Spatial analysis with R statistics, visualization, and computational methods
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Boca Raton ; London ; New York
CRC Press
[2021]
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| Ausgabe: | Second edition |
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| 100 | 1 | |a Oyana, Tonny J. |e Verfasser |0 (DE-588)1082383678 |4 aut | |
| 245 | 1 | 0 | |a Spatial analysis with R |b statistics, visualization, and computational methods |c Tonny J. Oyana |
| 250 | |a Second edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press |c [2021] | |
| 264 | 4 | |c © 2021 | |
| 300 | |a 1 Online-Ressource (xix, 333 Seiten) |b Illustrationen, Karten | ||
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| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- Author -- 1. Understanding the Context and Relevance of Spatial Analysis -- Learning Objectives -- Introduction -- From Data to Information, to Knowledge, and Wisdom -- Spatial Analysis Using a GIS Timeline -- Spatial Analysis in the Post-1990s Period -- Data Science, GIS, and Artificial Intelligence -- Geographic Data: Properties, Strengths, and Analytical Challenges -- Concept of Scale -- Concept of Spatial Dependency -- Concept of Spatial Proximity -- Modifiable Areal Unit Problem -- Concept of Spatial Autocorrelation -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Working with R -- Getting Started -- Working with Spatial Data -- Tips for Working with R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 2. Making Scientific Observations and Measurements in Spatial Analysis -- Learning Objectives -- Introduction -- Scales of Measurement -- Nominal Scale -- Ordinal Scale -- Interval Scale -- Ratio Scale -- Two Main Approaches for Data Collection That Involve Deductive and Inductive Reasoning -- Population and Sample -- Spatial Sampling -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Step I. View Data Structure -- Step II. Basic Data Summaries -- Step III. Exploring the Spatial Data -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 3. Using Statistical Measures to Analyze Data Distributions -- Learning Objectives -- Introduction -- Descriptive Statistics -- Measures of Central Tendency -- Deriving a Weighted Mean Using the Frequency Distributions in a Set of Observations -- Measures of Dispersion | |
| 505 | 8 | |a Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data -- Spatial Measures of Central Tendency -- Spatial Measures of Dispersion -- Random Variables and Probability Distribution -- Random Variable -- Probability and Theoretical Data Distributions -- Concepts and Applications -- Binomial Distribution -- Poisson Distribution -- Normal Distribution -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Exploring Z-Score to Assess the Relative Position in Data Distributions Using R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 4. Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing -- Learning Objectives -- Introduction -- Exploratory Data Analysis, Geovisualization, and Data Visualization Methods -- Data Visualization -- Geographic Visualization -- New Stunning Visualization Tools and Infographics -- Exploratory Approaches for Visualizing Spatial Datasets -- Visualizing Multidimensional Datasets: An Illustration Based on U.S. Educational Achievements Rates, 1970-2012 -- Hypothesis Testing, Confidence Intervals, and .p.-Values -- Computation -- Statistical Conclusion -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Generating Graphical Data Summaries -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 5. Understanding Spatial Statistical Relationships -- Learning Objectives -- Engaging in Correlation Analysis -- Ordinary Least Squares and Geographically Weighted Regression Methods -- Procedures in Developing a Spatial Regression Model -- Examining Relationships between Regression Variables | |
