دانلود کتاب Geographic Information Systems in Urban Planning and Management (Advances in Geographical and Environmental Sciences)

فهرست مطالب :

Preface\nContents\nAbout the Author\nPart I Fundamentals of Geographic Information Systems\n1 Introduction of Geographic Information System\n 1.1 Introduction\n 1.2 Evolution of Geographical Information System\n 1.2.1 Looking Behind the Geographic Information Systems\n 1.2.2 Development in Geographic Information Systems\n 1.2.3 Geographic Information Science (GIScience, GISc) Era\n 1.3 Meaning and Definition of Geographic Information System\n 1.4 Linkages Between GIS, Remote Sensing and Global Positioning System\n 1.4.1 Need of Linkages Between GPS with Remote Sensing and Global Positioning System\n 1.4.2 Integration Models\n 1.5 Basic Components of Geographic Information System\n 1.6 Capabilities of Geographic Information System\n 1.7 Basic Application of Geographic Information System in Recent World\n 1.7.1 Application in Cartographic Mapping\n 1.7.2 Application in Telecom and Network Services\n 1.7.3 Application in Accident Analysis and Hot Spot Analysis\n 1.7.4 Application in Urban Planning\n 1.7.5 Application in Transportation Planning\n 1.7.6 Application in Environmental Impact Analysis\n 1.7.7 Application in Agriculture\n 1.7.8 Application in Disaster Management and Mitigation\n 1.7.9 Application in Navigation\n 1.7.10 Application in Natural Resources Management\n 1.7.11 Application in Banking\n 1.7.12 Application in Planning and Community Development\n 1.7.13 Application in Irrigation Water Management\n 1.8 Conclusion\n References\n2 Referencing and Coordinate Systems in GIS\n 2.1 Introduction\n 2.2 Map Projection\n 2.2.1 Types of Projection\n 2.3 Coordinate System\n 2.4 Geographic Coordinate System\n 2.5 Projected Coordinate System\n 2.5.1 The Universal Transverse Mercator (UTM) Grid System\n 2.5.2 The Universal Polar Stereographic (UPS) Grid System\n 2.5.3 The State Plane Coordinate (SPC) Grid System\n 2.6 Widely Used Projections\n 2.6.1 Azimuthal Projection—Stereographic\n 2.6.2 Conic Projection—Lambert Conformal Conic\n 2.6.3 Cylindrical Projection—Mercator\n 2.6.4 Cylindrical Projection—Robinson\n 2.6.5 Cylindrical Projection—Transverse Mercator\n 2.7 Georeferencing\n 2.7.1 Georeferencing of Raster Images\n 2.7.2 Georeferencing of Vector Images\n 2.8 Conclusion\n References\n3 GIS Data Models\n 3.1 Introduction\n 3.2 Raster Data Model\n 3.2.1 Components of Raster Data Model\n 3.2.2 Raster Data Structure and Data Compression\n 3.2.3 Important Raster Data Products\n 3.3 Vector Data Model\n 3.3.1 Vector Data Structure\n 3.4 Vectorization and Rasterization\n 3.5 Conclusion\n References\n4 Data Input in GIS\n 4.1 Introduction\n 4.2 Sources of Geospatial Data\n 4.3 Spatial Data Input in GIS\n 4.3.1 Scanning\n 4.3.2 Digitization\n 4.3.3 Coordinate Geometry\n 4.3.4 Table Spatialization\n 4.3.5 Data Entry Errors and Spatial Data Editing in GIS\n 4.4 Non-spatial Data Input in GIS\n 4.5 Conclusion\n References\n5 Data Visualization and Output\n 5.1 Introduction\n 5.2 Geovisualization Process\n 5.3 GIS Data Output\n 5.3.1 Cartographic Representation of the Qualitative Data\n 5.3.2 Cartographic Representation of the Quantitative Data\n 5.3.3 Mapping Terrain Elevation\n 5.3.4 Cartographic Representation of the Time Series Data\n 5.4 Conclusion\n References\n6 Spatial Data Analysis\n 6.1 Introduction\n 6.2 Analytical Capabilities of GIS\n 6.3 Vector Data Analysis\n 6.4 Raster Data Analysis\n 6.5 Conclusion\n References\n7 Non-spatial Data Management\n 7.1 Introduction\n 7.1.1 Spatial Data\n 7.1.2 Non-spatial Data\n 7.2 Non-spatial Data in GIS\n 7.2.1 Types of Attribute Tables\n 7.2.2 Database Management\n 7.2.3 Attribute Data Types\n 7.3 The Relational Model\n 7.3.1 Example of Relational Database: SSURGO\n 7.3.2 Normalization\n 7.3.3 Types of Relationships\n 7.4 Joins, Relates and Relationship Classes\n 7.4.1 Joins\n 7.4.2 Relates\n 7.4.3 Relationship Classes\n 7.5 Spatial Join\n 7.6 Attribute Data Entry\n 7.6.1 Field Definition\n 7.6.2 Methods of Data Entry\n 7.6.3 Attribute Data Verification\n 7.7 Manipulation of Fields and Attribute Data\n 7.7.1 Adding and Deleting Fields\n 7.7.2 Attribute Data Classification\n 7.7.3 Attribute Data Computation\n 7.8 Conclusion\n References\n8 Application of GIS in Urban Policy/Planning/Management\n 8.1 Introduction\n 8.2 Application of GIS in Microlevel Planning\n 8.2.1 