Introduction to Geographic

Transcription

1 Eighth Edition Introduction to Geographic Information Systems Kang-tsung Chang University of Idaho Mc Graw Hill Education

2 Preface xiv CHAPTER 1 Introduction GIS Components of a GIS A Brief History of GIS GIS Software Products 4 Box 1.1 A List of GIS Software Producers and Their Main Products Elements of GIS Geospatial Data Data Acquisition Attribute Data Management Data Display Data Exploration Data Analysis Applications of GIS 10 Box 1.2 A List of GIS Applications 10 Box 1.3 Precision Farming 11 Box 1.4 Location-Based Services and Social Networking Integration of Desktop GIS, Web GIS, and Mobile Technology Web Mapping Collaborative Web Mapping Volunteered Geographic Information Implications of Web and Mobile Applications Organization ofthis Book Concepts and Practice 14 Key Concepts and Terms 75 Review Questions 15 Applications: Introduction 16 Task 1 Introduction to ArcCatalog 16 Task 2 Introduction to ArcMap 17 Challenge Task 18 References 19 CHAPTER 2 Coordinate Systems Geographic Coordinate System Approximation of the Earth Datum 23 Box 2.1 Datum Shift in Australia and New Zealand Map Projections 25 Box 2.2 How to Measure Distances on Earth's Surface Types of Map Projections Map Projection Parameters Commonly Used Map Projections Transverse Mercator Lambert Conformal Conic Albers Equal-Area Conic Equidistant Conic Web Mercator 31 iv

3 v 2.4 Projected Coordinate Systems 31 Box 2.3 Map Scale The Universal Transverse Mercator Grid System The Universal Polar Stereographic Grid System The State Plane Coordinate System The Public Land Survey System Options for Coordinate Systems in GIS Projection File Predefined Coordinate Systems 35 Box 2.4 A Projection File Example On-the-Fly Projection 36 Box 2.5 GIS Tools for Working With Coordinate Systems 37 Key Concepts and Terms 37 Review Questions 39 Applications: Coordinate Systems 40 Task 1 Project from a Geographic to a Projected Coordinate System 40 Task 2 Import a Coordinate System 41 Task 3 Project Using a Predefined Coordinate System 41 Task 4 Reproject a Coordinate System 42 Challenge Task 42 References 43 CHAPTER 3 Vector Data Model Representation of Simple Features 45 Box 3.1 Specifications of Spatial Features by Google and OpenStreetMap Topology TIGER Importance of Topology 4 7 Box 3.2 Adjacency and Incidence Georelational Data Model 49 Box 3.3 Topology or No Topology The Coverage Coverage Data Structure Nontopological Vector Data Object-Based Data Model Classes and Class Relationships Interface The Geodatabase 54 Box 3.4 ArcObjects and ArcGIS Topology Rules Advantages of the Geodatabase 56 Box 3.5 NHDinGEO Representation of Composite Features TINs Regions Routes 59 Key Concepts and Terms 61 Review Questions 62 Applications: Vector Data Model 63 Task 1 Examine the Data File Structure of Coverage and Shapefile 63 Task 2 Create File Geodatabase, Feature Dataset, and Feature Class 64 Task 3 Convert a Shapefile to a Personal Geodatabase Feature Class 65 Task 4 Examine Polylines with Measures 65 Task 5 View Regions and Routes 66 Task 6 View TIN 66 Challenge Task 66 References 67 CHAPTER 4 Raster Data Model Elements of the Raster Data Model Cell Value Cell Size Cell Depth Raster Bands Spatial Reference Satellite Images Landsat SPOT GeoEye and Digital Globe Terra Satellite 73 Box 4.1 Data Volumes of High Resolution (SPOT 5) and Very High Resolution (IKONOS) Satellite Images SAR Digital Elevation Models Optical Sensors 74 Box 4.2 An Example of a Digital Elevation Model InSAR LiDAR 75

