Geographic information systems in urban planning and management

목차 (Table of Contents)

• CONTENTS
• Part I. Fundamentals of Geographic Information Systems
• 1 Introduction of Geographic Information System = 3
• 1.1 Introduction = 3
• 1.2 Evolution of Geographical Information System = 4
• 1.2.1 Looking Behind the Geographic Information
• Systems = 4
• 1.2.2 Development in Geographic Information Systems = 5
• 1.2.3 Geographic Information Science (GIScience,
• GISc) Era = 9
• 1.3 Meaning and Definition of Geographic Information System = 11
• 1.4 Linkages Between GIS, Remote Sensing and Global
• Positioning System = 11
• 1.4.1 Need of Linkages Between GPS with Remote
• Sensing and Global Positioning System = 12
• 1.4.2 Integration Models = 13
• 1.5 Basic Components of Geographic Information System = 15
• 1.6 Capabilities of Geographic Information System = 18
• 1.7 Basic Application of Geographic Information System
• in Recent World = 19
• 1.7.1 Application in Cartographic Mapping = 19
• 1.7.2 Application in Telecom and Network Services = 20
• 1.7.3 Application in Accident Analysis and Hot Spot
• Analysis = 20
• 1.7.4 Application in Urban Planning = 20
• 1.7.5 Application in Transportation Planning = 20
• 1.7.6 Application in Environmental Impact Analysis = 20
• 1.7.7 Application in Agriculture = 21
• 1.7.8 Application in Disaster Management
• and Mitigation = 21
• 1.7.9 Application in Navigation = 21
• 1.7.10 Application in Natural Resources Management = 21
• 1.7.11 Application in Banking = 21
• 1.7.12 Application in Planning and Community
• Development = 21
• 1.7.13 Application in Irrigation Water Management = 22
• 1.8 Conclusion = 22
• References = 22
• 2 Referencing and Coordinate Systems in GIS = 25
• 2.1 Introduction = 25
• 2.2 Map Projection = 26
• 2.2.1 Types of Projection = 27
• 2.3 Coordinate System = 28
• 2.4 Geographic Coordinate System = 29
• 2.5 Projected Coordinate System = 33
• 2.5.1 The Universal Transverse Mercator (UTM) Grid
• System = 34
• 2.5.2 The Universal Polar Stereographic (UPS) Grid
• System = 36
• 2.5.3 The State Plane Coordinate (SPC) Grid System = 36
• 2.6 Widely Used Projections = 37
• 2.6.1 Azimuthal Projection-Stereographic = 37
• 2.6.2 Conic Projection-Lambert Conformal Conic = 38
• 2.6.3 Cylindrical Projection-Mercator = 38
• 2.6.4 Cylindrical Projection-Robinson = 40
• 2.6.5 Cylindrical Projection-Transverse Mercator = 41
• 2.7 Georeferencing = 42
• 2.7.1 Georeferencing of Raster Images = 43
• 2.7.2 Georeferencing of Vector Images = 44
• 2.8 Conclusion = 45
• References = 45
• 3 GIS Data Models = 47
• 3.1 Introduction = 47
• 3.2 Raster Data Model = 48
• 3.2.1 Components of Raster Data Model = 48
• 3.2.2 Raster Data Structure and Data Compression = 50
• 3.2.3 Important Raster Data Products = 52
• 3.3 Vector Data Model = 55
• 3.3.1 Vector Data Structure = 57
• 3.4 Vectorization and Rasterization = 59
• 3.5 Conclusion = 60
• References = 62
• 4 Data Input in GIS = 63
• 4.1 Introduction = 63
• 4.2 Sources of Geospatial Data = 64
• 4.3 Spatial Data Input in GIS = 67
• 4.3.1 Scanning = 68
• 4.3.2 Digitization = 69
• 4.3.3 Coordinate Geometry = 71
• 4.3.4 Table Spatialization = 71
• 4.3.5 Data Entry Errors and Spatial Data Editing in GIS = 71
• 4.4 Non-spatial Data Input in GIS = 73
• 4.5 Conclusion = 74
• References = 75
• 5 Data Visualization and Output = 77
• 5.1 Introduction = 77
