Geographical data science & spatial data analysis: an introduction in R
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne
SAGE
2021
|
| Schriftenreihe: | Spatial analytics and GIS series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xv, 339 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9781526449368 9781526449351 |
Internformat
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| 246 | 1 | 3 | |a Geographical data science and spatial data analysis |
| 264 | 1 | |a Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne |b SAGE |c 2021 | |
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Datensatz im Suchindex
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| adam_text | CONTENTS About the authors Preface Online resources 1 2 xii xiv xvi Introduction to Geographical Data Science and Spatial Data Analytics 1 1.1 1.2 1 2 Overview About this book 1.2.1 Why Geographical Data Science and Spatial Data Analytics? 1.2.2 Why R? 1.2.3 Chapter contents 1.2.4 Learning and arcs 1.3 Getting started in R 1.3.1 Installing R and RStudio 1.3.2 The RStudio interface 1.3.3 Working in R 1.3.4 Principles 1.4 Assignment operations and object types in R 1.4.1 Your first R script 1.4.2 Basic data types in R 1.4.3 Basic data selection operations 1.4.4 Logical operations in R 1.4.5 Functions in R 1.4.6 Packages 1.5 Summary References 2 4 5 7 8 8 9 10 11 12 13 16 22 25 28 30 32 33 Data and Spatial Data in R 35 2.1 Overview 2.2 Data and spatial data 2.2.1 Long vs. wide data 2.2.2 Changes to data formats 2.2.3 Data formats: tibble vs. data. frame 2.2.4 Spatial data formats: sf vs. sp 35 36 36 38 39 44
CONTENTS 3 4 2.3 2.4 The tidyverse and tidy data dplyr for manipulating data (without pipes) 2.4.1 Introduction to dplyr 2.4.2 Single-table manipulations: dplyr verbs 2.4.3 Joining data tables in dplyr 2.5 Mapping and visualising spatial properties with tmap 2.6 Summary References 53 55 55 58 62 66 76 76 A Framework for Processing Data: the Piping Syntax and dplyr 78 3.1 Overview 3.2 Introduction to pipelines of tidy data 3.3 The dplyr pipelining filters 3.3.1 Using select for column subsets 3.3.2 Using mutate to derive new variables and transform existing ones 3.3.3 group_by and summarise: changing the unit of observation 3.3.4 group_by with other data frame operations 3.3.5 Order-dependent window functions 3.4 The tidy data chaining process 3.4.1 Obtaining data 3.4.2 Making the data tidy 3.5 Pipelines, dplyr and spatial data 3.5.1 dplyr and sf format spatial objects 3.5.2 A practical example of spatial data analysis 3.5.3 A further map-based example 3.5.4 Other spatial manipulations 3.6 Summary References 78 80 83 83 87 90 93 95 96 101 109 110 111 116 119 123 124 Creating Databases and Queries in R 125 4.1 4.2 125 126 126 127 128 130 131 133 135 Overview Introduction to databases 4.2.1 Why use a database? 4.2.2 Databases in R 4.2.3 Prescribing data 4.3 Creating relational databases in R 4.3.1 Creating a local in-memory database 4.3.2 Creating a local on-file database 4.3.3 Summary 85
CONTENTS 5 6 4.4 Database queries 4.4.1 Extracting from a database 4.4.2 Joining (linking) database tables 4.4.3 Mutating, grouping and summarising 4.4.4 Final observations 4.5 Worked example: bringing it all together 4.6 Summary References 136 137 139 141 143 145 152 154 EDA and Finding Structure in Data 155 5.1 5.2 5.3 Overview Exploratory data analysis EDA with ggpiotZ 5.3.1 ggpłot basics 5.3.2 Groups with ggptot 5.4 EDA of single continuous variables 5.5 EDA of multiple continuous variables 5.6 EDA of categorical variables 5.6.1 . EDA ofsingle categorical variables 5.6.2 EDA of