Statistical Methods for Geography

A Student’s Guide
Fifth Edition
Peter A. Rogerson
Statistical Methods for Geography
December 2019 | 432 pages | Sage UK
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Description

Statistical Methods for Geography is the essential introduction for geography students looking to fully understand and apply key statistical concepts and techniques. Now in its fifth edition, this text is an accessible statistics ‘101’ focused on student learning, and includes definitions, examples, and exercises throughout. Fully integrated with online self-assessment exercises and video overviews, it explains everything required to get full credits for any undergraduate statistics module.

 

The fifth edition of this bestselling text includes:

·        Coverage of descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis.

·        New examples from physical geography and additional real-world examples.

·        Updated in-text and online exercises along with downloadable datasets.

 

This is the only text you’ll need for undergraduate courses in statistical analysis, statistical methods, and quantitative geography.

Peter A. Rogerson is SUNY Distinguished Professor in the Department of Geography at the University at Buffalo, USA.

 

 


Contents

1 INTRODUCTION TO STATISTICAL METHODS FOR GEOGRAPHY

  • 1.1 Introduction
  • 1.2 The scientific method
  • 1.3 Exploratory and confirmatory approaches in geography
  • 1.4 Probability and statistics
  • 1.5 Descriptive and inferential methods
  • 1.6 The nature of statistical thinking
  • 1.7 Special considerations for spatial data
  • 1.8 The structure of the book
  • 1.9 Datasets

2 DESCRIPTIVE STATISTICS

  • 2.1 Types of data
  • 2.2 Visual descriptive methods
  • 2.3 Measures of central tendency
  • 2.4 Measures of variability
  • 2.5 Other numerical measures for describing data
  • 2.6 Descriptive spatial statistics
  • 2.7 Descriptive statistics in SPSS 25 for Windows
  • Solved exercises
  • Exercises

3 PROBABILITY AND DISCRETE PROBABILITY DISTRIBUTIONS

  • 3.1 Introduction
  • 3.2 Sample spaces, random variables, and probabilities
  • 3.3 Binomial processes and the binomial distribution
  • 3.4 The geometric distribution
  • 3.5 The Poisson distribution
  • 3.6 The hypergeometric distribution
  • 3.7 Binomial tests in SPSS 25 for Windows
  • Solved exercises
  • Exercises

4 CONTINUOUS PROBABILITY DISTRIBUTIONS AND PROBABILITY MODELS

  • 4.1 Introduction
  • 4.2 The uniform or rectangular distribution
  • 4.3 The normal distribution
  • 4.4 The exponential distribution
  • 4.5 Summary of discrete and continuous distributions
  • 4.6 Probability models
  • Solved exercises
  • Exercises

5 INFERENTIAL STATISTICS: CONFIDENCE INTERVALS, HYPOTHESIS TESTING, AND SAMPLING

  • 5.1 Introduction to inferential statistics
  • 5.2 Confidence intervals
  • 5.3 Hypothesis testing
  • 5.4 Distributions of the random variable and distributions of the test statistic
  • 5.5 Spatial data and the implications of nonindependence
  • 5.6 Further discussion of the effects of deviations from the assumptions
  • 5.7 Sampling
  • 5.8 Some tests for spatial measures of central tendency and variability
  • 5.9 One-sample tests of means in SPSS 25 for Windows
  • 5.10 Two-sample t-tests in SPSS 25 for Windows
  • Solved exercises
  • Exercises

6 ANALYSIS OF VARIANCE

  • 6.1 Introduction
  • 6.2 Illustrations
  • 6.3 Analysis of variance with two categories
  • 6.4 Testing the assumptions
  • 6.5 Consequences of failure to meet assumptions
  • 6.6 The nonparametric Kruskal–Wallis test
  • 6.7 The nonparametric median test
  • 6.8 Contrasts
  • 6.9 One-way ANOVA in SPSS 25 for Windows
  • 6.10 One-way ANOVA in Excel
  • Solved exercises
  • Exercises
  • 7 CORRELATION
  • 7.1 Introduction and examples of correlation
  • 7.2 More illustrations
  • 7.3 A significance test for r
  • 7.4 The correlation coefficient and sample size
  • 7.5 Spearman’s rank correlation coefficient
  • 7.6 Additional topics
  • 7.7 Correlation in SPSS 25 for Windows
  • 7.8 Correlation in Excel
  • Solved exercises
  • Exercises