• 5.2 Geovisualization Process = 78
• 5.3 GIS Data Output = 80
• 5.3.1 Cartographic Representation of the Qualitative
• Data = 83
• 5.3.2 Cartographic Representation of the Quantitative
• Data = 83
• 5.3.3 Mapping Terrain Elevation = 85
• 5.3.4 Cartographic Representation of the Time Series
• Data = 85
• 5.4 Conclusion = 87
• References = 87
• 6 Spatial Data Analysis = 89
• 6.1 Introduction = 89
• 6.2 Analytical Capabilities of GIS = 91
• 6.3 Vector Data Analysis = 93
• 6.4 Raster Data Analysis = 96
• 6.5 Conclusion = 103
• References = 104
• 7 Non-spatial Data Management = 105
• 7.1 Introduction = 105
• 7.1.1 Spatial Data = 106
• 7.1.2 Non-spatial Data = 106
• 7.2 Non-spatial Data in GIS = 108
• 7.2.1 Types of Attribute Tables = 108
• 7.2.2 Database Management = 109
• 7.2.3 Attribute Data Types = 110
• 7.3 The Relational Model = 110
• 7.3.1 Example of Relational Database : SSURGO = 112
• 7.3.2 Normalization = 112
• 7.3.3 Types of Relationships = 114
• 7.4 Joins, Relates and Relationship Classes = 116
• 7.4.1 Joins = 116
• 7.4.2 Relates = 118
• 7.4.3 Relationship Classes = 118
• 7.5 Spatial Join = 118
• 7.6 Attribute Data Entry = 119
• 7.6.1 Field Definition = 119
• 7.6.2 Methods of Data Entry = 119
• 7.6.3 Attribute Data Verification = 120
• 7.7 Manipulation of Fields and Attribute Data = 120
• 7.7.1 Adding and Deleting Fields = 120
• 7.7.2 Attribute Data Classification = 121
• 7.7.3 Attribute Data Computation = 121
• 7.8 Conclusion = 122
• References = 122
• 8 Application of GIS in Urban Policy/Planning/Management = 125
• 8.1 Introduction = 125
• 8.2 Application of GIS in Microlevel Planning = 126
• 8.2.1 Concept of the Microlevel Planning = 126
• 8.2.2 Use of Remote Sensing and GIS in Microlevel
• Planning = 127
• 8.3 Use of Remote Sensing and GIS in Hydrological
• Management = 128
• 8.4 Application of Remote Sensing and GIS for Sustainable
• Development = 130
• 8.5 Use of Remote Sensing and GIS in Agricultural Resource
• Management = 131
• 8.5.1 Remote Sensing and GIS in Inventory of Crops = 131
• 8.5.2 Use of Remote Sensing and GIS for the Crop
• Management = 132
• 8.5.3 Nutrient and Water Stress Estimation Using
• Remote Sensing and GIS = 132
• 8.5.4 Flood Monitoring Using Remote Sensing and GIS = 133
• 8.5.5 Remote Sensing and GIS-Based Assessment
• of Land Use/Land Cover (LULC) = 134
• 8.5.6 GIS and Remote Sensing in Agro-Metrological
• Application = 135
• 8.5.7 Remote Sensing and GIS in Pest Infestation = 135
• 8.6 Remote Sensing and GIS in Sustainable Tourism
• Development = 136
• 8.7 Application of Remote Sensing and GIS in Disaster
• Management = 137
• 8.8 Conclusion = 139
• References = 140
• Part II. Case Studies : Applications of Geographic Information
• Systems in Urban Planning and Management
• 9 Case Study 1 : Monitoring and Modelling of Urban Land Use
• Changes = 145
• 9.1 Introduction = 145
• 9.2 Overview of the Study Area = 147
• 9.3 Database and Methodology = 147
• 9.3.1 Data Used = 148
• 9.3.2 Methodology = 149
• 9.3.3 Image Acquisition and Preprocessing = 149
• 9.3.4 Image Classification = 150
• 9.3.5 Criteria for Classification = 151
• 9.3.6 Supervised Classification = 151
• 9.3.7 Post-classification Processing = 151
• 9.3.8 Result and Discussion = 152
• 9.4 Conclusion = 153
• References = 155
• 10 Case Study 2 : Simulating Future Urban Growth Using
• Cellular Automata-Markov Chain Models = 157
