Murayama / Thapa Spatial Analysis and Modeling in Geographical Transformation Process

GIS-based Applications
1. Auflage 2011
ISBN: 978-94-007-0671-2
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 100, 302 Seiten

Reihe: GeoJournal Library

Currently, spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as GPS, Remote Sensing, and others.This book deals with spatial analysis and modelling. It provides a comprehensive discussion of spatial analysis, methods, and approaches related to human settlements and associated environment. Key contributions with empirical case studies from Iran, Philippines, Vietnam, Thailand, Nepal, and Japan that apply spatial analysis including autocorrelation, fuzzy, voronoi, cellular automata, analytic hierarchy process, artificial neural network, spatial metrics, spatial statistics, regression, and remote sensing mapping techniques are compiled comprehensively. The core value of this book is a wide variety of results with state of the art discussion including empirical case studies.It provides a milestone reference to students, researchers, planners, and other practitioners dealing the spatial problems on urban and regional issues.We are pleased to announce that this book has been presented with the 2011 publishing award from the GIS Association of Japan.
We would like to congratulate the authors!

Autoren/Hrsg.

Murayama, Yuji

Thapa, Rajesh Bahadur

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Contributors;10
4;1 Spatial Analysis: Evolution, Methods, and Applications;12
4.1;1.1 Development of Spatial Analysis with GIS;12
4.1.1;1.1.1 Genealogy of Spatial Analysis;13
4.1.2;1.1.2 New Spatial Analyses;16
4.1.3;1.1.3 Why Is Spatial Analysis Conducted with GIS?;17
4.1.3.1;1.1.3.1 Calculating;17
4.1.3.2;1.1.3.2 Mining;18
4.1.3.3;1.1.3.3 Visualizing;18
4.1.3.4;1.1.3.4 Creating Data;19
4.1.3.5;1.1.3.5 Handling Relations;19
4.1.3.6;1.1.3.6 Understanding Processes;20
4.1.4;1.1.4 Methodological Shift;20
4.1.4.1;1.1.4.1 From Aggregate Thinking to Non-aggregate Thinking;20
4.1.4.2;1.1.4.2 From Model-Driven to Data-Driven;21
4.1.4.3;1.1.4.3 From an Understanding of Pattern and Processes to Prediction, Control, and Management;22
4.2;1.2 Contemporary Spatial Analysis and Modeling;22
4.2.1;1.2.1 Spatial Autocorrelation;23
4.2.2;1.2.2 Geographically Weighted Regression;23
4.2.3;1.2.3 Spatial Metrics;24
4.2.4;1.2.4 Voronoi Method;24
4.2.5;1.2.5 Multi-criteria Decision Making and Analytical Hierarchy Process;25
4.2.6;1.2.6 Fuzzy Logic;27
4.2.7;1.2.7 Cellular Automata;27
4.2.8;1.2.8 Artificial Neural Network;28
4.2.9;1.2.9 Weight of Evidence;29
4.2.10;1.2.10 Markov Chain;30
4.2.11;1.2.11 Agent Based Model;30
4.3;1.3 Future Perspectives on Spatial Analysis and Modeling;31
4.4;1.4 Overview of the Book;32
4.5;References;34
5;Part I Spatial Scale, Autocorrelation and Neighborhood Analysis;38
5.1;2 Field-Based Fuzzy Spatial Reasoning Model for Constraint Satisfaction Problem;39
