldi, PhD is Associate Professor at the La Salle–Ramon Llull University, Barcelona, Spain. His scholarly interests span novel forms of innovation to develop new or existing businesses by analyzing different models of public-private governance, which offer a more active role to the citizens. Dr. Grimaldi’s research focuses on evaluating the real effect of emerging technologies (big data, Internet of Things, drones, social media, etc.) to promote new services for citizens that improve their quality of life.
Affiliations and Expertise
Associate Professor, La Salle-Ramon Llull University, Barcelona, Spain
Carlos Carrasco-Farré in the Department of Operations, Innovation and Data Science at ESADE Business School, Barcelona, Spain. His research interest is in human-machine interactions and decision-making. More specifically, his research focuses in computational social science, the intersection of technology (artificial intelligence, machine learning, data science) and society (management, decision-making and strategy). He has contributed to various academic and nonacademic publications and books on economics, management, and urban strategy.
Affiliations and Expertise
Researcher, ESADE Business School, Ramon Llull University, Barcelona, Spain