Spatial analysis methods and practice : describe - explore - explain through GIS /
George Grekousis.
Bok Engelsk 2020 · Electronic books.
| Omfang | 1 online resource (xv, 518 pages) : : digital, PDF file(s).
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| Opplysninger | Title from publisher's bibliographic system (viewed on 20 May 2020).. - Cover -- Half-title -- Reviews -- Title page -- Copyright information -- Contents -- Preface -- Theory -- Lab -- 1 Think Spatially: Basic Concepts of Spatial Analysis and Space Conceptualization -- Theory -- Learning Objectives -- 1.1 Introduction: Spatial Analysis -- Why Conduct Spatial Analysis? -- Spatial Analysis Workflow -- 1.2 Basic Definitions -- Definitions -- 1.3 Spatial Data: What Makes Them Special? -- 1.4 Conceptualization of Spatial Relationships -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.5 Distance Measure -- Definitions and Formulas -- 1.5.1 Fixed Distance Band (Sphere of Influence) -- Definition -- Why Use -- Interpretation -- Tools for Determining Distance Band -- Discussion and Practical Guidelines -- 1.5.2 Distance Decay -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- Example -- 1.6 Contiguity: Adjacency Matrix -- 1.6.1 Polygons Contiguity -- Definition -- Why Use -- Discussion and Practical Guidelines -- 1.6.2 Adjacency Matrix -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.7 Interaction -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.8 Neighborhood and Neighbors -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.8.1 k-Nearest Neighbors (k-NN) -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.8.2 Space-Time Window -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.8.3 Proximity Polygons -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.8.4 Delaunay Triangulation and Triangular Irregular Networks (TIN) -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.9 Spatial Weights and Row Standardization.. - Dealing with Multiple Comparisons Problem and Spatial Dependence.. - Definitions -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 1.10 Chapter Concluding Remarks -- Questions and Answers -- Lab 1 The Project: Spatial Analysis for Real Estate Market Investments -- Overall Progress -- Scope of Analysis -- Data -- Tasks -- Dataset Structure -- Guidelines -- Section A ArcGIS -- Section B GeoDa -- 2 Exploratory Spatial Data Analysis Tools and Statistics -- Theory -- Learning Objectives -- 2.1 Introduction in Exploratory Spatial Data Analysis, Descriptive Statistics, Inferential Statistics and Spatial Statistics -- Definitions -- Why Use Descriptive Statistics and ESDA -- Why Use Spatial Statistics -- 2.2 Simple ESDA Tools and Descriptive Statistics for Visualizing Spatial Data (Univariate Data) -- 2.2.1 Choropleth Maps -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.2.2 Frequency Distribution and Histograms -- Definitions -- Why Use -- Discussion and Practical Guidelines -- 2.2.3 Measures of Center -- Definitions -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.2.4 Measures of Shape -- Definitions -- Why Use -- Interpretation -- 2.2.5 Measures of Spread/Variability - Variation -- Definitions -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.2.6 Percentiles, Quartiles and Quantiles -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.2.7 Outliers -- Definition -- Why Use -- Interpretation (How to Trace Outliers) -- Discussion and Practical Guidelines -- 2.2.8 Boxplot -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.2.9 Normal QQ Plot -- Definition -- Why Use -- Interpretation -- 2.3 ESDA Tools and Descriptive Statistics for Analyzing Two or More Variables (Bivariate Analysis) -- 2.3.1 Scatter Plot -- Definition -- Why Use -- Interpretation.. - Discussion and Practical Guidelines -- 2.3.2 Scatter plot matrix -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.3.3 Covariance and Variance-Covariance Matrix -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.3.4 Correlation Coefficient -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.3.5 Pairwise Correlation -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.3.6 General QQ plot -- Definition -- Why Use -- Interpretation -- 2.4 Rescaling Data -- Definition -- Why Use -- Methods -- Discussion and Practical Guidelines (Normalization vs. Standardization) -- 2.5 Inferential Statistics and Their Importance in Spatial Statistics -- Definition -- Why Use -- Importance to Spatial Statistics -- 2.5.1 Parametric Methods -- Definitions -- How Parametric Methods Work -- Discussion and Practical Guidelines -- 2.5.2 Nonparametric Methods -- Definition -- Why Use -- Discussion and Practical Guidelines -- 2.5.3 Confidence Interval -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.5.4 Standard Error, Standard Error of the Mean, Standard Error of Proportion and Sampling Distribution -- Definitions -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 2.5.5 Significance Tests, Hypothesis, p-Value and z-Score -- Definition -- Interpretation -- Discussion and Practical Guidelines -- Example in a Geographical Context -- 2.6 Normal Distribution Use in Geographical Analysis -- Importance to Spatial Analysis -- How to Identify a Normal Distribution -- What to Do When Distribution Is Not Normal -- 2.7 Chapter Concluding Remarks -- Questions and Answers -- Lab 2 Exploratory Spatial Data Analysis (ESDA): Analyzing and Mapping Data -- Overall Progress.. - Scope of the Analysis: Crime Analysis -- 4 Spatial Autocorrelation -- Theory -- Learning Objectives -- 4.1 Spatial Autocorrelation -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.2 Global Spatial Autocorrelation -- 4.2.1 Moran's I Index and Scatter Plot -- Definition -- Why Use -- Interpretation and Moran's I Scatter Plot -- Discussion and Practical Guidelines -- 4.2.2 Geary's C Index -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.2.3 General G-Statistic -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.3 Incremental Spatial Autocorrelation -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.4 Local Spatial Autocorrelation -- 4.4.1 Local Moran's I (Cluster and Outlier Analysis) -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- Potential Case Studies Include -- 4.4.2 Optimized Outlier Analysis -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.4.3 Getis-Ord Gi and Gi* (Hot Spot Analysis) -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.4.4 Optimized Hot Spot Analysis -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.5 Space-Time Correlation Analysis -- 4.5.1 Bivariate Moran's I for Space-Time Correlation -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.5.2 Differential Moran's I -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.5.3 Emerging Hot Spot Analysis -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 4.6 Multiple Comparisons Problem and Spatial Dependence -- Multiple Comparisons Problem -- Spatial Dependency.. - Scope of the Analysis: Income and Expenses -- Section A ArcGIS -- Section B GeoDa -- 3 Analyzing Geographic Distributions and Point Patterns -- Theory -- Learning Objectives -- 3.1 Analyzing Geographic Distributions: Centrography -- 3.1.1 Mean Center -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.1.2 Median Center -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.1.3 Central Feature -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.1.4 Standard Distance -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.1.5 Standard Deviational Ellipse -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.1.6 Locational Outliers and Spatial Outliers -- Definition: Locational Outlier -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- Definition: Spatial Outlier -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.2 Analyzing Spatial Patterns: Point Pattern Analysis -- Definitions -- Why Use -- Discussion and Practical Guidelines -- 3.2.1 Definitions: Spatial Process, Complete Spatial Randomness, First- and Second-Order Effects -- Definition -- 3.2.2 Spatial Process -- 3.3 Point Pattern Analysis Methods -- 3.3.1 Nearest Neighbor Analysis -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.3.2 Ripley's K Function and the L Function Transformation -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.3.3 Kernel Density Function -- Definition -- Why Use -- Interpretation -- Discussion and Practical Guidelines -- 3.4 Chapter Concluding Remarks -- Questions and Answers -- Lab 3 Spatial Statistics: Measuring Geographic Distributions -- Overall Progress.. - This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.
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| Emner | |
| Sjanger | |
| Dewey | |
| ISBN | 1-108-61452-3. - 9781108614528
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