5.1.1;2.1 Introduction;39
5.1.2;2.2 Fuzzy Sets;40
5.1.2.1;2.2.1 Fuzzy Set Theory;40
5.1.2.2;2.2.2 Fuzzy Membership Function;41
5.1.3;2.3 Field-Based Qualitative Spatial Representation;43
5.1.3.1;2.3.1 Field-Based Models;43
5.1.3.2;2.3.2 Field-Based Qualitative Spatial Representation;44
5.1.3.2.1;2.3.2.1 Representation of Direction;45
5.1.3.2.2;2.3.2.2 Representation of Distance;46
5.1.4;2.4 Field-Based Fuzzy Spatial Reasoning;48
5.1.5;2.5 Application to an Illustrative Case Study;49
5.1.6;2.6 Discussion;52
5.1.7;2.7 Conclusion;53
5.1.8;References;53
5.2;3 Testing Local Spatial Autocorrelation Using k-Order Neighbours;55
5.2.1;3.1 Introduction;55
5.2.2;3.2 k -Order Neighbours and Their Measurement ;56
5.2.2.1;3.2.1 Defining k-Order Neighbours Using Delaunay Triangulation;56
5.2.2.2;3.2.2 Search Algorithms;57
5.2.2.3;3.2.3 Weight Coefficient Based on k-Order Neighbours;58
5.2.3;3.3 Local Spatial Autocorrelation Statistics with Weight Coefficient of k-Order Neighbours ;59
5.2.4;3.4 Example;60
5.2.5;3.5 Delaunay Triangulation of Ichikawa;61
5.2.5.1;3.5.1 Identification of Spatial Clusters;62
5.2.5.2;3.5.2 Verifying the Normality of Zi ;64
5.2.6;3.6 Conclusion;66
5.2.7;References;66
5.3;4 Effect of Spatial Scale on Urban Land-Use Pattern Analysis;67
5.3.1;4.1 Introduction;67
5.3.2;4.2 Methods;69
5.3.2.1;4.2.1 Data and Study Area;69
5.3.2.2;4.2.2 Land-Use Classification Systems;69
5.3.2.3;4.2.3 Data Processing;71
5.3.2.4;4.2.4 Detection of Spatial Autocorrelation;73
5.3.3;4.3 Results and Analysis;74
5.3.4;4.4 Discussion;79
5.3.5;4.5 Conclusions;81
5.3.6;References;82
5.4;5 Modeling Neighborhood Interaction in Cellular Automata-Based Urban Geosimulation;85
5.4.1;5.1 Introduction;85
5.4.2;5.2 Modelling Neighborhood Interaction;86
5.4.3;5.3 Calibration of Neighborhood Interaction;88
5.4.3.1;5.3.1 Study Area and Data Set;88
5.4.3.2;5.3.2 Calibration of Neighborhood Interaction;89
5.4.4;5.4 Simulation and Results;90
5.4.5;5.5 Concluding Remarks;92
5.4.6;References;92
6;Part II Urban Analysis: Zonation and Population Structure;94
6.1;6 Estimation of Building Population from LIDAR Derived Digital Volume Model;95
6.1.1;6.1 Introduction;95
6.1.2;6.2 Development of GIS Tool;96
6.1.2.1;6.2.1 Methodology;96
6.1.2.2;6.2.2 A GIS Tool;98
6.1.2.3;6.2.3 Data Requirement;98
6.1.2.4;6.2.4 Data Acquisitions;99
6.1.3;6.3 Dasymetric Mapping;100
6.1.4;6.4 Conclusion;104
6.1.5;References;105
6.2;7 Accuracy Assessment of GIS Based Building Population Estimation Algorithm;107
6.2.1;7.1 Introduction;107
6.2.2;7.2 Problems in Micro-Spatial Analysis;108
6.2.3;7.3 Methodology;109
6.2.3.1;7.3.1 Applied Method;109
6.2.3.2;7.3.2 Test Data;110
6.2.3.3;7.3.3 Test Method;111
6.2.3.4;7.3.4 Implementation of GIS Tool;111
6.2.4;7.4 Results and Accuracy Assessment;112
6.2.5;7.5 Potential Applications;117
6.2.6;7.6 Conclusion and Future Work;119
6.2.7;References;119
6.3;8 The Application of GIS in Education Administration;121