4 vi Contents 4.4 Other Types of Raster Data 76 A.A.I Digital Orthophotos Land Cover Data Bi-Level Scanned Files 76 AAA Digital Raster Graphics Graphic Files GIS Software-Specific Raster Data Raster Data Structure Cell-by-Cell Encoding Run-Length Encoding Quadtree Header File 80 Box 4.3 A Header File Example 81 A.6 Raster Data Compression 81 Box 4.4 A Simple Wavelet Example: The Haar Wavelet Data Conversion and Integration Rasterization 83 A.1.2 Vectorization 83 Box 4.5 Digital Earth 84 A.7.3 Integration of Raster and Vector Data 84 Key Concepts and Terms 85 Review Questions 86 Applications: Raster Data Model 86 Task 1 View and Import DEM Data 86 Task 2 View a Satellite Image 87 Task 3 View a Land Cover Image Metadata Conversion of Existing Data Direct Translation Neutral Format Creation of New Data Remotely Sensed Data Field Data 99 Box 5.3 An Example of GPS Data 100 Box 5.4 GPS Tracker 101 Box 5.5 GPS and OpenStreetMap Text Files with x-, y-coordinates 102 Box 5.6 Positional Accuracy ofgps Units and GPS-Enabled Mobile Phones Digitizing Using a Digitizing Table Scanning 104 5A.6 On-Screen Digitizing Importance of Source Maps 107 Key Concepts and Terms 108 Review Questions 109 Applications: GIS Data Acquisition 110 Task 1 Download USGS DEM 110 Task 2 Digitize On-Screen 110 Task 3 Add XY Data 112 Task 4 Download KML File and Display it in Google Earth 112 Challenge References 113 Task 112 Task 4 Convert Vector Data to Raster Data 88 Challenge Task 88 References 89 CHAPTER 6 CHAPTER 5 GIS Data Acquisition Existing GIS Data Spatial Data Infrastructure, Clearinghouse, and Geoportal U.S. Geological Survey 92 Box 5.1 An Application Example oflidar DEM 94 Box 5.2 Data Formats for USGS Products U.S. Census Bureau Natural Resources Conservation Service Examples of Statewide, Metropolitan, and County-Level Data GIS Data from Other Sources 96 Geometric Transformation Geometric Transformation Map-to-Map and Image-to-Map Transformation Transformation Methods Affine Transformation Control Points 117 Box 6.1 Estimation of Transformation Coefficients 118 Box 6.2 Outputfrom an Affine Transformation Root Mean Square (RMS) Error 119 Box 6.3 RMSfrom an Affine Transformation Interpretation of RMS Errors on Digitized Maps 121

5 vii 6.4 Resampling of Pixel Values Resampling Methods 122 Box 6.4 Computation for Bilinear Interpolation Other Uses of Resampling 123 Box 6.5 Pyramiding in Image Processing 124 Key Concepts and Terms 124 Review Questions 124 Applications: Geometric Transformation 125 Task 1 Georeference and Rectify a Scanned Map 125 Task 2 Vectorize Raster Lines 126 Task 3 Perform Image-to-Map Transformation 127 Challenge Task 129 References 129 CHAPTER 7 Spatial Data Accuracy and Quality Location Errors Location Errors Using Secondary Data Sources Causes of Digitizing Errors Location Errors Using Primary Data Sources Spatial Data Accuracy Standards Topological Errors 133 Box 7.1 National Standardfor Spatial Data Accuracy Statistic Topological Errors with Spatial Features Topological Errors between Layers 135 Box 7.2 Radius Topology Topological Editing Cluster Tolerance and Snapping Tolerance 136 Box 7.3 Cluster Tolerance Editing Using Map Topology Editing Using Topology Rules Nontopological Editing Editing Existing Features Creating Features from Existing Features Other Editing Operations Edgematching Line Simplification and Smoothing 140 Key Concepts and Terms 141 Review Questions 142 Applications: Spatial Data Accuracy and Quality 143 Task 1 Edit a Shapefile 143 Task 2 Use Cluster Tolerance to Fix Digitizing Errors Between Two Shapefiles 144 Task 3 Use Topology Rule to Fix Dangles 145 Task 4 Use Topology Rule to Ensure Two Polygon Layers Covering Each Other 147 Challenge Task 147 References 148 CHAPTER 8 Attribute Data Management Attribute Data in GIS Types of Attribute Tables Database Management 151 Box 8.1 Spatial Database Management System 152 Box 8.2 Selection of Numeric Data Type Types of Attribute Data 152 Box 8.3 What Is BLOB? The Relational Model SSURGO: A Relational Database Example Normalization Types of Relationships Joins, Relates, and Relationship Classes Joins Relates Relationship Classes Attribute Data Entry Field Definition Methods of Data Entry Attribute Data Verification Manipulation of Fields and Attribute Data Adding and Deleting Fields Classification of Attribute Data Computation of Attribute Data 162 Key Concepts and Terms 162 Review Questions 163 Applications: Attribute Data Management 164 Task 1 Use Validation Rule for Entering Attribute Data 164 Task 2 Join Tables 165 Task 3 Relate Tables 165 Task 4 Create New Attribute by Data Classification 166

