This book builds on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping, to consider Spatial Data (ie the location attached to data), issues of inference, linking Big Data, Geography / GIS / Mapping and Spatial Analytics. A 'learning by doing' text book, it covers important theoretical issues and helps to develop practical skills in the reader for addressing these.
This book builds on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping, to consider Spatial Data (ie the location attached to data), issues of inference, linking Big Data, Geography / GIS / Mapping and Spatial Analytics. A 'learning by doing' text book, it covers important theoretical issues and helps to develop practical skills in the reader for addressing these.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Alexis Comber, Lex, is Professor of Spatial Data Analytics at Leeds Institute for Data Analytics (LIDA) the University of Leeds. He worked previously at the University of Leicester where he held a chair in Geographical Information Science. His first degree was in Plant and Crop Science at the University of Nottingham and he completed a PhD in Computer Science at the Macaulay Institute, Aberdeen (now the James Hutton Institute) and the University of Aberdeen. This developed expert systems for land cover monitoring from satellite imagery and brought him into the world of spatial data, spatial analysis, and mapping. Lex's research interests span many different application areas including environment, land cover / land use, demographics, public health, agriculture, bio-energy and accessibility, all of which require multi-disciplinary approaches. His research draws from methods in geocomputation, mathematics, statistics and computer science and he has extended techniques in operations research / location-allocation (what to put where), graph theory (cluster detection in networks), heuristic searches (how to move intelligently through highly dimensional big data), remote sensing (novel approaches for classification), handling divergent data semantics (uncertainty handling, ontologies, text mining) and spatial statistics (quantifying spatial and temporal process heterogeneity). He has co-authored (with Chris Brunsdon) An Introduction to R for Spatial Analysis and Mapping, the first 'how to book' for spatial analyses and mapping in R, the open source statistical software, now in its second edition. Outside of academic work and in no particular order, Lex enjoys his vegetable garden, walking the dog and playing pinball (he is the proud owner of a 1981 Bally Eight Ball Deluxe).
Inhaltsangabe
Chapter 1: Introduction to Geographical Data Science and Spatial Data Analytics Chapter 2: Data and Spatial Data in R Chapter 3: A Framework for Processing Data: The Piping Syntax and dplyr Chapter 4: Creating Databases and Queries in R Chapter 5: EDA and Finding Structure in Data Chapter 6: Modelling and Exploration of Data Chapter 7: Applications of Machine Learning to Spatial Data Chapter 8: Alternative Spatial Summaries and Visualisations Chapter 9: Epilogue on the Principles of Spatial Data Analytics
Chapter 1: Introduction to Geographical Data Science and Spatial Data Analytics Chapter 2: Data and Spatial Data in R Chapter 3: A Framework for Processing Data: The Piping Syntax and dplyr Chapter 4: Creating Databases and Queries in R Chapter 5: EDA and Finding Structure in Data Chapter 6: Modelling and Exploration of Data Chapter 7: Applications of Machine Learning to Spatial Data Chapter 8: Alternative Spatial Summaries and Visualisations Chapter 9: Epilogue on the Principles of Spatial Data Analytics
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