8 DATA REDUCTION: FACTOR ANALYSIS AND CLUSTER ANALYSIS

  • 8.1 Introduction
  • 8.2 Factor analysis and principal components analysis
  • 8.3 Cluster analysis
  • 8.4 Data reduction methods in SPSS 25 for Windows
  • Exercises

9 INTRODUCTION TO REGRESSION ANALYSIS

  • 9.1 Introduction
  • 9.2 Fitting a regression line to a set of bivariate data
  • 9.3 Regression in terms of explained and unexplained sums of squares
  • 9.4 Assumptions of regression
  • 9.5 Standard error of the estimate
  • 9.6 Tests for ß
  • 9.7 Illustration: state aid to secondary schools
  • 9.8 Linear versus nonlinear models
  • 9.9 Regression in SPSS 25 for Windows
  • 9.10 Regression in Excel
  • Solved exercises
  • Exercises

10 MORE ON REGRESSION

  • 10.1 Multiple regression
  • 10.2 Misspecification error
  • 10.3 Dummy variables
  • 10.4 Multiple regression illustration: species in the Galápagos Islands
  • 10.5 Variable selection
  • 10.6 Regression analysis on component scores
  • 10.7 Categorical dependent variable
  • 10.8 A summary of some problems that can arise in regression analysis
  • 10.9 Multiple and logistic regression in SPSS 25 for Windows
  • Exercises

11 SPATIAL DATA, SPATIAL PATTERNS, AND SPATIAL REGRESSION

  • 11.1 Introduction
  • 11.2 The analysis of point patterns
  • 11.3 Geographic patterns in areal data
  • 11.4 Local statistics
  • 11.5 Introduction to spatial aspects of regression
  • 11.6 Spatial lag model and neighborhood-based explanatory variables
  • 11.7 Spatial regression: autocorrelated errors
  • 11.8 Geographically weighted regression
  • 11.9 Illustration
  • 11.10 Finding Moran’s I using SPSS 25 for Windows
  • 11.11 Finding Moran’s I using GeoDa
  • 11.12 Spatial Regression with GeoDa 1.4.6
  • Exercises

EPILOGUE

EPILOGUE

ANSWERS FOR SELECTED EXERCISES

ANSWERS FOR SELECTED EXERCISES

APPENDIX A: STATISTICAL TABLES

APPENDIX A: STATISTICAL TABLES

APPENDIX B: MATHEMATICAL CONVENTIONS AND NOTATION

APPENDIX B: MATHEMATICAL CONVENTIONS AND NOTATION

Bibliography

Bibliography

Index

Index

Description

Statistical Methods for Geography is the essential introduction for geography students looking to fully understand and apply key statistical concepts and techniques. Now in its fifth edition, this text is an accessible statistics ‘101’ focused on student learning, and includes definitions, examples, and exercises throughout. Fully integrated with online self-assessment exercises and video overviews, it explains everything required to get full credits for any undergraduate statistics module.

 

The fifth edition of this bestselling text includes:

·        Coverage of descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis.

·        New examples from physical geography and additional real-world examples.

·        Updated in-text and online exercises along with downloadable datasets.

 

This is the only text you’ll need for undergraduate courses in statistical analysis, statistical methods, and quantitative geography.

Peter A. Rogerson is SUNY Distinguished Professor in the Department of Geography at the University at Buffalo, USA.

 

 


Contents

1 INTRODUCTION TO STATISTICAL METHODS FOR GEOGRAPHY

  • 1.1 Introduction
  • 1.2 The scientific method
  • 1.3 Exploratory and confirmatory approaches in geography
  • 1.4 Probability and statistics
  • 1.5 Descriptive and inferential methods
  • 1.6 The nature of statistical thinking
  • 1.7 Special considerations for spatial data
  • 1.8 The structure of the book
  • 1.9 Datasets

2 DESCRIPTIVE STATISTICS

  • 2.1 Types of data
  • 2.2 Visual descriptive methods
  • 2.3 Measures of central tendency
  • 2.4 Measures of variability
  • 2.5 Other numerical measures for describing data
  • 2.6 Descriptive spatial statistics
  • 2.7 Descriptive statistics in SPSS 25 for Windows
  • Solved exercises
  • Exercises