multiple categorical variables 5.7 Temporal trends: summarising data over time 5.8 Spatial EDA 5.9 Summary References 155 156 158 159 160 163 168 180 180 183 189 192 198 200 Modelling andExploration of Data 201 6.1 6.2 201 204 205 Overview Questions, questions 6.2.1 Is this a fake coin? 6.2.2 What is the probability of getting a head in a coin flip? 6.2.3 How many heads next time I flip the coin? 6.3 More conceptually demanding questions 6.3.1 House price problem 6.3.2 The underlying method 6.3.3 Practical computation in R 6.4 More technically demanding questions 6.4.1 An example: fitting generalised linear models 6.4.2 Practical considerations 6.4.3 A random subset for regressions 6.4.4 Speeding up the GLM estimation 209 215 220 222 223 224 228 228 234 236 237
CONTENTS Questioning the answering process and questioning the questioning process 6.6 Summary References 239 240 241 Applications of Machine Learning to Spatial Data 243 Overview Data Prediction versus inference The mechanics of machine learning 7.4.1 Data rescaling and normalisation 7.4.2 Training data 7.4.3 Measures of fit 7.4.4 Model tuning 7.4.5 Validation 7.4.6 Summary of key points 7.5 Machine learning in caret 7.5.1 Data 7.5.2 Model overviews 7.5.3 Prediction 7.5.4 Inference 7.5.5 Summary of key points 7.6 Classification 7.6.1 Supervised classification 7.6.2 Unsupervised classification 7.6.3 Other considerations 7.6.4 Pulling it all together 7.6.5 Summary References 243 244 247 250 251 253 254 256 263 264 265 265 267 270 273 276 278 279 282 286 288 293 295 Alternative Spatial Summaries and Visualisations 297 8.1 8.2 8.3 8.4 8.5 8.6 297 297 299 303 306 309 309 310 313 316 318 319 6.5 7 7.1 7.2 7.3 7.4 8 Overview The invisibility problem Cartograms Hexagonal binning and tile maps Spatial binning data: a small worked example Binning large spatial datasets: the geographyof misery 8.6.1 Background context 8.6.2 Extracting from and wrangling with large datasets 8.6.3 Mapping 8.6.4 Considerations 8.7 Summary References
CONTENTS 9 Epilogue on the Principles of Spatial Data Analytics 321 What we have done 9.1.1 Use the tidyverse 9.1.2 Link analytical software to databases 9.1.3 Look through a spatial lens 9.1.4 Consider visual aspects 9.1.5 Consider inferential aspects 9.2 What we have failed to do 9.2.1 Look at spatio-temporal processes 9.2.2 Look at textual data 9.2.3 Look at raster data 9.2.4 Be uncritical 9.3 A series of consummations devoutly to be wished 9.3.1 A more integrated spatial database to work with R 9.3.2 Cloud-based R computing 9.3.3 Greater critical evaluation of data scienceprojects References 321 321 323 324 325 326 328 328 329 330 330 331 331 331 331 332 9.1 Index 334
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| adam_txt |
CONTENTS About the authors Preface Online resources 1 2 xii xiv xvi Introduction to Geographical Data Science and Spatial Data Analytics 1 1.1 1.2 1 2 Overview About this book 1.2.1 Why Geographical Data Science and Spatial Data Analytics? 1.2.2 Why R? 1.2.3 Chapter contents 1.2.4 Learning and arcs 1.3 Getting started in R 1.3.1 Installing R and RStudio 1.3.2 The RStudio interface 1.3.3 Working in R 1.3.4 Principles 1.4 Assignment operations and object types in R 1.4.1 Your first R script 1.4.2 Basic data types in R 1.4.3 Basic data selection operations 1.4.4 Logical operations in R 1.4.5 Functions in R 1.4.6 Packages 1.5 Summary References 2 4 5 7 8 8 9 10 11 12 13 16 22 25 28 30 32 33 Data and Spatial Data in R 35 2.1 Overview 2.2 Data and spatial data 2.2.1 Long vs. wide data 2.2.2 Changes to data formats 2.2.3 Data formats: tibble vs. data. frame 2.2.4 Spatial data formats: sf vs. sp 35 36 36 38 39 44
