Atjaunināt sīkdatņu piekrišanu

Geocomputation with R [Hardback]

4.55/5 (57 ratings by Goodreads)
, (Friedrich Schiller University, Jena, Germany), (University of Leeds, UK)
  • Formāts: Hardback, 335 pages, height x width: 234x156 mm, weight: 770 g
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 25-Mar-2019
  • Izdevniecība: CRC Press
  • ISBN-10: 1138304514
  • ISBN-13: 9781138304512
Citas grāmatas par šo tēmu:
Geocomputation with RPalielināt
  • Formāts: Hardback, 335 pages, height x width: 234x156 mm, weight: 770 g
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 25-Mar-2019
  • Izdevniecība: CRC Press
  • ISBN-10: 1138304514
  • ISBN-13: 9781138304512
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781138304512.html
Keywords:
Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data.The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), bridges to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

Recenzijas

"Geocomputation with R offers several advantages. Firstly, it uses up-to-date packages, mainly the 'sf' package for vector processing which was not available at the time the previous books were written. 'sf' is truly a game-changer in the field of working with spatial data in R. I believe this alone makes writing the new book worthwhile. Secondly, the book offers a very broad overview, tryingand in my opinion succeedingto encompass all non-statistical themes involved in geo-computation, including subjects such as location and transport modeling in R (chapters 7-8) which were never published before. Thirdly, the book offers a lot of illustrations and clearly demonstrates key concepts in GIS and geo-computation from the R point of view. I believe these characteristics will give the book an advantage and quite possibly make it the most popular choice in the category of spatial analysis in R for several years to comeThe book can be used both as reference and as a textbookThe present book will definitely become the main textbook for this course once published." ~Michael Dorman, Ben-Gurion University of the Negev

"This book sets out to explain the key ideas in geocomputation more specifically manipulating, visualising, modelling and analysing geographical data. A further aim is to achieve all of this using only open source software. Developments of recent years have made this an achievable goal, and this book provides a good starting point for readers wishing to do this The strength of this book is therefore on the computational aspects (for example producing R-based interactive web sites using shiny) and as a comprehensive overview of the kinds of data one is likely to work with (for example taking care to ensure raster and vector data are both well represented here). The final part showcasing real-world applications is also strong. Another highlight of the book are the exercises at the end of each chapterFinally, the book comes with excellent resources, including an electronic version of the book itself (as a bookdown output) together with all the datasets and useful links (https://geocompr.robinlovelace.net). The emphasis of this book is perhaps most strongly on data manipulation, management and visualisation. If these are your main needs, it provides a comprehensive and readable companion." ~Urban Analytics and City Science

"Geocomputation with R is an excellent reference for those who have just started to program using R as well as for professionals who already have advanced knowledge using spatial data. The functions and topics presented in the book can be easily applied to projects with small data, but also to bigger data when making use of parallel processing and cloud computing. The very active as well as supportive R-spatial community is another big advantage for considering R as the go-to tool for geocomputation." ~Revista Cartogrįfica "Geocomputation with R offers several advantages. Firstly, it uses up-to-date packages, mainly the 'sf' package for vector processing which was not available at the time the previous books were written. 'sf' is truly a game-changer in the field of working with spatial data in R. I believe this alone makes writing the new book worthwhile. Secondly, the book offers a very broad overview, tryingand in my opinion succeedingto encompass all non-statistical themes involved in geo-computation, including subjects such as location and transport modeling in R (chapters 7-8) which were never published before. Thirdly, the book offers a lot of illustrations and clearly demonstrates key concepts in GIS and geo-computation from the R point of view. I believe these characteristics will give the book an advantage and quite possibly make it the most popular choice in the category of spatial analysis in R for several years to comeThe book can be used both as reference and as a textbookThe present book will definitely become the main textbook for this course once published." ~Michael Dorman, Ben-Gurion University of the Negev

"This book sets out to explain the key ideas in geocomputation more specifically manipulating, visualising, modelling and analysing geographical data. A further aim is to achieve all of this using only open source software. Developments of recent years have made this an achievable goal, and this book provides a good starting point for readers wishing to do this The strength of this book is therefore on the computational aspects (for example producing R-based interactive web sites using shiny) and as a comprehensive overview of the kinds of data one is likely to work with (for example taking care to ensure raster and vector data are both well represented here). The final part showcasing real-world applications is also strong. Another highlight of the book are the exercises at the end of each chapterFinally, the book comes with excellent resources, including an electronic version of the book itself (as a bookdown output) together with all the datasets and useful links (https://geocompr.robinlovelace.net). The emphasis of this book is perhaps most strongly on data manipulation, management and visualisation. If these are your main needs, it provides a comprehensive and readable companion." ~Urban Analytics and City Science

