Spatial data are everywhere. Besides those we collect ourselves (is it raining?), they confront us on television, in newspapers, on route planners, on computer screens, and on plain paper maps. Making a map that is suited to its purpose and does not distort the underlying data unnecessarily is not easy. Beyond creating and viewing maps, spatial data analysis is concerned with questions not directly answered by looking at the data themselves. These questions refer to hypothetical processes that generate the observed data. Statistical inference for such spatial processes is often challenging, but is necessary when we try to draw conclusions about questions that interest us. In this book we will be concerned with applied spatial data analysis, meaning that we will deal with data sets, explain the problems they confront us with, and show how we can attempt to reach a conclusion. This book will refer to the theoretical background of methods and models for data analysis, but emphasise hands-on, do-it-yourself examples using R; readers needing this background should consult the references. All data sets used in this book and all examples given are available, and interested readers will be able to reproduce them.
CITATION STYLE
Bivand, R. S., Pebesma, E., & Gómez-Rubio, V. (2013). Hello World: Introducing Spatial Data. In Applied Spatial Data Analysis with R (pp. 1–16). Springer New York. https://doi.org/10.1007/978-1-4614-7618-4_1
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