6 viii Contents Task 5 Use Advanced Method for Attribute Data Classification 166 CHAPTER 1 0 Task 6 Create New Attribute by Data Computation 167 Task 7 Create Relationship Class 167 Challenge Task 168 References 168 CHAPTER 9 Data Display and Cartography Cartographic Representation Spatial Features and Map Symbols Use of Color 173 Box 9.1 Choice ofmap Symbols in Google My Maps Data Classification Generalization Types of Quantitative Maps 176 Box 9.2 Locating Dots on a Dot Map 178 Box 9.3 Mapping Derived and Absolute Values Typography Type Variations Selection of Type Variations Placement of Text in the Map Body 181 Box 9.4 Optionsfor Dynamic Labeling 181 Box 9.5 Better Mapping Campaign Map Design Layout Visual Hierarchy Animated Maps 188 Box 9.6 Working with Soft-Copy Maps Map Production 189 Box 9.7A Web Tool for Making Color Maps 190 Key Concepts and Terms 190 Review Questions 192 Applications: Data Display and Cartography 192 Task 1 Make a Choropleth Map 192 Task 2 Use Graduated Symbols, Line Symbols, Highway Shield Symbols, and Text Symbols 195 Task 3 Label Streams 198 Challenge Task 199 References 199 Data Exploration Data Exploration 202 Box 10.1 Data Visualization Descriptive Statistics 202 Box 10.2 Descriptive Statistics Graphs Dynamic Graphics Map-Based Data Manipulation Data Classification 207 Box 10.3 Geovisualization and Geovisual Analytics Spatial Aggregation Map Comparison Attribute Data Query SQL (Structured Query Language) 210 Box 10.4 SQL for Attribute Data Query Query Expressions Type of Operation Examples of Query Operations Relational Database Query Spatial Data Query Feature Selection by Cursor Feature Selection by Graphic Feature Selection by Spatial Relationship Combining Attribute and Spatial Data Queries 215 Box 10.5 Expressions of Spatial Relationships Spatial Join Raster Data Query Query by Cell Value Query by Select Features 218 Key Concepts and Terms 218 Review Questions 219 Applications: Data Exploration 220 Task 1 Select Features by Location 220 Task 2 Make Dynamic Chart 221 Task 3 Query Attribute Data from a Joint Table 221 Task 4 Query Attribute Data from a Relational Database 222 Task 5 Combine Spatial and Attribute Data Queries 223 Task 6 Perform Spatial Join 223

7 ix Task 7 Query Raster Data 224 Challenge Task 224 References 225 CHAPTER 1 1 Vector Data Analysis Buffering Variations in Buffering 227 Box 11.1 Riparian Buffer Width Applications of Buffering 229 Box 11.2 Buffer Zones for Analysis of Food Deserts 230 Box 11.3 Buffer Zones as Indicators of Positional Accuracy Overlay Feature Type and Overlay Overlay Methods Overlay and Data Format 232 Box 11.4 Difference between Overlay and Spatial Join Slivers Error Propagation in Overlay 234 Box 11.5 Error Propagation Models Applications of Overlay Distance Measurement Pattern Analysis 236 Box 11.6 Distance Measures for Assessing Positional Accuracy Analysis of Random and Nonrandom Patterns Moran's I for Measuring Spatial Autocorrelation G-Statistic for Measuring High/Low Clustering 240 Box 11.7 Detection ofdrug Hotspots Applications of Pattern Analysis Feature Manipulation 242 Key Concepts and Terms 244 Review Questions 246 Applications: Vector Data Analysis 246 Task 1 Perform Buffering and Overlay 246 Task 2 Overlay Multicomponent Polygons 248 Task 3 Perform Areal Interpolation 248 Task 4 Compute General and Local G-Statistics 249 Task 5 Perform Select and Clip 250 Challenge References 251 Task 251 CHAPTER 1 2 Raster Data Analysis Data Analysis Environment Local Operations 255 Box 12.1 How to Make an Analysis Mask Local Operations with a Single Raster Reclassification Local Operations with Multiple Rasters Applications oflocal Operations 257 Box 12.2 A Case Study ofrusle Neighborhood Operations Neighborhood Statistics Applications of Neighborhood Operations 259 Box 12.3 More Examples of Neighborhood Operations Zonal Operations Zonal Statistics Applications of Zonal Operations 262 Box 12.4 An Application of Zonal Operations Physical Distance Measure Operations Allocation and Direction Applications of Physical Distance Measure Operations 264 Box 12.5 Limitations of Physical Distance Measures Other Raster Data Operations Raster Data Management Raster Data Extraction Raster Data Generalization Map Algebra Comparison of Vector- and Raster-Based Data Analysis Overlay 267 Box 12.6 A Case for Raster-Based Overlay Buffering 268 Key Concepts and Terms 269 Review Questions 2<59 Applications: Raster Data Analysis 270 Task 1 Perform a Local Operation 270 Task 2 Perform a Combine Operation 270 Task 3 Perform a Neighborhood Operation 271 Task 4 Perform a Zonal Operation 271 Task 5 Measure Physical Distances 277

