Nigel Walford has taught Geographical Information Systems courses at Kingston University for 12 years and previously worked in the Data Archive at Essex University. He graduated from the University of Sussex and did his postgraduate work at the University of London. His thematic research interests relate mainly to rural issues and include agriculture, land use planning and population. Recent book publications include Geographical Data Analysis and Reshaping the Countryside: Perceptions and Processes of Rural Change as well as several journal articles in Health and Place, Geography, Planning Practice and Research, Applied Geography and International Journal of Population Geography.

• Preface xiAcknowledgements xiiiGlossary xvSection 1 First principles 11 What's in a number? 3Learning outcomes1.1 Introduction to quantitative analysis 41.2 Nature of numerical data 91.3 Simplifying mathematical notation 141.4 Introduction to case studies and structure of the book 192 Geographical data: quantity and content 21Learning outcomes2.1 Geographical data 212.2 Populations and samples 222.3 Specifying attributes and variables 433 Geographical data: collection and acquisition 57Learning outcomes3.1 Originating data 583.2 Collection methods 593.3 Locating phenomena in geographical space 874 Statistical measures (or quantities) 93Learning outcomes4.1 Descriptive statistics 934.2 Spatial descriptive statistics 964.3 Central tendency 1004.4 Dispersion 1184.5 Measures of skewness and kurtosis for nonspatial data 1244.6 Closing comments 1295 Frequency distributions, probability and hypotheses 131Learning outcomes5.1 Frequency distributions 1325.2 Bivariate and multivariate frequency distributions 1375.3 Estimation of statistics from frequency distributions 1455.4 Probability 1495.5 Inference and hypotheses 1655.6 Connecting summary measures, frequency distributions and probability 169Section 2 Testing times 1736 Parametric tests 175Learning outcomes6.1 Introduction to parametric tests 1766.2 One variable and one sample 1776.3 Two samples and one variable 2016.4 Three or more samples and one variable 2106.5 Confi dence intervals 2166.6 Closing comments 2197 Nonparametric tests 221Learning outcomes7.1 Introduction to nonparametric tests 2227.2 One variable and one sample 2237.3 Two samples and one (or more) variable(s) 2457.4 Multiple samples and/or multiple variables 2567.5 Closing comments 264Section 3 Forming relationships 2658 Correlation 267Learning outcomes8.1 Nature of relationships between variables 2688.2 Correlation techniques 2758.3 Concluding remarks 2989 Regression 299Learning outcomes9.1 Specification of linear relationships 3009.2 Bivariate regression 3029.3 Concluding remarks 33610 Correlation and regression of spatial data 341Learning outcomes10.1 Issues with correlation and regression of spatial data 34210.2 Spatial and temporal autocorrelation 34510.3 Trend surface analysis 37810.4 Concluding remarks 394References 397Further Reading 399Index 403Plate section: Statistical Analysis Planner and Checklist falls between pages 172 and 173

“Overall, this is potentially a very useful, reader-friendly book for its target audience.”  (Soil Use and Management, 1 December 2013)

Nigel Walford has taught Geographical Information Systems courses at Kingston University for 12 years and previously worked in the Data Archive at Essex University. He graduated from the University of Sussex and did his postgraduate work at the University of London. His thematic research interests relate mainly to rural issues and include agriculture, land use planning and population. Recent book publications include Geographical Data Analysis and Reshaping the Countryside: Perceptions and Processes of Rural Change as well as several journal articles in Health and Place, Geography, Planning Practice and Research, Applied Geography and International Journal of Population Geography.