| 505 | 8 | |a Examining the Strength of Association and Direction of All Paired Variables Using a Scatterplot Matrix -- Fitting the Ordinary Least Squares Regression Model -- Primary Model -- Examining Variance Inflation Factor Results -- Reduced Model -- Best Model -- Examining Residual Changes in Ordinary Least Squares Regression Models -- Fitting the Geographically Weighted Regression Model -- Examining Residual Change and Effects of Predictor Variables on Local Areas -- Summary of Modeling Result -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 6. Engaging in Point Pattern Analysis -- Learning Objectives -- Introduction -- Rationale for Studying Point Patterns and Distributions -- Exploring Patterns, Distributions, and Trends Associated with Point Features -- Quadrat Count -- Nearest Neighbor Approach -- K-Function Approach -- Kernel Estimation Approach -- Constructing a Voronoi Map from Point Features -- Exploring Space-Time Patterns -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Explore Potential Path Area and Activity Space Concepts -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 7. Engaging in Areal Pattern Analysis Using Global and Local Statistics -- Learning Objectives -- Rationale for Studying Areal Patterns -- The Notion of Spatial Relationships -- Quantifying Spatial Autocorrelation Effects in Areal Patterns -- Join Count Statistics -- Interpreting the Join Count Statistics and Methodological Flaws -- Global Moran's I Coefficient of Spatial Autocorrelation -- Interpreting Moran's I and Methodological Flaws -- Global Geary's C Coefficient of Spatial Autocorrelation | |
| 505 | 8 | |a Interpreting Geary's C and Methodological Flaws -- Getis-Ord G Statistics -- Interpretation of Getis-Ord G and Methodological Flaws -- Local Moran's I -- Local G-Statistic -- Local Geary -- Using Scatterplots to Synthesize and Interpret Local Indicators of Spatial Association Statistics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Quiz -- Review and Study Questions -- Glossary of Key Terms -- References -- 8. Engaging in Geostatistical Analysis -- Learning Objectives -- Introduction -- Rationale for Using Geostatistics to Study Complex Spatial Patterns -- Basic Interpolation Equations -- Spatial Structure Functions for Regionalized Variables -- Kriging Method and Its Theoretical Framework -- Simple Kriging -- Ordinary Kriging -- Universal Kriging -- Indicator Kriging -- Key Points to Note about the Geostatistical Estimation Using Kriging -- Exploratory Data Analysis -- Spatial Prediction and Modeling -- Uncertainty Analysis -- Conditional Geostatistical Simulation -- Inverse Distance Weighting -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 9. Data Science: Understanding Computing Systems and Analytics for Big Data -- Learning Objectives -- Introduction to Data Science -- Rationale for a Big Geospatial Data Framework -- Data Management -- Data Warehousing -- Data Sources, Processing Tools, and the Extract-Transform-Load Process -- Data Integration and Storage -- Data-Mining Algorithms for Big Geospatial Data -- Tools, Algorithms, and Methods for Data Mining and Actionable Knowledge -- Business Intelligence, Spatial Online Analytical Processing, and Analytics -- Analytics and Strategies for Big Geospatial Data -- Spatiotemporal Data Analytics | |
| 505 | 8 | |a Classification Algorithms for Detecting Clusters in Big Geospatial Data -- Embedding Solutions/Algorithm with Topological Considerations -- Graph and Text Analytics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- Index | |
| 650 | 4 | |a Spatial analysis (Statistics) | |
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| 776 | 0 | 8 | |i Erscheint auch als |a Oyana, Tonny J. |t Spatial Analysis with R |d Milton : Taylor & Francis Group,c2020 |n Druck-Ausgabe, Hardcover |z 978-0-367-86085-1 |
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Datensatz im Suchindex
| _version_ | 1819311355182710784 |
|---|---|
| any_adam_object | |
| author | Oyana, Tonny J. |
| author_GND | (DE-588)1082383678 |
| author_facet | Oyana, Tonny J. |
| author_role | aut |
| author_sort | Oyana, Tonny J. |
| author_variant | t j o tj tjo |
| building | Verbundindex |
| bvnumber | BV047441910 |
| classification_rvk | RB 10104 |
| collection | ZDB-30-PQE |
| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- Author -- 1. Understanding the Context and Relevance of Spatial Analysis -- Learning Objectives -- Introduction -- From Data to Information, to Knowledge, and Wisdom -- Spatial Analysis Using a GIS Timeline -- Spatial Analysis in the Post-1990s Period -- Data Science, GIS, and Artificial Intelligence -- Geographic Data: Properties, Strengths, and Analytical Challenges -- Concept of Scale -- Concept of Spatial Dependency -- Concept of Spatial Proximity -- Modifiable Areal Unit Problem -- Concept of Spatial Autocorrelation -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Working with R -- Getting Started -- Working with Spatial Data -- Tips for Working with R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 2. Making Scientific Observations and Measurements in Spatial Analysis -- Learning Objectives -- Introduction -- Scales of Measurement -- Nominal Scale -- Ordinal Scale -- Interval Scale -- Ratio Scale -- Two Main Approaches for Data Collection That Involve Deductive and Inductive Reasoning -- Population and Sample -- Spatial Sampling -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Step I. View Data Structure -- Step II. Basic Data Summaries -- Step III. Exploring the Spatial Data -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 3. Using Statistical Measures to Analyze Data Distributions -- Learning Objectives -- Introduction -- Descriptive Statistics -- Measures of Central Tendency -- Deriving a Weighted Mean Using the Frequency Distributions in a Set of Observations -- Measures of Dispersion Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data -- Spatial Measures of Central Tendency -- Spatial Measures of Dispersion -- Random Variables and Probability Distribution -- Random Variable -- Probability and Theoretical Data Distributions -- Concepts and Applications -- Binomial Distribution -- Poisson Distribution -- Normal Distribution -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Exploring Z-Score to Assess the Relative Position in Data Distributions Using R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 4. Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing -- Learning Objectives -- Introduction -- Exploratory Data Analysis, Geovisualization, and Data Visualization Methods -- Data Visualization -- Geographic Visualization -- New Stunning Visualization Tools and Infographics -- Exploratory Approaches for Visualizing Spatial Datasets -- Visualizing Multidimensional Datasets: An Illustration Based on U.S. Educational Achievements Rates, 1970-2012 -- Hypothesis Testing, Confidence Intervals, and .p.-Values -- Computation -- Statistical Conclusion -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Generating Graphical Data Summaries -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 5. Understanding Spatial Statistical Relationships -- Learning Objectives -- Engaging in Correlation Analysis -- Ordinary Least Squares and Geographically Weighted Regression Methods -- Procedures in Developing a Spatial Regression Model -- Examining Relationships between Regression Variables Examining the Strength of Association and Direction of All Paired Variables Using a Scatterplot Matrix -- Fitting the Ordinary Least Squares Regression Model -- Primary Model -- Examining Variance Inflation Factor Results -- Reduced Model -- Best Model -- Examining Residual Changes in Ordinary Least Squares Regression Models -- Fitting the Geographically Weighted Regression Model -- Examining Residual Change and Effects of Predictor Variables on Local Areas -- Summary of Modeling Result -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 6. Engaging in Point Pattern Analysis -- Learning Objectives -- Introduction -- Rationale for Studying Point Patterns and Distributions -- Exploring Patterns, Distributions, and Trends Associated with Point Features -- Quadrat Count -- Nearest Neighbor Approach -- K-Function Approach -- Kernel Estimation Approach -- Constructing a Voronoi Map from Point Features -- Exploring Space-Time Patterns -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Explore Potential Path Area and Activity Space Concepts -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 7. Engaging in Areal Pattern Analysis Using Global and Local Statistics -- Learning Objectives -- Rationale for Studying Areal Patterns -- The Notion of Spatial Relationships -- Quantifying Spatial Autocorrelation Effects in Areal Patterns -- Join Count Statistics -- Interpreting the Join Count Statistics and Methodological Flaws -- Global Moran's I Coefficient of Spatial Autocorrelation -- Interpreting Moran's I and Methodological Flaws -- Global Geary's C Coefficient of Spatial Autocorrelation Interpreting Geary's C and Methodological Flaws -- Getis-Ord G Statistics -- Interpretation of Getis-Ord G and Methodological Flaws -- Local Moran's I -- Local G-Statistic -- Local Geary -- Using Scatterplots to Synthesize and Interpret Local Indicators of Spatial Association Statistics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Quiz -- Review and Study Questions -- Glossary of Key Terms -- References -- 8. Engaging in Geostatistical Analysis -- Learning Objectives -- Introduction -- Rationale for Using Geostatistics to Study Complex Spatial Patterns -- Basic Interpolation Equations -- Spatial Structure Functions for Regionalized Variables -- Kriging Method and Its Theoretical Framework -- Simple Kriging -- Ordinary Kriging -- Universal Kriging -- Indicator Kriging -- Key Points to Note about the Geostatistical