CONTENTS 3 4 2.3 2.4 The tidyverse and tidy data dplyr for manipulating data (without pipes) 2.4.1 Introduction to dplyr 2.4.2 Single-table manipulations: dplyr verbs 2.4.3 Joining data tables in dplyr 2.5 Mapping and visualising spatial properties with tmap 2.6 Summary References 53 55 55 58 62 66 76 76 A Framework for Processing Data: the Piping Syntax and dplyr 78 3.1 Overview 3.2 Introduction to pipelines of tidy data 3.3 The dplyr pipelining filters 3.3.1 Using select for column subsets 3.3.2 Using mutate to derive new variables and transform existing ones 3.3.3 group_by and summarise: changing the unit of observation 3.3.4 group_by with other data frame operations 3.3.5 Order-dependent window functions 3.4 The tidy data chaining process 3.4.1 Obtaining data 3.4.2 Making the data tidy 3.5 Pipelines, dplyr and spatial data 3.5.1 dplyr and sf format spatial objects 3.5.2 A practical example of spatial data analysis 3.5.3 A further map-based example 3.5.4 Other spatial manipulations 3.6 Summary References 78 80 83 83 87 90 93 95 96 101 109 110 111 116 119 123 124 Creating Databases and Queries in R 125 4.1 4.2 125 126 126 127 128 130 131 133 135 Overview Introduction to databases 4.2.1 Why use a database? 4.2.2 Databases in R 4.2.3 Prescribing data 4.3 Creating relational databases in R 4.3.1 Creating a local in-memory database 4.3.2 Creating a local on-file database 4.3.3 Summary 85
CONTENTS 5 6 4.4 Database queries 4.4.1 Extracting from a database 4.4.2 Joining (linking) database tables 4.4.3 Mutating, grouping and summarising 4.4.4 Final observations 4.5 Worked example: bringing it all together 4.6 Summary References 136 137 139 141 143 145 152 154 EDA and Finding Structure in Data 155 5.1 5.2 5.3 Overview Exploratory data analysis EDA with ggpiotZ 5.3.1 ggpłot basics 5.3.2 Groups with ggptot 5.4 EDA of single continuous variables 5.5 EDA of multiple continuous variables 5.6 EDA of categorical variables 5.6.1 . EDA ofsingle categorical variables 5.6.2 EDA of multiple categorical variables 5.7 Temporal trends: summarising data over time 5.8 Spatial EDA 5.9 Summary References 155 156 158 159 160 163 168 180 180 183 189 192 198 200 Modelling andExploration of Data 201 6.1 6.2 201 204 205 Overview Questions, questions 6.2.1 Is this a fake coin? 6.2.2 What is the probability of getting a head in a coin flip? 6.2.3 How many heads next time I flip the coin? 6.3 More conceptually demanding questions 6.3.1 House price problem 6.3.2 The underlying method 6.3.3 Practical computation in R 6.4 More technically demanding questions 6.4.1 An example: fitting generalised linear models 6.4.2 Practical considerations 6.4.3 A random subset for regressions 6.4.4 Speeding up the GLM estimation 209 215 220 222 223 224 228 228 234 236 237
CONTENTS Questioning the answering process and questioning the questioning process 6.6 Summary References 239 240 241 Applications of Machine Learning to Spatial Data 243 Overview Data Prediction versus inference The mechanics of machine learning 7.4.1 Data rescaling and normalisation 7.4.2 Training data 7.4.3 Measures of fit 7.4.4 Model tuning 7.4.5 Validation 7.4.6 Summary of key points 7.5 Machine learning in caret 7.5.1 Data 7.5.2 Model overviews 7.5.3 Prediction 7.5.4 Inference 7.5.5 Summary of key points 7.6 Classification 7.6.1 Supervised classification 7.6.2 Unsupervised classification 7.6.3 Other considerations 7.6.4 Pulling it all together 7.6.5 Summary References 243 244 247 250 251 253 254 256 263 264 265 265 267 270 273 276 278 279 282 286 288 293 295 Alternative Spatial Summaries and Visualisations 297 8.1 8.2 8.3 8.4 8.5 8.6 297 297 299 303 306 309 309 310 313 316 318 319 6.5 7 7.1 7.2 7.3 7.4 8 Overview The invisibility problem Cartograms Hexagonal binning and tile maps Spatial binning data: a small worked example Binning large spatial datasets: the geographyof misery 8.6.1 Background context 8.6.2 Extracting from and wrangling with large datasets 8.6.3 Mapping 8.6.4 Considerations 8.7 Summary References