"Geocomputation with R is an excellent reference for those who have just started to program using R as well as for professionals who already have advanced knowledge using spatial data. The functions and topics presented in the book can be easily applied to projects with small data, but also to bigger data when making use of parallel processing and cloud computing. The very active as well as supportive R-spatial community is another big advantage for considering R as the go-to tool for geocomputation." ~Revista Cartogrįfica

Foreword xiii
Preface xv
1 Introduction
1(14)
1.1 What is geocomputation?
2(2)
1.2 Why use R for geocomputation?
4(2)
1.3 Software for geocomputation
6(2)
1.4 R's spatial ecosystem
8(2)
1.5 The history of R-spatial
10(3)
1.6 Exercises
13(2)
I Foundations
15(144)
2 Geographic data in R
17(30)
2.1 Introduction
18(1)
2.2 Vector data
19(16)
2.2.1 An introduction to simple features
20(4)
2.2.2 Why simple features?
24(1)
2.2.3 Basic map making
25(1)
2.2.4 Base plot arguments
26(2)
2.2.5 Geometry types
28(1)
2.2.6 Simple feature geometries (sfg)
29(3)
2.2.7 Simple feature columns (sfc)
32(2)
2.2.8 The sf class
34(1)
2.3 Raster data
35(5)
2.3.1 An introduction to raster
36(1)
2.3.2 Basic map making
37(1)
2.3.3 Raster classes
38(2)
2.4 Coordinate Reference Systems
40(4)
2.4.1 Geographic coordinate systems
41(1)
2.4.2 Projected coordinate reference systems
41(1)
2.4.3 CRSs in R
42(2)
2.5 Units
44(2)
2.6 Exercises
46(1)
3 Attribute data operations
47(20)
3.1 Introduction
47(1)
3.2 Vector attribute manipulation
48(12)
3.2.1 Vector attribute subsetting
50(4)
3.2.2 Vector attribute aggregation
54(1)
3.2.3 Vector attribute joining
55(4)
3.2.4 Creating attributes and removing spatial information
59(1)
3.3 Manipulating raster objects
60(5)
3.3.1 Raster subsetting
62(2)
3.3.2 Summarizing raster objects
64(1)
3.4 Exercises
65(2)
4 Spatial data operations
67(24)
4.1 Introduction
67(1)
4.2 Spatial operations on vector data
68(13)
4.2.1 Spatial subsetting
68(3)
4.2.2 Topological relations
71(2)
4.2.3 Spatial joining
73(2)
4.2.4 Non-overlapping joins
75(2)
4.2.5 Spatial data aggregation
77(3)
4.2.6 Distance relations
80(1)
4.3 Spatial operations on raster data
81(7)
4.3.1 Spatial subsetting
81(2)
4.3.2 Map algebra
83(1)
4.3.3 Local operations
84(1)
4.3.4 Focal operations
85(1)
4.3.5 Zonal operations
86(1)
4.3.6 Global operations and distances
87(1)
4.3.7 Merging rasters
88(1)
4.4 Exercises
88(3)
5 Geometry operations
91(36)
5.1 Introduction
91(1)
5.2 Geometric operations on vector data
92(14)
5.2.1 Simplification
92(2)
5.2.2 Centroids
94(2)
5.2.3 Buffers
96(1)
5.2.4 Affine transformations
97(2)
5.2.5 Clipping
99(2)
5.2.6 Geometry unions
101(1)
5.2.7 Type transformations
102(4)
5.3 Geometric operations on raster data
106(5)
5.3.1 Geometric intersections
107(1)
5.3.2 Extent and origin
107(2)
5.3.3 Aggregation and disaggregation
109(2)
5.4 Raster-vector interactions
111(12)
5.4.1 Raster cropping
112(1)
5.4.2 Raster extraction
113(4)
5.4.3 Rasterization
117(3)
5.4.4 Spatial vectorization
120(3)
5.5 Exercises
123(4)
6 Reprojecting geographic data
127(16)
6.1 Introduction
127(3)
6.2 When to reproject?
130(1)
6.3 Which CRS to use?