Estimation Using Kriging -- Exploratory Data Analysis -- Spatial Prediction and Modeling -- Uncertainty Analysis -- Conditional Geostatistical Simulation -- Inverse Distance Weighting -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 9. Data Science: Understanding Computing Systems and Analytics for Big Data -- Learning Objectives -- Introduction to Data Science -- Rationale for a Big Geospatial Data Framework -- Data Management -- Data Warehousing -- Data Sources, Processing Tools, and the Extract-Transform-Load Process -- Data Integration and Storage -- Data-Mining Algorithms for Big Geospatial Data -- Tools, Algorithms, and Methods for Data Mining and Actionable Knowledge -- Business Intelligence, Spatial Online Analytical Processing, and Analytics -- Analytics and Strategies for Big Geospatial Data -- Spatiotemporal Data Analytics Classification Algorithms for Detecting Clusters in Big Geospatial Data -- Embedding Solutions/Algorithm with Topological Considerations -- Graph and Text Analytics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- Index |
| ctrlnum | (ZDB-30-PQE)EBC6317218 (ZDB-30-PAD)EBC6317218 (ZDB-89-EBL)EBL6317218 (OCoLC)1190857072 (DE-599)BVBBV047441910 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Geographie |
| edition | Second edition |
| format | Electronic eBook |
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Making Scientific Observations and Measurements in Spatial Analysis -- Learning Objectives -- Introduction -- Scales of Measurement -- Nominal Scale -- Ordinal Scale -- Interval Scale -- Ratio Scale -- Two Main Approaches for Data Collection That Involve Deductive and Inductive Reasoning -- Population and Sample -- Spatial Sampling -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Step I. View Data Structure -- Step II. Basic Data Summaries -- Step III. Exploring the Spatial Data -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 3. Using Statistical Measures to Analyze Data Distributions -- Learning Objectives -- Introduction -- Descriptive Statistics -- Measures of Central Tendency -- Deriving a Weighted Mean Using the Frequency Distributions in a Set of Observations -- Measures of Dispersion</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data -- Spatial Measures of Central Tendency -- Spatial Measures of Dispersion -- Random Variables and Probability Distribution -- Random Variable -- Probability and Theoretical Data Distributions -- Concepts and Applications -- Binomial Distribution -- Poisson Distribution -- Normal Distribution -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Exploring Z-Score to Assess the Relative Position in Data Distributions Using R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 4. Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing -- Learning Objectives -- Introduction -- Exploratory Data Analysis, Geovisualization, and Data Visualization Methods -- Data Visualization -- Geographic Visualization -- New Stunning Visualization Tools and Infographics -- Exploratory Approaches for Visualizing Spatial Datasets -- Visualizing Multidimensional Datasets: An Illustration Based on U.S. Educational Achievements Rates, 1970-2012 -- Hypothesis Testing, Confidence Intervals, and .p.-Values -- Computation -- Statistical Conclusion -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Generating Graphical Data Summaries -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 5. Understanding Spatial Statistical Relationships -- Learning Objectives -- Engaging in Correlation Analysis -- Ordinary Least Squares and Geographically Weighted Regression Methods -- Procedures in Developing a Spatial Regression Model -- Examining Relationships between Regression Variables</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Examining the Strength of Association and Direction of All Paired Variables Using a Scatterplot Matrix -- Fitting the Ordinary Least Squares Regression Model -- Primary Model -- Examining Variance Inflation Factor Results -- Reduced Model -- Best Model -- Examining Residual Changes in Ordinary Least Squares Regression Models -- Fitting the Geographically Weighted Regression Model -- Examining Residual Change and Effects of Predictor Variables on Local Areas -- Summary of Modeling Result -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 6. Engaging in Point Pattern Analysis -- Learning Objectives -- Introduction -- Rationale for Studying Point Patterns and Distributions -- Exploring Patterns, Distributions, and Trends Associated with Point Features -- Quadrat Count -- Nearest Neighbor Approach -- K-Function Approach -- Kernel Estimation Approach -- Constructing a Voronoi Map from Point Features -- Exploring Space-Time Patterns -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Explore Potential Path Area and Activity Space Concepts -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 7. Engaging in Areal Pattern Analysis Using Global and Local Statistics -- Learning Objectives -- Rationale for Studying Areal Patterns -- The Notion of Spatial Relationships -- Quantifying Spatial Autocorrelation Effects in Areal Patterns -- Join Count Statistics -- Interpreting the Join Count Statistics and Methodological Flaws -- Global Moran's I Coefficient of Spatial Autocorrelation -- Interpreting Moran's I and Methodological Flaws -- Global Geary's C Coefficient of Spatial Autocorrelation</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Interpreting Geary's C and Methodological Flaws -- Getis-Ord G Statistics -- Interpretation of Getis-Ord G and Methodological Flaws -- Local Moran's I -- Local G-Statistic -- Local Geary -- Using Scatterplots to Synthesize and Interpret Local Indicators of Spatial Association Statistics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Quiz -- Review and Study Questions -- Glossary of Key Terms -- References -- 8. Engaging in Geostatistical Analysis -- Learning Objectives -- Introduction -- Rationale for Using Geostatistics to Study Complex Spatial Patterns -- Basic Interpolation Equations -- Spatial Structure Functions for Regionalized Variables -- Kriging Method and Its Theoretical Framework -- Simple Kriging -- Ordinary Kriging -- Universal Kriging -- Indicator Kriging -- Key Points to Note about the Geostatistical Estimation Using Kriging -- Exploratory Data Analysis -- Spatial Prediction and Modeling -- Uncertainty Analysis -- Conditional Geostatistical Simulation -- Inverse Distance Weighting -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 9. 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| id | DE-604.BV047441910 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T08:55:12Z |
| institution | BVB |
| isbn | 9781000173451 9781003021643 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032844062 |
| oclc_num | 1190857072 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xix, 333 Seiten) Illustrationen, Karten |
| psigel | ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Oyana, Tonny J. Verfasser (DE-588)1082383678 aut Spatial analysis with R statistics, visualization, and computational methods Tonny J. Oyana Second edition Boca Raton ; London ; New York CRC Press [2021] © 2021 1 Online-Ressource (xix, 333 Seiten) Illustrationen, Karten txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- Author -- 1. Understanding the Context and Relevance of Spatial Analysis -- Learning Objectives -- Introduction -- From Data to Information, to Knowledge, and Wisdom -- Spatial Analysis Using a GIS Timeline -- Spatial Analysis in the Post-1990s Period -- Data Science, GIS, and Artificial Intelligence -- Geographic Data: Properties, Strengths, and Analytical Challenges -- Concept of Scale -- Concept of Spatial Dependency -- Concept of Spatial Proximity -- Modifiable Areal Unit Problem -- Concept of Spatial Autocorrelation -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Working with R -- Getting Started -- Working with Spatial Data -- Tips for Working with R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 2. Making Scientific Observations and Measurements in Spatial Analysis -- Learning Objectives -- Introduction -- Scales of Measurement -- Nominal Scale -- Ordinal Scale -- Interval Scale -- Ratio Scale -- Two Main Approaches for Data Collection That Involve Deductive and Inductive Reasoning -- Population and Sample -- Spatial Sampling -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Step I. View Data Structure -- Step II. Basic Data Summaries -- Step III. Exploring the Spatial Data -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 3. Using Statistical Measures to Analyze Data Distributions -- Learning Objectives -- Introduction -- Descriptive Statistics -- Measures of Central Tendency -- Deriving a Weighted Mean Using the Frequency Distributions in a Set of Observations -- Measures of Dispersion Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data -- Spatial Measures of Central Tendency -- Spatial Measures of Dispersion -- Random Variables and Probability Distribution -- Random Variable -- Probability and Theoretical Data Distributions -- Concepts and Applications -- Binomial Distribution -- Poisson Distribution -- Normal Distribution -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Exploring Z-Score to Assess the Relative Position in Data Distributions Using R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 4. Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing -- Learning Objectives -- Introduction -- Exploratory Data Analysis, Geovisualization, and Data Visualization Methods -- Data Visualization -- Geographic Visualization -- New Stunning Visualization Tools and Infographics -- Exploratory Approaches for Visualizing Spatial Datasets -- Visualizing Multidimensional Datasets: An Illustration Based on U.S. Educational Achievements Rates, 1970-2012 -- Hypothesis Testing, Confidence Intervals, and .p.-Values -- Computation -- Statistical Conclusion -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Generating Graphical Data Summaries -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 5. Understanding Spatial Statistical Relationships -- Learning Objectives -- Engaging in Correlation Analysis -- Ordinary Least Squares and Geographically Weighted Regression Methods -- Procedures in Developing a Spatial Regression Model -- Examining Relationships between Regression Variables Examining the Strength of Association and Direction of All Paired Variables Using a Scatterplot Matrix -- Fitting the Ordinary Least Squares Regression Model -- Primary Model -- Examining Variance Inflation Factor Results -- Reduced Model -- Best Model -- Examining Residual Changes in Ordinary Least Squares Regression Models -- Fitting the Geographically Weighted Regression Model -- Examining Residual Change and Effects of Predictor Variables on Local Areas -- Summary of Modeling Result -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 6. Engaging in Point Pattern Analysis -- Learning Objectives -- Introduction -- Rationale for Studying Point Patterns and Distributions -- Exploring Patterns, Distributions, and Trends Associated with Point Features -- Quadrat Count -- Nearest Neighbor Approach -- K-Function Approach -- Kernel Estimation Approach -- Constructing a Voronoi Map from Point Features -- Exploring Space-Time Patterns -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Explore Potential Path Area and Activity Space Concepts -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 7. Engaging in Areal Pattern Analysis Using Global and Local Statistics -- Learning Objectives -- Rationale for Studying Areal Patterns -- The Notion of Spatial Relationships -- Quantifying Spatial Autocorrelation Effects in Areal Patterns -- Join Count Statistics -- Interpreting the Join Count Statistics and Methodological Flaws -- Global Moran's I Coefficient of Spatial Autocorrelation -- Interpreting Moran's I and Methodological Flaws -- Global Geary's C Coefficient of Spatial Autocorrelation Interpreting Geary's C and Methodological Flaws -- Getis-Ord G Statistics -- Interpretation of Getis-Ord G and Methodological Flaws -- Local Moran's I -- Local G-Statistic -- Local Geary -- Using Scatterplots to Synthesize and Interpret Local Indicators of Spatial Association Statistics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Quiz -- Review and Study Questions -- Glossary of Key Terms -- References -- 8. Engaging in Geostatistical Analysis -- Learning Objectives -- Introduction -- Rationale for Using Geostatistics to Study Complex Spatial Patterns -- Basic Interpolation Equations -- Spatial Structure Functions for Regionalized Variables -- Kriging Method and Its Theoretical Framework -- Simple Kriging -- Ordinary Kriging -- Universal Kriging -- Indicator Kriging -- Key Points to Note about the Geostatistical Estimation Using Kriging -- Exploratory Data Analysis -- Spatial Prediction and Modeling -- Uncertainty Analysis -- Conditional Geostatistical Simulation -- Inverse Distance Weighting -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 9. Data Science: Understanding Computing Systems and Analytics for Big Data -- Learning Objectives -- Introduction to Data Science -- Rationale for a Big Geospatial Data Framework -- Data Management -- Data Warehousing -- Data Sources, Processing Tools, and the Extract-Transform-Load Process -- Data Integration and Storage -- Data-Mining Algorithms for Big Geospatial Data -- Tools, Algorithms, and Methods for Data Mining and Actionable Knowledge -- Business Intelligence, Spatial Online Analytical Processing, and Analytics -- Analytics and Strategies for Big Geospatial Data -- Spatiotemporal Data Analytics Classification Algorithms for Detecting Clusters in Big Geospatial Data -- Embedding Solutions/Algorithm with Topological Considerations -- Graph and Text Analytics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- Index Spatial analysis (Statistics) Datenanalyse (DE-588)4123037-1 gnd rswk-swf Räumliche Statistik (DE-588)4386767-4 gnd rswk-swf Geoinformationssystem (DE-588)4261642-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Raumdaten (DE-588)4206012-6 gnd rswk-swf Geoinformationssystem (DE-588)4261642-6 s Raumdaten (DE-588)4206012-6 s Räumliche Statistik (DE-588)4386767-4 s Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s DE-604 Erscheint auch als Oyana, Tonny J. Spatial Analysis with R Milton : Taylor & Francis Group,c2020 Druck-Ausgabe, Hardcover 978-0-367-86085-1 |
