Statistical analysis of geographical data : an introduction / Simon J. Dadson.

Format:

Book

Author/Creator:

Dadson, Simon J., author.

Language:

English

Subjects (All):

Geography--Statistical methods.

Geography.

Physical Description:

1 online resource (267 pages)

Place of Publication:

Hoboken, New Jersey ; Chichester, West Sussex, England : Wiley, 2017.

Summary:

Statistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography. It covers a wide range of topics including graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression. Taking a clear and logical approach, this book examines real problems with real data from the geographical literature in order to illustrate the important role that statistics play in geographical investigations. Presented in a clear and accessible manner the book includes recent, relevant examples, designed to enhance the reader's understanding.

Contents:

Intro

Title Page

Copyright Page

Contents

Preface

Chapter 1 Dealing with data

1.1 The role of statistics in geography

1.1.1 Why do geographers need to use statistics?

1.2 About this book

1.3 Data and measurement error

1.3.1 Types of geographical data: nominal, ordinal, interval, and ratio

1.3.2 Spatial data types

1.3.3 Measurement error, accuracy and precision

1.3.4 Reporting data and uncertainties

1.3.5 Significant figures

1.3.6 Scientific notation (standard form)

1.3.7 Calculations in scientific notation

Exercises

Chapter 2 Collecting and summarizing data

2.1 Sampling methods

2.1.1 Research design

2.1.2 Random sampling

2.1.3 Systematic sampling

2.1.4 Stratified sampling

2.2 Graphical summaries

2.2.1 Frequency distributions and histograms

2.2.2 Time series plots

2.2.3 Scatter plots

2.3 Summarizing data numerically

2.3.1 Measures of central tendency: mean, median and mode

2.3.2 Mean

2.3.3 Median

2.3.4 Mode

2.3.5 Measures of dispersion

2.3.6 Variance

2.3.7 Standard deviation

2.3.8 Coefficient of variation

2.3.9 Skewness and kurtosis

Chapter 3 Probability and sampling distributions

3.1 Probability

3.1.1 Probability, statistics and random variables

3.1.2 The properties of the normal distribution

3.2 Probability and the normal distribution: z-scores

3.3 Sampling distributions and the central limit theorem

Chapter 4 Estimating parameters with confidence intervals

4.1 Confidence intervals on the mean of a normal distribution: the basics

4.2 Confidence intervals in practice: the t-distribution

4.3 Sample size

4.4 Confidence intervals for a proportion

Chapter 5 Comparing datasets

5.1 Hypothesis testing with one sample: general principles.

5.1.1 Comparing means: one-sample z-test

5.1.2 p-values

5.1.3 General procedure for hypothesis testing

5.2 Comparing means from small samples: one-sample t-test

5.3 Comparing proportions for one sample

5.4 Comparing two samples

5.4.1 Independent samples

5.4.2 Comparing means: t-test with unknown population variances assumed equal

5.4.3 Comparing means: t-test with unknown population variances assumed unequal

5.4.4 t-test for use with paired samples (paired t-test)

5.4.5 Comparing variances: F-test

5.5 Non-parametric hypothesis testing

5.5.1 Parametric and non-parametric tests

5.5.2 Mann-whitney U-test

Chapter 6 Comparing distributions: the Chi-squared test

6.1 Chi-squared test with one sample

6.2 Chi-squared test for two samples

Chapter 7 Analysis of variance

7.1 One-way analysis of variance

7.2 Assumptions and diagnostics

7.3 Multiple comparison tests after analysis of variance

7.4 Non-parametric methods in the analysis of variance

7.5 Summary and further applications

Chapter 8 Correlation

8.1 Correlation analysis

8.2 Pearson's product-moment correlation coefficient

8.3 Significance tests of correlation coefficient

8.4 Spearman's rank correlation coefficient

8.5 Correlation and causality

Chapter 9 Linear regression

9.1 Least-squares linear regression

9.2 Scatter plots

9.3 Choosing the line of best fit: the 'least-squares' procedure

9.4 Analysis of residuals

9.5 Assumptions and caveats with regression

9.6 Is the regression significant?

9.7 Coefficient of determination

9.8 Confidence intervals and hypothesis tests concerning regression parameters

9.8.1 Standard error of the regression parameters

9.8.2 Tests on the regression parameters.

9.8.3 Confidence intervals on the regression parameters

9.8.4 Confidence interval about the regression line

9.9 Reduced major axis regression

9.10 Summary

Chapter 10 Spatial Statistics

10.1 Spatial Data

10.1.1 Types of Spatial Data

10.1.2 Spatial Data Structures

10.1.3 Map Projections

10.2 Summarizing Spatial Data

10.2.1 Mean Centre

10.2.2 Weighted Mean Centre

10.2.3 Density Estimation

10.3 Identifying Clusters

10.3.1 Quadrat Test

10.3.2 Nearest Neighbour Statistics

10.4 Interpolation and Plotting Contour Maps

10.5 Spatial Relationships

10.5.1 Spatial Autocorrelation

10.5.2 Join Counts

Chapter 11 Time series analysis

11.1 Time series in geographical research

11.2 Analysing time series

11.2.1 Describing time series: definitions

11.2.2 Plotting time series

11.2.3 Decomposing time series: trends, seasonality and irregular fluctuations

11.2.4 Analysing trends

11.2.5 Removing trends ('detrending' data)

11.2.6 Quantifying seasonal variation

11.2.7 Autocorrelation

11.3 Summary

Appendix A: Introduction to the R package

A.1 Obtaining R

A.2 Simplecalculations

A.3 Vectors

A.4 Basicstatistics

A.5 Plottingdata

A.6 Multiplefigures

A.7 Readingand writing data

A.8 Summary

Appendix B: Statistical tables

References

Index

EULA.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781118525142

1118525140

OCLC:

971021188