6.3.1;8.1 Introduction;121
6.3.2;8.2 Methods;122
6.3.2.1;8.2.1 Study Site;122
6.3.2.2;8.2.2 Research Method;125
6.3.3;8.3 Results and Discussion;126
6.3.3.1;8.3.1 Characteristic Features of the Constructed School Attendance Areas;126
6.3.3.2;8.3.2 Spatial Allocation of Students;127
6.3.3.2.1;8.3.2.1 Are students traveling further to get to better schools?;130
6.3.3.2.2;8.3.2.2 High Traffic Accident Rates in Iran;134
6.3.3.3;8.3.3 Minimizing Student Travel Time and Distance Through Proposed Attendance Areas;134
6.3.4;8.4 Conclusions;138
6.3.5;References;139
7;Part III Land Use and Land Cover Change;141
7.1;9 Accuracy of Land Use and Land Cover Mapping Methods;142
7.1.1;9.1 Introduction;142
7.1.2;9.2 Methods;144
7.1.2.1;9.2.1 Study Area: Tsukuba City, Urban Frontier of Tokyo;144
7.1.2.2;9.2.2 Data Sources;145
7.1.2.2.1;9.2.2.1 Remote Sensing Image Data;145
7.1.2.2.2;9.2.2.2 Image Geometry Rectification;147
7.1.2.2.3;9.2.2.3 Ground Reference Data;147
7.1.2.3;9.2.3 Scheming LULC Classification;148
7.1.2.4;9.2.4 Image Classification;149
7.1.2.4.1;9.2.4.1 Unsupervised Classification Approach;149
7.1.2.4.2;9.2.4.2 Supervised Classification Approach;149
7.1.2.4.3;9.2.4.3 Fuzzy Supervised classification Approach;150
7.1.2.4.4;9.2.4.4 GIS Post-processing Approach;151
7.1.3;9.3 Accuracy Assessment;151
7.1.4;9.4 Results and Discussion;152
7.1.5;9.5 Conclusions;156
7.1.6;References;157
7.2;10 Urban Dynamics Analysis Using Spatial Metrics Geosimulation;159
7.2.1;10.1 Introduction;159
7.2.2;10.2 Spatial Metrics;160
7.2.3;10.3 Study Area and Data Set;163
7.2.4;10.4 Urban Dynamics of Yokohama City;164
7.2.4.1;10.4.1 Land Use Structure Changes;164
7.2.4.2;10.4.2 Analysis of Spatial and Temporal Urban Land-Use Pattern;167
7.2.5;10.5 Conclusions;170
7.2.6;References;171
7.3;11 Modeling Deforestation Using a Neural Network-Markov Model;174
7.3.1;11.1 Introduction;174
7.3.2;11.2 Methods;176
7.3.2.1;11.2.1 Study Area;176
7.3.2.2;11.2.2 Multi-layer Perceptron Neural Network and Markov Model;178
7.3.2.2.1;11.2.2.1 Observed Changes in Forest Cover;179
7.3.2.2.2;11.2.2.2 Selection of Spatial Variables;180
7.3.2.2.3;11.2.2.3 Forest Conversion Potential Estimation;183
7.3.2.2.4;11.2.2.4 Prediction of Forest Conversion for Identifying Vulnerable Areas;183
7.3.3;11.3 Results;184
7.3.3.1;11.3.1 Observed Changes in Forest Cover;184
7.3.3.2;11.3.2 Model Validation;186
7.3.3.3;11.3.3 Areas Vulnerable to Future Forest Conversions;189
7.3.4;11.4 Discussion;190
7.3.5;11.5 Conclusions;192
7.3.6;References;193
8;Part IV Multi-criteria GIS Analysis;196
8.1;12 Land Suitability Analysis for Peri-Urban Agriculture;197
8.1.1;12.1 Introduction;197
8.1.2;12.2 Study Area: Hanoi Province;199
8.1.3;12.3 Database Methodology;202
8.1.4;12.4 Results;205
8.1.4.1;12.4.1 Soil;205
8.1.4.2;12.4.2 Land Use Pattern;207
8.1.4.3;12.4.3 Road Accessibility;207
8.1.4.4;12.4.4 Water Resources Accessibility;209
8.1.4.5;12.4.5 Market Accessibility;209