| spellingShingle | Oyana, Tonny J. Spatial analysis with R statistics, visualization, and computational methods Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- Author -- 1. Understanding the Context and Relevance of Spatial Analysis -- Learning Objectives -- Introduction -- From Data to Information, to Knowledge, and Wisdom -- Spatial Analysis Using a GIS Timeline -- Spatial Analysis in the Post-1990s Period -- Data Science, GIS, and Artificial Intelligence -- Geographic Data: Properties, Strengths, and Analytical Challenges -- Concept of Scale -- Concept of Spatial Dependency -- Concept of Spatial Proximity -- Modifiable Areal Unit Problem -- Concept of Spatial Autocorrelation -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Working with R -- Getting Started -- Working with Spatial Data -- Tips for Working with R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 2. Making Scientific Observations and Measurements in Spatial Analysis -- Learning Objectives -- Introduction -- Scales of Measurement -- Nominal Scale -- Ordinal Scale -- Interval Scale -- Ratio Scale -- Two Main Approaches for Data Collection That Involve Deductive and Inductive Reasoning -- Population and Sample -- Spatial Sampling -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Step I. View Data Structure -- Step II. Basic Data Summaries -- Step III. Exploring the Spatial Data -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 3. Using Statistical Measures to Analyze Data Distributions -- Learning Objectives -- Introduction -- Descriptive Statistics -- Measures of Central Tendency -- Deriving a Weighted Mean Using the Frequency Distributions in a Set of Observations -- Measures of Dispersion Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data -- Spatial Measures of Central Tendency -- Spatial Measures of Dispersion -- Random Variables and Probability Distribution -- Random Variable -- Probability and Theoretical Data Distributions -- Concepts and Applications -- Binomial Distribution -- Poisson Distribution -- Normal Distribution -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Exploring Z-Score to Assess the Relative Position in Data Distributions Using R -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 4. Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing -- Learning Objectives -- Introduction -- Exploratory Data Analysis, Geovisualization, and Data Visualization Methods -- Data Visualization -- Geographic Visualization -- New Stunning Visualization Tools and Infographics -- Exploratory Approaches for Visualizing Spatial Datasets -- Visualizing Multidimensional Datasets: An Illustration Based on U.S. Educational Achievements Rates, 1970-2012 -- Hypothesis Testing, Confidence Intervals, and .p.-Values -- Computation -- Statistical Conclusion -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Generating Graphical Data Summaries -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 5. Understanding Spatial Statistical Relationships -- Learning Objectives -- Engaging in Correlation Analysis -- Ordinary Least Squares and Geographically Weighted Regression Methods -- Procedures in Developing a Spatial Regression Model -- Examining Relationships between Regression Variables Examining the Strength of Association and Direction of All Paired Variables Using a Scatterplot Matrix -- Fitting the Ordinary Least Squares Regression Model -- Primary Model -- Examining Variance Inflation Factor Results -- Reduced Model -- Best Model -- Examining Residual Changes in Ordinary Least Squares Regression Models -- Fitting the Geographically Weighted Regression Model -- Examining Residual Change and Effects of Predictor Variables on Local Areas -- Summary of Modeling Result -- Conclusion -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 6. Engaging in Point Pattern Analysis -- Learning Objectives -- Introduction -- Rationale for Studying