• 10.1 Introduction = 157
• 10.2 Overview of the Study Area = 159
• 10.3 Materials and Methods = 160
• 10.3.1 Data Collections = 160
• 10.3.2 Data Processing = 160
• 10.4 Result and Discussion = 163
• 10.4.1 LULC Change Analysis and Urban Sprawl = 163
• 10.4.2 Analysis of the Markov Transition Probability
• Matrix = 166
• 10.4.3 Validation = 167
• 10.5 Conclusion = 167
• References = 168
• 11 Case Study 3 : Identification of Potential Sites for Housing
• Development Using GIS-Based Multi-criteria Evaluation
• Technique = 171
• 11.1 Introduction = 171
• 11.2 Overview of the Study Area = 174
• 11.3 Database and Methodology = 175
• 11.3.1 Database and Properties of Criterion = 175
• 11.3.2 Criterion Standardization = 180
• 11.3.3 Assigning Rank and Estimation of Criteria Weights = 180
• 11.3.4 Built-Up Suitability = 183
• 11.4 Results = 183
• 11.4.1 Criteria Influence Analysis for AHP = 183
• 11.4.2 Suitability Area Analysis for AHP = 185
• 11.4.3 Validation of the Result = 186
• 11.5 Discussion and Conclusion = 186
• References = 188
• 12 Case Study 4 : Urban Green Space Analysis and Potential Site
• Selection for Green Space Expansion in NCT Delhi = 191
• 12.1 Introduction = 191
• 12.2 Advantages of the Urban Green Spaces (UGS) = 192
• 12.3 Overview of the Study Area = 194
• 12.4 Methodology = 194
• 12.4.1 Mapping of the Existing Urban Green Space = 195
• 12.4.2 Urban Green Space Analysis = 196
• 12.4.3 Potential Site Selection for Expansion of Urban
• Green Space = 196
• 12.5 Results and Discussion = 197
• 12.6 Conclusion = 201
• References = 203
• 13 Case Study 5 : A Multi-criteria Decision-Making
• for Alternative Landfill Site Selections Using Fuzzy TOPSIS
• Approach = 205
• 13.1 Introduction = 205
• 13.2 Overview of the Study Area = 207
• 13.3 Database and Methodology = 208
• 13.3.1 Criterion for the Selection of Landfill Sites = 208
• 13.3.2 Preparation of Fuzzy Rank Decision Matrix = 210
• 13.3.3 Normalized Fuzzy Decision Matrix = 211
• 13.3.4 Weighted Normalized Fuzzy Decision Matrix = 213
• 13.3.5 Fuzzy Positive Ideal Solution and Fizzy Negative
• Ideal Solution (FPIS & FNIS) = 213
• 13.3.6 Distance from FPIS and FNIS = 216
• 13.3.7 Closeness Coefficient and Suitability Rank = 216
• 13.4 Result and Discussion = 216
• 13.5 Conclusion = 219
• References = 219
• 14 Case Study 6 : Urban Flood Susceptibility Modelling
• of Srinagar Using Novel Fuzzy Multi-layer Perceptron Neural
• Network = 221
• 14.1 Introduction = 222
• 14.2 Overview of the Study Area = 222
• 14.3 Database and Methodology = 223
• 14.3.1 Flood Conditioning Factors = 224
• 14.3.2 Flood Inventory Databases = 228
• 14.3.3 Fuzzy Multi-layer Perceptron Neural Network
• (Fuzzy MLPNN) = 229
• 14.3.4 Accuracy Assessment of the Flood Risk Map = 230
• 14.4 Results = 231
• 14.4.1 Flood Susceptibility Modelling = 233
• 14.4.2 Role of the Flood Conditioning Factors = 234
• 14.4.3 Map Validation by AUC Analysis = 235
• 14.5 Discussion = 235
• 14.6 Conclusion = 237
• References = 237
• 15 Case Study 7 : Assessment, Mapping and Prediction of Urban
• Heat Island in Srinagar City Region = 239
• 15.1 Introduction = 239
• 15.2 Overview of the Study Area = 240
• 15.3 Data and Methods = 241
• 15.3.1 Urban Heat Island Mapping and Assessment = 242
• 15.3.2 Urban Heat Island (UHI) Prediction = 244
• 15.4 Results and Discussion = 245
• 15.5 Conclusion = 251
• References = 252