GIS, Environmental Modeling and Engineering

AvAllan Brimicombe

Häftad, Engelska, 2020

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Beskrivning

Spatial dimensions need to be properly captured if modeling and engineering techniques are to be successfully applied in addressing environmental problems. The links between the geographical information systems (GIS) that capture this data, simulation modeling, and engineering offer tremendous possibilities for building versatile support systems for managing the environment. GIS, Environmental Modeling and Engineering focuses on using GIS and external models to solve real environmental problems, promoting the critical thinking needed for the effective applications of these systems and their analytical outputs.Divided into three major sections, this textbook first concentrates on defining GIS, identifying how data is structured, and explaining common functionality. The text examines GIS from a technological perspective, exploring the evolution of its scientific basis and its synergies with other technologies within a geocomputational paradigm. The next section explores modeling from a neutral scientific perspective in its role of simulating phenomena, as well as from a more specific perspective in its role within environmental science and engineering. The third and largest section looks at how GIS and simulation modeling are joined. It provides case studies and covers issues such as interoperability, data quality, model validity, space-time dynamics, and decision-support systems. This volume provides seniors and postgraduate students with a structured, coherent text that goes beyond introductory subject matter by enabling readers to think critically about the data acquisition process and the results they get from the technology.

Produktinformation

Utgivningsdatum:2020-06-30
Mått:156 x 234 x 23 mm
Vikt:562 g
Format:Häftad
Språk:Engelska
Antal sidor:378
Upplaga:2
Förlag:Taylor & Francis Ltd
ISBN:9780367577193

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Mer om författaren

Professor Allan Brimicombe is Head of the Centre for Geo-Information Studies at the University of East London. Prior to this he was the founding Head of the Department of Land Surveying and Geo-Informatics at the Hong Kong Polytechnic University which in turn was preceded by a successful international career in consulting engineering.

Recensioner i media

This well-written, second edition is a significant and needed contribution to the understanding of the increasing interaction between geospatial disciplines and tools - i.e., GIS, and their use in conjunction with environmental and engineering models for problem solving. The exposition is clear, lively, and scholarly, revealing an adequate use of a colloquial tone while keeping commentary brief and to the point with few linguistic variants and regional terms used. For the most part, figures are well designed, clearly printed, with their elements discernible, and the book abounds in helpful diagrams and illustrations for both conceptualization and example.This book provides a guiding paradigm for the study of the current possibilities for mutual synergy between geospatial contextualization and the mathematical and algorithmic formulations of model/simulation inputs and outputs. This volume would undoubtedly enrich the libraries of those professionals who are seeking a deeper understanding of how to better integrate environmental and engineering modeling - both with and from within a GIS, constituting a valuable resource for research projects and course materials.—Demetrio P. Zourarakis, PhD, GISp, CMS-RS, eMS-GIS/LIS, Remote Sensing/GIS Analyst, in Photogrammetric Engineering & Remote Sensing, January 2011Praise for the Previous Edition: Brimicombe’s rich experience clearly shows ... the book is a useful guide to the core information about the workings of environmental models and their development." -Muki Haklay, University College London, in Environment and Planning B, 2005

Innehållsförteckning

Introduction. From GIS to geocomputation. The rise of geo-information science and engineering. Approaches to modelling. The role and nature of environmental models. Case studies. Issues of coupling the technologies. Data and information quality issues. Modelling issues. Decision-making under uncertainty.