Point Patterns and Distributions -- Exploring Patterns, Distributions, and Trends Associated with Point Features -- Quadrat Count -- Nearest Neighbor Approach -- K-Function Approach -- Kernel Estimation Approach -- Constructing a Voronoi Map from Point Features -- Exploring Space-Time Patterns -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Explore Potential Path Area and Activity Space Concepts -- Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 7. Engaging in Areal Pattern Analysis Using Global and Local Statistics -- Learning Objectives -- Rationale for Studying Areal Patterns -- The Notion of Spatial Relationships -- Quantifying Spatial Autocorrelation Effects in Areal Patterns -- Join Count Statistics -- Interpreting the Join Count Statistics and Methodological Flaws -- Global Moran's I Coefficient of Spatial Autocorrelation -- Interpreting Moran's I and Methodological Flaws -- Global Geary's C Coefficient of Spatial Autocorrelation Interpreting Geary's C and Methodological Flaws -- Getis-Ord G Statistics -- Interpretation of Getis-Ord G and Methodological Flaws -- Local Moran's I -- Local G-Statistic -- Local Geary -- Using Scatterplots to Synthesize and Interpret Local Indicators of Spatial Association Statistics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Quiz -- Review and Study Questions -- Glossary of Key Terms -- References -- 8. Engaging in Geostatistical Analysis -- Learning Objectives -- Introduction -- Rationale for Using Geostatistics to Study Complex Spatial Patterns -- Basic Interpolation Equations -- Spatial Structure Functions for Regionalized Variables -- Kriging Method and Its Theoretical Framework -- Simple Kriging -- Ordinary Kriging -- Universal Kriging -- Indicator Kriging -- Key Points to Note about the Geostatistical Estimation Using Kriging -- Exploratory Data Analysis -- Spatial Prediction and Modeling -- Uncertainty Analysis -- Conditional Geostatistical Simulation -- Inverse Distance Weighting -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- 9. Data Science: Understanding Computing Systems and Analytics for Big Data -- Learning Objectives -- Introduction to Data Science -- Rationale for a Big Geospatial Data Framework -- Data Management -- Data Warehousing -- Data Sources, Processing Tools, and the Extract-Transform-Load Process -- Data Integration and Storage -- Data-Mining Algorithms for Big Geospatial Data -- Tools, Algorithms, and Methods for Data Mining and Actionable Knowledge -- Business Intelligence, Spatial Online Analytical Processing, and Analytics -- Analytics and Strategies for Big Geospatial Data -- Spatiotemporal Data Analytics Classification Algorithms for Detecting Clusters in Big Geospatial Data -- Embedding Solutions/Algorithm with Topological Considerations -- Graph and Text Analytics -- Conclusions -- Worked Examples in R and Stay One Step Ahead with Challenge Assignments -- Review and Study Questions -- Glossary of Key Terms -- References -- Index Spatial analysis (Statistics) Datenanalyse (DE-588)4123037-1 gnd Räumliche Statistik (DE-588)4386767-4 gnd Geoinformationssystem (DE-588)4261642-6 gnd R Programm (DE-588)4705956-4 gnd Raumdaten (DE-588)4206012-6 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4386767-4 (DE-588)4261642-6 (DE-588)4705956-4 (DE-588)4206012-6 |
| title | Spatial analysis with R statistics, visualization, and computational methods |
| title_auth | Spatial analysis with R statistics, visualization, and computational methods |
| title_exact_search | Spatial analysis with R statistics, visualization, and computational methods |
| title_full | Spatial analysis with R statistics, visualization, and computational methods Tonny J. Oyana |
| title_fullStr | Spatial analysis with R statistics, visualization, and computational methods Tonny J. Oyana |
| title_full_unstemmed | Spatial analysis with R statistics, visualization, and computational methods Tonny J. Oyana |
| title_short | Spatial analysis with R |
| title_sort | spatial analysis with r statistics visualization and computational methods |
| title_sub | statistics, visualization, and computational methods |
| topic | Spatial analysis (Statistics) Datenanalyse (DE-588)4123037-1 gnd Räumliche Statistik (DE-588)4386767-4 gnd Geoinformationssystem (DE-588)4261642-6 gnd R Programm (DE-588)4705956-4 gnd Raumdaten (DE-588)4206012-6 gnd |
| topic_facet | Spatial analysis (Statistics) Datenanalyse Räumliche Statistik Geoinformationssystem R Programm Raumdaten |
| work_keys_str_mv | AT oyanatonnyj spatialanalysiswithrstatisticsvisualizationandcomputationalmethods |