Spatial Data Science: With Applications in R

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors.

The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes - array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https: //r-spatial.org/book/. The solutions to the exercises can be found here: https: //edzer.github.io/sdsr_exercises/.

《空間數據科學》介紹了每位數據科學家在開始處理空間數據之前應該了解的基本概念。這些概念包括幾何形狀的表示方式、坐標參考系統（投影、基準點）、地球是圓的以及對分析的影響，以及幾何形狀的屬性如何與幾何形狀相關聯。在書的第二部分，這些概念通過使用R語言的數據科學示例進行了說明。在第三部分，使用真實世界的數據示例演示了統計建模方法。閱讀本書後，讀者將能夠避免一些主要的空間數據分析錯誤。

本書詳細解釋了R的核心空間軟件包：sf用於簡單要素訪問，以及stars用於栅格和矢量數據立方體 - 具有空間和時間維度的數組數據。它還展示了從平面空間到球面的幾何操作如何改變，這是當坐標未投影時sf和stars使用的方式（經度/緯度度）。單獨的章節詳細介紹了使用R繪製空間地圖的各種方法，以及處理非常大的矢量或栅格（影像）數據集的不同方式，包括本地處理、數據庫或雲端處理。所使用的數據和所有代碼示例都可以從https://r-spatial.org/book/免費獲取。練習的解答可以在此處找到：https://edzer.github.io/sdsr_exercises/。

Edzer Pebesma is professor at the Institute for Geoinformatics of the University of Muenster, Germany, where he leads the spatiotemporal modelling lab. He co-initiated openEO, an open source software ecosystem around a language neutral API for analyzing very large data cubes and image collections.

Roger Bivand is a geographer, emeritus professor of the Department of Economics of the Norwegian School of Economics, Bergen, Norway, has worked with spatial autocorrelation since the 1970's, and is a Fellow of the Spatial Econometrics Association.

Edzer and Roger have actively interacted with the open source geospatial user and developer communities since the last century. They author and maintain a number of key R packages for the handling and analysis of spatial and spatiotemporal data, including sf, stars, s2, sp, and gstat, spdep, spatialreg and rgrass. Both are ordinary members of the R foundation.

Edzer Pebesma是德國明斯特大學地理資訊學院的教授，他領導著時空建模實驗室。他是openEO的共同發起人，該項目是一個圍繞著語言中立API的開源軟體生態系統，用於分析非常大的數據立方體和圖像集合。

Roger Bivand是挪威經濟學院經濟學系的退休地理學家，自1970年代以來一直從事空間自相關研究，並是空間計量經濟學協會的會士。

Edzer和Roger自上個世紀以來一直積極與開源地理空間使用者和開發者社區互動。他們撰寫並維護了一些關鍵的R軟體包，用於處理和分析空間和時空數據，包括sf、stars、s2、sp、gstat、spdep、spatialreg和rgrass。兩人都是R基金會的普通會員。