3 PROBABILITY AND DISCRETE PROBABILITY DISTRIBUTIONS

  • 3.1 Introduction
  • 3.2 Sample spaces, random variables, and probabilities
  • 3.3 Binomial processes and the binomial distribution
  • 3.4 The geometric distribution
  • 3.5 The Poisson distribution
  • 3.6 The hypergeometric distribution
  • 3.7 Binomial tests in SPSS 25 for Windows
  • Solved exercises
  • Exercises

4 CONTINUOUS PROBABILITY DISTRIBUTIONS AND PROBABILITY MODELS

  • 4.1 Introduction
  • 4.2 The uniform or rectangular distribution
  • 4.3 The normal distribution
  • 4.4 The exponential distribution
  • 4.5 Summary of discrete and continuous distributions
  • 4.6 Probability models
  • Solved exercises
  • Exercises

5 INFERENTIAL STATISTICS: CONFIDENCE INTERVALS, HYPOTHESIS TESTING, AND SAMPLING

  • 5.1 Introduction to inferential statistics
  • 5.2 Confidence intervals
  • 5.3 Hypothesis testing
  • 5.4 Distributions of the random variable and distributions of the test statistic
  • 5.5 Spatial data and the implications of nonindependence
  • 5.6 Further discussion of the effects of deviations from the assumptions
  • 5.7 Sampling
  • 5.8 Some tests for spatial measures of central tendency and variability
  • 5.9 One-sample tests of means in SPSS 25 for Windows
  • 5.10 Two-sample t-tests in SPSS 25 for Windows
  • Solved exercises
  • Exercises

6 ANALYSIS OF VARIANCE

  • 6.1 Introduction
  • 6.2 Illustrations
  • 6.3 Analysis of variance with two categories
  • 6.4 Testing the assumptions
  • 6.5 Consequences of failure to meet assumptions
  • 6.6 The nonparametric Kruskal–Wallis test
  • 6.7 The nonparametric median test
  • 6.8 Contrasts
  • 6.9 One-way ANOVA in SPSS 25 for Windows
  • 6.10 One-way ANOVA in Excel
  • Solved exercises
  • Exercises
  • 7 CORRELATION
  • 7.1 Introduction and examples of correlation
  • 7.2 More illustrations
  • 7.3 A significance test for r
  • 7.4 The correlation coefficient and sample size
  • 7.5 Spearman’s rank correlation coefficient
  • 7.6 Additional topics
  • 7.7 Correlation in SPSS 25 for Windows
  • 7.8 Correlation in Excel
  • Solved exercises
  • Exercises

8 DATA REDUCTION: FACTOR ANALYSIS AND CLUSTER ANALYSIS

  • 8.1 Introduction
  • 8.2 Factor analysis and principal components analysis
  • 8.3 Cluster analysis
  • 8.4 Data reduction methods in SPSS 25 for Windows
  • Exercises

9 INTRODUCTION TO REGRESSION ANALYSIS

  • 9.1 Introduction
  • 9.2 Fitting a regression line to a set of bivariate data
  • 9.3 Regression in terms of explained and unexplained sums of squares
  • 9.4 Assumptions of regression
  • 9.5 Standard error of the estimate
  • 9.6 Tests for ß
  • 9.7 Illustration: state aid to secondary schools
  • 9.8 Linear versus nonlinear models
  • 9.9 Regression in SPSS 25 for Windows
  • 9.10 Regression in Excel
  • Solved exercises
  • Exercises

10 MORE ON REGRESSION

  • 10.1 Multiple regression
  • 10.2 Misspecification error
  • 10.3 Dummy variables
  • 10.4 Multiple regression illustration: species in the Galápagos Islands
  • 10.5 Variable selection
  • 10.6 Regression analysis on component scores
  • 10.7 Categorical dependent variable
  • 10.8 A summary of some problems that can arise in regression analysis
  • 10.9 Multiple and logistic regression in SPSS 25 for Windows
  • Exercises