• Preface xiAcknowledgements xiiiGlossary xvSection 1 First principles 11 What's in a number? 3Learning outcomes1.1 Introduction to quantitative analysis 41.2 Nature of numerical data 91.3 Simplifying mathematical notation 141.4 Introduction to case studies and structure of the book 192 Geographical data: quantity and content 21Learning outcomes2.1 Geographical data 212.2 Populations and samples 222.3 Specifying attributes and variables 433 Geographical data: collection and acquisition 57Learning outcomes3.1 Originating data 583.2 Collection methods 593.3 Locating phenomena in geographical space 874 Statistical measures (or quantities) 93Learning outcomes4.1 Descriptive statistics 934.2 Spatial descriptive statistics 964.3 Central tendency 1004.4 Dispersion 1184.5 Measures of skewness and kurtosis for nonspatial data 1244.6 Closing comments 1295 Frequency distributions, probability and hypotheses 131Learning outcomes5.1 Frequency distributions 1325.2 Bivariate and multivariate frequency distributions 1375.3 Estimation of statistics from frequency distributions 1455.4 Probability 1495.5 Inference and hypotheses 1655.6 Connecting summary measures, frequency distributions and probability 169Section 2 Testing times 1736 Parametric tests 175Learning outcomes6.1 Introduction to parametric tests 1766.2 One variable and one sample 1776.3 Two samples and one variable 2016.4 Three or more samples and one variable 2106.5 Confi dence intervals 2166.6 Closing comments 2197 Nonparametric tests 221Learning outcomes7.1 Introduction to nonparametric tests 2227.2 One variable and one sample 2237.3 Two samples and one (or more) variable(s) 2457.4 Multiple samples and/or multiple variables 2567.5 Closing comments 264Section 3 Forming relationships 2658 Correlation 267Learning outcomes8.1 Nature of relationships between variables 2688.2 Correlation techniques 2758.3 Concluding remarks 2989 Regression 299Learning outcomes9.1 Specification of linear relationships 3009.2 Bivariate regression 3029.3 Concluding remarks 33610 Correlation and regression of spatial data 341Learning outcomes10.1 Issues with correlation and regression of spatial data 34210.2 Spatial and temporal autocorrelation 34510.3 Trend surface analysis 37810.4 Concluding remarks 394References 397Further Reading 399Index 403Plate section: Statistical Analysis Planner and Checklist falls between pages 172 and 173

“Overall, this is potentially a very useful, reader-friendly book for its target audience.”  (Soil Use and Management, 1 December 2013)

Nigel Walford has taught Geographical Information Systems courses at Kingston University for 12 years and previously worked in the Data Archive at Essex University. He graduated from the University of Sussex and did his postgraduate work at the University of London. His thematic research interests relate mainly to rural issues and include agriculture, land use planning and population. Recent book publications include Geographical Data Analysis and Reshaping the Countryside: Perceptions and Processes of Rural Change as well as several journal articles in Health and Place, Geography, Planning Practice and Research, Applied Geography and International Journal of Population Geography.

• Preface xiAcknowledgements xiiiGlossary xvSection 1 First principles 11 What's in a number? 3Learning outcomes1.1 Introduction to quantitative analysis 41.2 Nature of numerical data 91.3 Simplifying mathematical notation 141.4 Introduction to case studies and structure of the book 192 Geographical data: quantity and content 21Learning outcomes2.1 Geographical data 212.2 Populations and samples 222.3 Specifying attributes and variables 433 Geographical data: collection and acquisition 57Learning outcomes3.1 Originating data 583.2 Collection methods 593.3 Locating phenomena in geographical space 874 Statistical measures (or quantities) 93Learning outcomes4.1 Descriptive statistics 934.2 Spatial descriptive statistics 964.3 Central tendency 1004.4 Dispersion 1184.5 Measures of skewness and kurtosis for nonspatial data 1244.6 Closing comments 1295 Frequency distributions, probability and hypotheses 131Learning outcomes5.1 Frequency distributions 1325.2 Bivariate and multivariate frequency distributions 1375.3 Estimation of statistics from frequency distributions 1455.4 Probability 1495.5 Inference and hypotheses 1655.6 Connecting summary measures, frequency distributions and probability 169Section 2 Testing times 1736 Parametric tests 175Learning outcomes6.1 Introduction to parametric tests 1766.2 One variable and one sample 1776.3 Two samples and one variable 2016.4 Three or more samples and one variable 2106.5 Confi dence intervals 2166.6 Closing comments 2197 Nonparametric tests 221Learning outcomes7.1 Introduction to nonparametric tests 2227.2 One variable and one sample 2237.3 Two samples and one (or more) variable(s) 2457.4 Multiple samples and/or multiple variables 2567.5 Closing comments 264Section 3 Forming relationships 2658 Correlation 267Learning outcomes8.1 Nature of relationships between variables 2688.2 Correlation techniques 2758.3 Concluding remarks 2989 Regression 299Learning outcomes9.1 Specification of linear relationships 3009.2 Bivariate regression 3029.3 Concluding remarks 33610 Correlation and regression of spatial data 341Learning outcomes10.1 Issues with correlation and regression of spatial data 34210.2 Spatial and temporal autocorrelation 34510.3 Trend surface analysis 37810.4 Concluding remarks 394References 397Further Reading 399Index 403Plate section: Statistical Analysis Planner and Checklist falls between pages 172 and 173

“Overall, this is potentially a very useful, reader-friendly book for its target audience.”  (Soil Use and Management, 1 December 2013)