8 x Contents Task 6 Perform Extract by Attributes and by Mask 272 Challenge Task 272 References 273 CHAPTER 1 3 Terrain Mapping and Analysis Data for Terrain Mapping and Analysis DEM TIN Terrain Mapping Contouring Vertical Profiling Hill Shading Hypsometric Tinting 275 Box 13.1 The Pseudoscopic Effect 279 Box 13.2 A Worked Example of Computing Relative Radiance Perspective View Slope and Aspect Computing Algorithms for Slope and Aspect Using Raster 282 Box 13.3 Methods of Slope Measurement in the Field 283 Box 13.4 Conversion ofd to Aspect 284 Box 13.5 A Worked Example of Computing Slope and Aspect Using Raster Computing Algorithms for Slope and Aspect Using TIN Factors Influencing Slope and Aspect Measures 285 Box 13.6A Worked Example of Computing Slope and Aspect Using TIN Surface Curvature 287 Box 13.7A Worked Example of Computing Surface Curvature Raster Versus TIN 288 Key Concepts and Terms 289 Review Questions 290 Applications: Terrain Mapping and Analysis 291 Task 1 Use DEM for Terrain Mapping 291 Task 2 Derive Slope, Aspect, and Curvature from DEM 292 Task 3 Build and Display a TIN 294 Challenge Task 294 References 295 CHAPTER 14 vlewshed and watershed Analysis Viewshed Analysis Line-of-Sight Operation Raster-Based Viewshed Analysis TIN-Based Viewshed Analysis Cumulative Viewshed 299 Box 14.1 An Application Example of Cumulative Viewshed Accuracy of Viewshed Analysis Parameters of Viewshed Analysis 301 Box 14.2 Tools for Selecting Viewpoints Applications of Viewshed Analysis 303 HA Watershed Analysis 303 Box 14.3 HydroSHEDS 304 Box 14.4 Watershed Boundary Dataset (WBD) Filled DEM Flow Direction Flow Accumulation 305 HAA Stream Network Stream Links Areawide Watersheds Point-Based Watersheds 307 Box 14.5 Snapping Pour Points Factors Influencing Watershed Analysis DEM Resolution Flow Direction Flow Accumulation Threshold Applications of Watershed Analysis 312 Key Concepts and Terms 313 Review Questions 313 Applications: Viewsheds and Watersheds 314 Task 1 Perform Viewshed Analysis 314 Task 2 Create a New Lookout Shapefile for Viewshed Analysis 315 Task 3 Delineate Areawide Watersheds 316 Task 4 Derive Upstream Contributing Areas at Pour Points 317 Challenge Task 318 References 318

