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Full Description
An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis-containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS-as well as detailed illustrations and numerous case studies. The book enables readers to:Identify types and characterize non-spatial and spatial dataDemonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and resultsConstruct testable hypotheses that require inferential statistical analysisProcess spatial data, extract explanatory variables, conduct statistical tests, and explain resultsUnderstand and interpret spatial data summaries and statistical testsSpatial Analysis: Statistics, Visualization, and Computational Methods incorporates traditional statistical methods, spatial statistics, visualization, and computational methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Topics covered include: spatial descriptive methods, hypothesis testing, spatial regression, hot spot analysis, geostatistics, spatial modeling, and data science.
Contents
The Context and Relevance of Spatial AnalysisFrom Data to Information, to Knowledge and WisdomSpatial Analysis Using a GIS TimelineGeographic Data: Properties, Strengths, and AnalyticalChallengesConclusionChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesScientific Observations and Measurements in Spatial AnalysisScales of MeasurementPopulation and SampleConclusionChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesUsing Statistical Measures to Analyze Data DistributionsDescriptive StatisticsSpatial Statistics: Measures for Describing Basic Characteristics of Spatial DataSpatial Measures of Central TendencySpatial Measures of DispersionRandom Variables and Probability DistributionConclusionChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesExploratory Data Analysis, Visualization, and Hypothesis TestingExploratory Data Analysis, Geovisualization, and DataVisualization MethodsExploratory Approaches for Visualizing Spatial DatasetsVisualizing Multidimensional Datasets: An Illustration Based on the US Educational Achievements Rates, 1970-2012Hypothesis Testing, Confidence Intervals, and p ValuesComputationStatistical ConclusionConclusionChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesAnalyzing Spatial Statistical RelationshipsEngaging in Correlation AnalysisOrdinary Least Squares and Geographically Weighted RegressionMethodsConclusionChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesEngaging in Point Pattern AnalysisRationale for Studying Point Patterns and DistributionsExploring Patterns, Distributions, and Trends Associated with Point FeaturesConclusionsChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesEngaging in Areal Pattern Analysis Using Global and LocalStatisticsRationale for Studying Areal PatternsThe Notion of Spatial RelationshipsQuantifying Spatial Autocorrelation Effects in Areal PatternsConclusionsChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesEngaging in Geostatistical AnalysisRationale for Using Geostatistics to Study Complex SpatialPatternsBasic Interpolation EquationsSpatial Structure Functions for Regionalized VariablesKriging Method and its Theoretical FrameworkConditional Geostatistical SimulationInverse Distance WeightingConclusionsChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferencesData Science: Understanding Computing Systems and Analytics for Big DataIntroduction to Data ScienceRationale for a Big Geospatial Data FrameworkData ManagementAnalytics and Strategies for Big Geospatial DataConclusionsChallenge AssignmentsReview and Study QuestionsGlossary of Key TermsReferences