CONTENTS 9 Epilogue on the Principles of Spatial Data Analytics 321 What we have done 9.1.1 Use the tidyverse 9.1.2 Link analytical software to databases 9.1.3 Look through a spatial lens 9.1.4 Consider visual aspects 9.1.5 Consider inferential aspects 9.2 What we have failed to do 9.2.1 Look at spatio-temporal processes 9.2.2 Look at textual data 9.2.3 Look at raster data 9.2.4 Be uncritical 9.3 A series of consummations devoutly to be wished 9.3.1 A more integrated spatial database to work with R 9.3.2 Cloud-based R computing 9.3.3 Greater critical evaluation of data scienceprojects References 321 321 323 324 325 326 328 328 329 330 330 331 331 331 331 332 9.1 Index 334 |
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| spelling | Comber, Lex Verfasser (DE-588)1069224200 aut Geographical data science & spatial data analysis an introduction in R Lex Comber and Chris Brunsdon Geographical data science and spatial data analysis Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne SAGE 2021 xv, 339 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Spatial analytics and GIS series Räumliche Statistik (DE-588)4386767-4 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Geoinformationssystem (DE-588)4261642-6 gnd rswk-swf R (Computer program language) Spatial analysis (Statistics) / Data processing Räumliche Statistik (DE-588)4386767-4 s Geoinformationssystem (DE-588)4261642-6 s R Programm (DE-588)4705956-4 s DE-604 Brunsdon, Chris Verfasser (DE-588)171743814 aut Erscheint auch als Online-Ausgabe 978-1-5264-8543-4 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032513555&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Comber, Lex Brunsdon, Chris Geographical data science & spatial data analysis an introduction in R Räumliche Statistik (DE-588)4386767-4 gnd R Programm (DE-588)4705956-4 gnd Geoinformationssystem (DE-588)4261642-6 gnd |
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| title | Geographical data science & spatial data analysis an introduction in R |
| title_alt | Geographical data science and spatial data analysis |
| title_auth | Geographical data science & spatial data analysis an introduction in R |
| title_exact_search | Geographical data science & spatial data analysis an introduction in R |
| title_exact_search_txtP | Geographical data science & spatial data analysis an introduction in R |
| title_full | Geographical data science & spatial data analysis an introduction in R Lex Comber and Chris Brunsdon |
| title_fullStr | Geographical data science & spatial data analysis an introduction in R Lex Comber and Chris Brunsdon |
| title_full_unstemmed | Geographical data science & spatial data analysis an introduction in R Lex Comber and Chris Brunsdon |
| title_short | Geographical data science & spatial data analysis |
| title_sort | geographical data science spatial data analysis an introduction in r |
| title_sub | an introduction in R |
| topic | Räumliche Statistik (DE-588)4386767-4 gnd R Programm (DE-588)4705956-4 gnd Geoinformationssystem (DE-588)4261642-6 gnd |
| topic_facet | Räumliche Statistik R Programm Geoinformationssystem |
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