Concept of the Microlevel Planning\n 8.2.2 Use of Remote Sensing and GIS in Microlevel Planning\n 8.3 Use of Remote Sensing and GIS in Hydrological Management\n 8.4 Application of Remote Sensing and GIS for Sustainable Development\n 8.5 Use of Remote Sensing and GIS in Agricultural Resource Management\n 8.5.1 Remote Sensing and GIS in Inventory of Crops\n 8.5.2 Use of Remote Sensing and GIS for the Crop Management\n 8.5.3 Nutrient and Water Stress Estimation Using Remote Sensing and GIS\n 8.5.4 Flood Monitoring Using Remote Sensing and GIS\n 8.5.5 Remote Sensing and GIS-Based Assessment of Land Use/Land Cover (LULC)\n 8.5.6 GIS and Remote Sensing in Agro-Metrological Application\n 8.5.7 Remote Sensing and GIS in Pest Infestation\n 8.6 Remote Sensing and GIS in Sustainable Tourism Development\n 8.7 Application of Remote Sensing and GIS in Disaster Management\n 8.8 Conclusion\n References\nPart II Case Studies: Applications of Geographic Information Systems in Urban Planning and Management\n9 Case Study 1: Monitoring and Modelling of Urban Land Use Changes\n 9.1 Introduction\n 9.2 Overview of the Study Area\n 9.3 Database and Methodology\n 9.3.1 Data Used\n 9.3.2 Methodology\n 9.3.3 Image Acquisition and Preprocessing\n 9.3.4 Image Classification\n 9.3.5 Criteria for Classification\n 9.3.6 Supervised Classification\n 9.3.7 Post-classification Processing\n 9.3.8 Result and Discussion\n 9.4 Conclusion\n References\n10 Case Study 2: Simulating Future Urban Growth Using Cellular Automata-Markov Chain Models\n 10.1 Introduction\n 10.2 Overview of the Study Area\n 10.3 Materials and Methods\n 10.3.1 Data Collections\n 10.3.2 Data Processing\n 10.4 Result and Discussion\n 10.4.1 LULC Change Analysis and Urban Sprawl\n 10.4.2 Analysis of the Markov Transition Probability Matrix\n 10.4.3 Validation\n 10.5 Conclusion\n References\n11 Case Study 3: Identification of Potential Sites for Housing Development Using GIS-Based Multi-criteria Evaluation Technique\n 11.1 Introduction\n 11.2 Overview of the Study Area\n 11.3 Database and Methodology\n 11.3.1 Database and Properties of Criterion\n 11.3.2 Criterion Standardization\n 11.3.3 Assigning Rank and Estimation of Criteria Weights\n 11.3.4 Built-Up Suitability\n 11.4 Results\n 11.4.1 Criteria Influence Analysis for AHP\n 11.4.2 Suitability Area Analysis for AHP\n 11.4.3 Validation of the Result\n 11.5 Discussion and Conclusion\n References\n12 Case Study 4: Urban Green Space Analysis and Potential Site Selection for Green Space Expansion in NCT Delhi\n 12.1 Introduction\n 12.2 Advantages of the Urban Green Spaces (UGS)\n 12.3 Overview of the Study Area\n 12.4 Methodology\n 12.4.1 Mapping of the Existing Urban Green Space\n 12.4.2 Urban Green Space Analysis\n 12.4.3 Potential Site Selection for Expansion of Urban Green Space\n 12.5 Results and Discussion\n 12.6 Conclusion\n References\n13 Case Study 5: A Multi-criteria Decision-Making for Alternative Landfill Site Selections Using Fuzzy TOPSIS Approach\n 13.1 Introduction\n 13.2 Overview of the Study Area\n 13.3 Database and Methodology\n 13.3.1 Criterion for the Selection of Landfill Sites\n 13.3.2 Preparation of Fuzzy Rank Decision Matrix\n 13.3.3 Normalized Fuzzy Decision Matrix\n 13.3.4 Weighted Normalized Fuzzy Decision Matrix\n 13.3.5 Fuzzy Positive Ideal Solution and Fizzy Negative Ideal Solution (FPIS & FNIS)\n 13.3.6 Distance from FPIS and FNIS\n 13.3.7 Closeness Coefficient and Suitability Rank\n 13.4 Result and Discussion\n 13.5 Conclusion\n References\n14 Case Study 6: Urban Flood Susceptibility Modelling of Srinagar Using Novel Fuzzy Multi-layer Perceptron Neural Network\n 14.1 Introduction\n 14.2 Overview of the Study Area\n 14.3 Database and Methodology\n 14.3.1 Flood Conditioning Factors\n 14.3.2 Flood Inventory Databases\n 14.3.3 Fuzzy Multi-layer Perceptron Neural Network (Fuzzy MLPNN)\n 14.3.4 Accuracy Assessment of the Flood Risk Map\n 14.4 Results\n 14.4.1 Flood Susceptibility Modelling\n 14.4.2 Role of the Flood Conditioning Factors\n 14.4.3 Map Validation by AUC Analysis\n 14.5 Discussion\n 14.6 Conclusion\n References\n15 Case Study 7: Assessment, Mapping and Prediction of Urban Heat Island in Srinagar City Region\n 15.1 Introduction\n 15.2 Overview of the Study Area\n 15.3 Data and Methods\n 15.3.1 Urban Heat Island Mapping and Assessment\n 15.3.2 Urban Heat Island (UHI) Prediction\n 15.4 Results and Discussion\n 15.5 Conclusion\n References