131(3)
6.4 Reprojecting vector geometries
134(1)
6.5 Modifying map projections
135(3)
6.6 Reprojecting raster geometries
138(3)
6.7 Exercises
141(2)
7 Geographic data I/O
143(16)
7.1 Introduction
143(1)
7.2 Retrieving open data
144(1)
7.3 Geographic data packages
145(2)
7.4 Geographic web services
147(2)
7.5 File formats
149(2)
7.6 Data input (I)
151(3)
7.6.1 Vector data
151(3)
7.6.2 Raster data
154(1)
7.7 Data output (O)
154(3)
7.7.1 Vector data
154(2)
7.7.2 Raster data
156(1)
7.8 Visual outputs
157(1)
7.9 Exercises
158(1)
II Extensions
159(98)
8 Making maps with R
161(38)
8.1 Introduction
161(1)
8.2 Static maps
162(17)
8.2.1 Tmap basics
163(2)
8.2.2 Map objects
165(2)
8.2.3 Aesthetics
167(1)
8.2.4 Color settings
168(4)
8.2.5 Layouts
172(3)
8.2.6 Faceted maps
175(2)
8.2.7 Inset maps
177(2)
8.3 Animated maps
179(2)
8.4 Interactive maps
181(7)
8.5 Mapping applications
188(4)
8.6 Other mapping packages
192(5)
8.7 Exercises
197(2)
9 Bridges to GIS software
199(22)
9.1 Introduction
199(3)
9.2 (R)QGIS
202(4)
9.3 (R)SAGA
206(3)
9.4 GRASS through rgrass?
209(5)
9.5 When to use what?
214(1)
9.6 Other bridges
215(5)
9.6.1 Bridges to GDAL
215(2)
9.6.2 Bridges to spatial databases
217(3)
9.7 Exercises
220(1)
10 Scripts, algorithms and functions
221(14)
10.1 Introduction
221(1)
10.2 Scripts
222(2)
10.3 Geometric algorithms
224(5)
10.4 Functions
229(3)
10.5 Programming
232(1)
10.6 Exercises
233(2)
11 Statistical learning
235(22)
11.1 Introduction
235(2)
11.2 Case study: Landslide susceptibility
237(2)
11.3 Conventional modeling approach in R
239(3)
11.4 Introduction to (spatial) cross-validation
242(1)
11.5 Spatial CV with mlr
243(10)
11.5.1 Generalized linear model
244(3)
11.5.2 Spatial tuning of machine-learning hyperparameters
247(6)
11.6 Conclusions
253(1)
11.7 Exercises
254(3)
III Applications
257(64)
12 Transportation
259(22)
12.1 Introduction
259(2)
12.2 A case study of Bristol
261(2)
12.3 Transport zones
263(4)
12.4 Desire lines
267(3)
12.5 Routes
270(2)
12.6 Nodes
272(2)
12.7 Route networks
274(1)
12.8 Prioritizing new infrastructure
275(2)
12.9 Future directions of travel
277(1)
12.10 Exercises
278(3)
13 Geomarketing
281(14)
13.1 Introduction
281(1)
13.2 Case study: bike shops in Germany
282(1)
13.3 Tidy the input data
283(1)
13.4 Create census rasters
283(3)
13.5 Define metropolitan areas
286(3)
13.6 Points of interest
289(2)
13.7 Identifying suitable locations
291(2)
13.8 Discussion and next steps
293(1)
13.9 Exercises
294(1)
14 Ecology
295(18)
14.1 Introduction
295(2)
14.2 Data and data preparation
297(3)
14.3 Reducing dimensionality
300(3)
14.4 Modeling the floristic gradient
303(6)
14.4.1 Mlr building blocks
305(2)
14.4.2 Predictive mapping
307(2)
14.5 Conclusions
309(1)
14.6 Exercises
310(3)
15 Conclusion
313(8)
15.1 Introduction
313(1)
15.2 Package choice
314(2)
15.3 Gaps and overlaps
316(1)
15.4 Where to go next?
317(2)
15.5 The open source approach
319(2)
Bibliography 321(10)
Index 331
Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems.

Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes.

Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping.

All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.