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, Sao 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.
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