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.