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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This brief explores two of the main challenges of spatial network data analysis: the many connected components in the spatial network and the many candidates that have to be processed. Within this book, these challenges are conceptualized, well-defined problems are explored, and critical techniques are discussed.The process of summarizing spatial network data entails finding a compact description or representation of observations or activities on large spatial or spatiotemporal networks. However, summarizing spatial network data can be computationally challenging for various reasons, depending on the domain. The content has applications for professionals, organizations, and researchers in transportation safety, public safety, public health, disaster response, and related fields.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This brief explores two of the main challenges of spatial network data analysis: the many connected components in the spatial network and the many candidates that have to be processed. Within this book, these challenges are conceptualized, well-defined problems are explored, and critical techniques are discussed.The process of summarizing spatial network data entails finding a compact description or representation of observations or activities on large spatial or spatiotemporal networks. However, summarizing spatial network data can be computationally challenging for various reasons, depending on the domain. The content has applications for professionals, organizations, and researchers in transportation safety, public safety, public health, disaster response, and related fields.