9 xi CHAPTER 1 5 CHAPTER 1 6 Spatial Interpolation Elements of Spatial Interpolation Control Points Type of Spatial Interpolation Global Methods Trend Surface Models 323 Box 15.1 A Worked Example of Trend Surface Analysis Regression Models Local Methods Thiessen Polygons Density Estimation Inverse Distance Weighted Interpolation 328 Box 15.2 A Worked Example of Kernel Density Estimation 329 Box 15.3 A Worked Example of Inverse Distance Weighted Estimation Thin-Plate Splines 330 Box 15.4 Radial Basis Functions Kriging 331 Box 15.5 A Worked Example of Thin-Plate Splines with Tension Semivariogram Models Ordinary Kriging 335 Box 15.6 A Worked Example of Ordinary Kriging Estimation Universal Kriging Other Kriging Methods 338 Box 15.7 A Worked Example of Universal Kriging Estimation Comparison of Spatial Interpolation Methods 340 Key Concepts and Terms 341 Review Questions 342 Applications: Spatial Interpolation 343 Task 1 Use Trend Surface Model forinterpolation 343 Task 2 Compute Kernel Density Estimation 344 Task 3 Use IDW for Interpolation 345 Task 4 Use Ordinary Kriging for Interpolation 345 Task 5 Use Universal Kriging for Interpolation 347 Challenge Task 347 References 348 Geocoding and Dynamic Segmentation Geocoding Geocoding Reference Database The Address Matching Process 351 Box 16.1 Positional Accuracy ofroad Networks in TIGER/Line Files 352 Box 16.2 Map Reporter Address Matching Options Offset Plotting Options 354 Box 16.3 Scoring System for Geocoding Quality of Geocoding Variations of Geocoding 355 Box 16.4 Online Geocoding Services Applications of Geocoding Location-Based Services Business Applications Wireless Emergency Services Crime Mapping and Analysis Public Health Dynamic Segmentation Routes Creating Routes 358 Box 16.5 Route Feature Classes Events Creating Event Tables Applications of Dynamic Segmentation Data Management Data Display Data Query Data Analysis 363 Key Concepts and Terms 363 Review Questions 364 Applications: Geocoding and Dynamic Segmentation 364 Task 1 Geocode Street Addresses 365 Task 2 Display and Query Routes and Events 366 Task 3 Analyze Two Event Layers 366 Task 4 Create a Stream Route and Analyze Slope Along the Route 367 Task 5 Locate Cities Along an Interstate Route 368

10 xii Contents Task 6 Check the Quality of TIGER/Line Files 369 Challenge Task 369 References 369 CHAPTER 1 7 Review Questions 388 Applications: Path Analysis and Network Applications 388 Task 1 Compute the Least Accumulative Cost Distance 388 Task 2 Compute the Path Distance 389 Task 3 Run Shortest Path Analysis 390 Task 4 Build a Geodatabase Network Dataset 391 Task 5 Find Closest Facility 392 Least-Cost Path Analysis and Network Analysis Least-Cost Path Analysis 373 Task 6 Find Service Area 393 Challenge Task 393 References Source Raster Cost Raster 373 Box 17.1 Cost Raster for a Site Analysis of Pipelines Cost Distance Measures Deriving the Least Accumulative Cost Path 374 Box 17.2 Derivation of the Least Accumulative Cost Path Options for Least-Cost Path Analysis Applications of Least-Cost Path Analysis Network Link and Link Impedance Junction and Turn Impedance Restrictions 379 MA Assembly of a Network Gathering Linear Features Editing and Building Network 379 Box 17.3 Routing Network for Disabled People 379 Box 17.4 Network Dataset Attributing the Network Features Network Analysis Shortest Path Analysis 381 Box 17.5 Accessibility Analysis in Food Desert Studies Traveling Salesman Problem Vehicle Routing Problem Closest Facility Allocation 384 Box 17.6 Response Time to Fires Location-Allocation 385 Key Concepts and Terms 387 CHAPTER 1 8 GIS Models and Modeling Basic Elements of GIS Modeling Classification of GIS Models The Modeling Process The Role of GIS in Modeling 398 Box 18.1 GIS and Location Modeling Integration of GIS and Other Modeling Programs Binary Models Vector-B ased Method Raster-Based Method Applications of Binary Models 401 Box 18.2 The Conservation Reserve Program Index Models The Weighted Linear Combination Method Other Index Methods Applications of the Index Model 406 Box 18.3 The Land Evaluation and Site Assessment System Regression Models Linear Regression Models Local Regression Models Logistic Regression Models Process Models Revised Universal Soil Loss Equation Critical Rainfall Model 410 Key Concepts and Terms 411

11 xiii Review Questions 411 Applications: GIS Models and Modeling 412 Task 1 Build a Vector-Based Binary Model 412 Task 2 Build a Raster-Based Binary Model 413 Task 3 Build a Vector-Based Index Model 414 Task 4 Build a Raster-Based Index Model 415 Challenge Task 416 References 416 Index 420