Progress in geospatial analysis
著者
書誌事項
Progress in geospatial analysis
Springer, c2012
- : hbk
- タイトル別名
-
Springer GIS/cartography
大学図書館所蔵 全9件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
This book examines current trends and developments in the methods and applications of geospatial analysis and highlights future development prospects. It provides a comprehensive discussion of remote sensing- and geographical information system (GIS)-based data processing techniques, current practices, theories, models, and applications of geospatial analysis. Data acquisition and processing techniques such as remote sensing image selections, classifications, accuracy assessments, models of GIS data, and spatial modeling processes are the focus of the first part of the book. In the second part, theories and methods related to fuzzy sets, spatial weights and prominence, geographically weighted regression, weight of evidence, Markov-cellular automata, artificial neural network, agent-based simulation, multi-criteria evaluation, analytic hierarchy process, and a GIS network model are included. Part three presents selected best practices in geospatial analysis. The chapters, all by expert authors, are arranged so that readers who are new to the field will gain an overview and important insights. Those readers who are already practitioners will gain from the advanced and updated materials and state-of-the-art developments in geospatial analysis.
目次
From the Contents: Part I Geospatial Data Acquisition and Processing.- Multispectral Classification of Remote Sensing Data for Geospatial Analysis.- Part II Geospatial Theories and Methods.- Fuzzy Set Theory in Geospatial Analysis.- Spatial Prominence and Spatial Weights Matrix in Geospatial Analysis.- Geographically Weighted Regression in Geospatial Analysis.- Weight of Evidence in Geospatial Analysis.- Markov-Cellular Automata in Geospatial Analysis.- Multilayer Perceptron Neural Networks in Geospatial Analysis.- Part III Applications in Geospatial Analysis.- Urban Growth Modeling Using Bayesian Probability Function.- Land Suitability Assessment Using a Fuzzy Multi-Criteria Evaluation.- Neighborhood Interaction in Urban Land-Use Changes Using Cellular Automata-Based.
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