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This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently - to become 'smart' and 'sustainable'. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of 'big' data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
This book addresses one of the fundamental theoretical issues in GIS: uncertainties in spatial data and analysis. Along with presenting research findings, the text provides methods to control uncertainties in GIS applications. The author discusses his own unique research in modeling positional uncertainty based on probability theory and statisti
Modeling uncertainties in geographic information science is essential to the development of the field. This book addresses the fundamental theoretical issue: uncertainties in spatial data and analysis. It provides methods to control uncertainties in GIS applications. It introduces the area of uncertainty-based spatial mining.
This edited collection covers many of the cutting edge issues related to spatial data quality, including measurement in GIS and geostatistics, the modeling of spatial objects that have inherent uncertainity, and much more.
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