8.1.4.6;12.4.6 Land Allocation for Peri-Urban Agriculture;211
8.1.5;12.5 Discussion and Conclusion;213
8.1.6;References;217
8.2;13 Suitability Analysis for Beekeeping Sites Integrating GIS & MCE Techniques;219
8.2.1;13.1 Introduction;219
8.2.1.1;13.1.1 Economic Importance of Beekeeping;219
8.2.1.2;13.1.2 Geographic Information System (GIS) and Multi-criteria Evaluation (MCE) Techniques;220
8.2.1.3;13.1.3 Statement of the Problem and Objective of the Study;220
8.2.2;13.2 Materials and Methods;221
8.2.2.1;13.2.1 Study Area: Province of La Union, Philippines;221
8.2.2.2;13.2.2 Determination and Preparation of the Criteria;224
8.2.2.2.1;13.2.2.1 DEM, River and Road Networks;225
8.2.2.2.2;13.2.2.2 Land Use/Cover Map Development;225
8.2.2.3;13.2.3 The Conceptual Model;227
8.2.2.4;13.2.4 The Standardization of Factors to a Continuous Scale;229
8.2.2.5;13.2.5 The Analytical Hierarchy Process (AHP);230
8.2.2.6;13.2.6 The Validation Process;233
8.2.3;13.3 Results and Discussion;233
8.2.4;13.4 Conclusions;235
8.2.5;References;236
8.3;14 Spatial Allocation of the Best Shipping Canal in South Thailand;238
8.3.1;14.1 Introduction;238
8.3.2;14.2 Sea Navigation in the Asia Pacific Region;240
8.3.3;14.3 Environmental and Social Consequences;241
8.3.4;14.4 Potential Sites Assessment for Shipping Canal;243
8.3.4.1;14.4.1 Recent Status in Thailand;243
8.3.4.2;14.4.2 Study Area, Database and Method;243
8.3.4.3;14.4.3 Results and Discussion;246
8.3.5;14.5 Conclusions;253
8.3.6;References;253
9;Part V Socio-environmental Applications;255
9.1;15 Spatiotemporal Patterns of Urbanization: Mapping, Measurement, and Analysis;256
9.1.1;15.1 Introduction;256
9.1.2;15.2 Research Methodology;258
9.1.2.1;15.2.1 Study Area;258
9.1.2.2;15.2.2 Data Sources;258
9.1.2.3;15.2.3 Mapping of Spatial Patterns;260
9.1.2.4;15.2.4 Analysis of Spatial Patterns;262
9.1.3;15.3 Results;264
9.1.3.1;15.3.1 Spatial Patterns of Land Use;264
9.1.3.2;15.3.2 Spatial Patterns Changes;265
9.1.3.3;15.3.3 Landscape Fragmentation and Heterogeneity Analysis;267
9.1.4;15.4 Discussion;271
9.1.5;15.5 Conclusions;273
9.1.6;References;274
9.2;16 Spatial Determinants of Poverty Using GIS-Based Mapping;276
9.2.1;16.1 Introduction;276
9.2.2;16.2 Methods;277
9.2.2.1;16.2.1 Study Area;277
9.2.2.2;16.2.2 Data Collection;278
9.2.2.3;16.2.3 Pre-processing and Database Built-Up;281
9.2.2.4;16.2.4 Spatial Analysis;281
9.2.2.5;16.2.5 Regression Analysis;282
9.2.3;16.3 Discussion of Results and Analysis;284
9.2.3.1;16.3.1 Elevation;284
9.2.3.2;16.3.2 Slope;286
9.2.3.3;16.3.3 Soil;286
9.2.3.4;16.3.4 Rainfall;290
9.2.3.5;16.3.5 Access to River;290
9.2.3.6;16.3.6 Access to Road;290
9.2.3.7;16.3.7 Proximity to Major Markets;291
9.2.3.8;16.3.8 Influence of the Agrarian Reform Program;291
9.2.3.9;16.3.9 Influence of the Fiscal Decentralization Policy;292
9.2.3.10;16.3.10 Influence of Population Growth;292
9.2.4;16.4 Summary and Conclusion;292
9.2.5;16.5 Appendix 1;293
9.2.6;References;296
10;Index;298