11 SPATIAL DATA, SPATIAL PATTERNS, AND SPATIAL REGRESSION

  • 11.1 Introduction
  • 11.2 The analysis of point patterns
  • 11.3 Geographic patterns in areal data
  • 11.4 Local statistics
  • 11.5 Introduction to spatial aspects of regression
  • 11.6 Spatial lag model and neighborhood-based explanatory variables
  • 11.7 Spatial regression: autocorrelated errors
  • 11.8 Geographically weighted regression
  • 11.9 Illustration
  • 11.10 Finding Moran’s I using SPSS 25 for Windows
  • 11.11 Finding Moran’s I using GeoDa
  • 11.12 Spatial Regression with GeoDa 1.4.6
  • Exercises

EPILOGUE

EPILOGUE

ANSWERS FOR SELECTED EXERCISES

ANSWERS FOR SELECTED EXERCISES

APPENDIX A: STATISTICAL TABLES

APPENDIX A: STATISTICAL TABLES

APPENDIX B: MATHEMATICAL CONVENTIONS AND NOTATION

APPENDIX B: MATHEMATICAL CONVENTIONS AND NOTATION

Bibliography

Bibliography

Index

Index

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Statistical Methods for Geography

A Student’s Guide

Peter A. Rogerson
Cancel Print

December 2019 | 432 pages | Sage UK

Format Published Date ISBN Price

Statistical Methods for Geography is the essential introduction for geography students looking to fully understand and apply key statistical concepts and techniques. Now in its fifth edition, this text is an accessible statistics ‘101’ focused on student learning, and includes definitions, examples, and exercises throughout. Fully integrated with online self-assessment exercises and video overviews, it explains everything required to get full credits for any undergraduate statistics module.

 

The fifth edition of this bestselling text includes:

·        Coverage of descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis.

·        New examples from physical geography and additional real-world examples.

·        Updated in-text and online exercises along with downloadable datasets.

 

This is the only text you’ll need for undergraduate courses in statistical analysis, statistical methods, and quantitative geography.

Peter A. Rogerson is SUNY Distinguished Professor in the Department of Geography at the University at Buffalo, USA.

 

 



Table Of Contents:

  • 1 INTRODUCTION TO STATISTICAL METHODS FOR GEOGRAPHY
  • 1.1 Introduction
  • 1.2 The scientific method
  • 1.3 Exploratory and confirmatory approaches in geography
  • 1.4 Probability and statistics
  • 1.5 Descriptive and inferential methods
  • 1.6 The nature of statistical thinking
  • 1.7 Special considerations for spatial data
  • 1.8 The structure of the book
  • 1.9 Datasets
  • 2 DESCRIPTIVE STATISTICS
  • 2.1 Types of data
  • 2.2 Visual descriptive methods
  • 2.3 Measures of central tendency
  • 2.4 Measures of variability
  • 2.5 Other numerical measures for describing data
  • 2.6 Descriptive spatial statistics
  • 2.7 Descriptive statistics in SPSS 25 for Windows
  • Solved exercises
  • Exercises
  • 3 PROBABILITY AND DISCRETE PROBABILITY DISTRIBUTIONS
  • 3.1 Introduction
  • 3.2 Sample spaces, random variables, and probabilities
  • 3.3 Binomial processes and the binomial distribution
  • 3.4 The geometric distribution
  • 3.5 The Poisson distribution
  • 3.6 The hypergeometric distribution
  • 3.7 Binomial tests in SPSS 25 for Windows
  • Solved exercises
  • Exercises
  • 4 CONTINUOUS PROBABILITY DISTRIBUTIONS AND PROBABILITY MODELS
  • 4.1 Introduction
  • 4.2 The uniform or rectangular distribution
  • 4.3 The normal distribution
  • 4.4 The exponential distribution
  • 4.5 Summary of discrete and continuous distributions
  • 4.6 Probability models
  • Solved exercises
  • Exercises
  • 5 INFERENTIAL STATISTICS: CONFIDENCE INTERVALS, HYPOTHESIS TESTING, AND SAMPLING
  • 5.1 Introduction to inferential statistics
  • 5.2 Confidence intervals
  • 5.3 Hypothesis testing
  • 5.4 Distributions of the random variable and distributions of the test statistic
  • 5.5 Spatial data and the implications of nonindependence
  • 5.6 Further discussion of the effects of deviations from the assumptions
  • 5.7 Sampling
  • 5.8 Some tests for spatial measures of central tendency and variability
  • 5.9 One-sample tests of means in SPSS 25 for Windows
  • 5.10 Two-sample t-tests in SPSS 25 for Windows
  • Solved exercises
  • Exercises
  • 6 ANALYSIS OF VARIANCE
  • 6.1 Introduction
  • 6.2 Illustrations
  • 6.3 Analysis of variance with two categories
  • 6.4 Testing the assumptions
  • 6.5 Consequences of failure to meet assumptions
  • 6.6 The nonparametric Kruskal–Wallis test
  • 6.7 The nonparametric median test
  • 6.8 Contrasts
  • 6.9 One-way ANOVA in SPSS 25 for Windows
  • 6.10 One-way ANOVA in Excel
  • Solved exercises
  • Exercises
  • 7 CORRELATION
  • 7.1 Introduction and examples of correlation
  • 7.2 More illustrations
  • 7.3 A significance test for r
  • 7.4 The correlation coefficient and sample size
  • 7.5 Spearman’s rank correlation coefficient
  • 7.6 Additional topics
  • 7.7 Correlation in SPSS 25 for Windows
  • 7.8 Correlation in Excel
  • Solved exercises
  • Exercises
  • 8 DATA REDUCTION: FACTOR ANALYSIS AND CLUSTER ANALYSIS
  • 8.1 Introduction
  • 8.2 Factor analysis and principal components analysis
  • 8.3 Cluster analysis
  • 8.4 Data reduction methods in SPSS 25 for Windows
  • Exercises
  • 9 INTRODUCTION TO REGRESSION ANALYSIS
  • 9.1 Introduction
  • 9.2 Fitting a regression line to a set of bivariate data
  • 9.3 Regression in terms of explained and unexplained sums of squares
  • 9.4 Assumptions of regression
  • 9.5 Standard error of the estimate
  • 9.6 Tests for ß
  • 9.7 Illustration: state aid to secondary schools
  • 9.8 Linear versus nonlinear models
  • 9.9 Regression in SPSS 25 for Windows
  • 9.10 Regression in Excel
  • Solved exercises
  • Exercises
  • 10 MORE ON REGRESSION
  • 10.1 Multiple regression
  • 10.2 Misspecification error
  • 10.3 Dummy variables
  • 10.4 Multiple regression illustration: species in the Galápagos Islands
  • 10.5 Variable selection
  • 10.6 Regression analysis on component scores
  • 10.7 Categorical dependent variable
  • 10.8 A summary of some problems that can arise in regression analysis
  • 10.9 Multiple and logistic regression in SPSS 25 for Windows
  • Exercises
  • 11 SPATIAL DATA, SPATIAL PATTERNS, AND SPATIAL REGRESSION
  • 11.1 Introduction
  • 11.2 The analysis of point patterns
  • 11.3 Geographic patterns in areal data
  • 11.4 Local statistics
  • 11.5 Introduction to spatial aspects of regression
  • 11.6 Spatial lag model and neighborhood-based explanatory variables
  • 11.7 Spatial regression: autocorrelated errors
  • 11.8 Geographically weighted regression
  • 11.9 Illustration
  • 11.10 Finding Moran’s I using SPSS 25 for Windows
  • 11.11 Finding Moran’s I using GeoDa
  • 11.12 Spatial Regression with GeoDa 1.4.6
  • Exercises
  • EPILOGUE
  • ANSWERS FOR SELECTED EXERCISES
  • APPENDIX A: STATISTICAL TABLES
  • APPENDIX B: MATHEMATICAL CONVENTIONS AND NOTATION
  • Bibliography
  • Index

Recent Product Reviews:

This book has become the gold standard for teaching statistical methods to geographers. With a friendly and accessible manner, the author covers introductory statistics while revealing the quirkiness of spatial data. It is suitable for a one-year undergraduate class in geography, and there is no better reference for students transitioning to graduate studies. While always including rich examples form human geography, this new edition includes more examples from physical geography that will appeal to a wider audience.
Nicholas Nagle, Associate Professor of Geography, University of Tennessee
Absolutely fabulous resource that connects the utility of statistics for addressing geographic problems and issues. I have long used this text in teaching and research, beginning with the first edition in 2001. The continued revision and updating make this the premier text for introductory quantitative geographical inquiry.
Alan Murray, Professor of Geography, University of California at Santa Barbara

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