![]() We cover reprojecting vector and raster objects in sections 7.7 and 7.8 and modifying map projections in Section 7.9. We tackle questions of when to reproject and which CRS to use in Section 7.5 and Section 7.6, respectively. Section 7.4 demonstrates the importance of knowing what CRS your data is in with reference to a worked example of creating buffers. In the next section we introduce CRSs in R, followed by Section 7.2 which shows how to get and set CRSs associated with spatial objects. This chapter teaches the fundamentals of CRSs, demonstrates the consequences of using different CRSs (including what can go wrong), and how to ‘reproject’ datasets from one coordinate system to another. ![]() Having a clearly defined project CRS that all project data is in, plus understanding how and why to use different CRSs, can ensure that things don’t go wrong.įurthermore, learning about coordinate systems will deepen your knowledge of geographic datasets and how to use them effectively. However, if you know the CRS of your data and the consequences for geometry operations (covered in the next section), CRSs should just work behind the scenes: people often suddenly need to learn about CRSs when things go wrong. ![]() It is important to know if your data is in a projected or geographic coordinate system, and the consequences of this for geometry operations. In many projects there is no need to worry about, let alone convert between, different CRSs. It demonstrates how to set and transform geographic data from one CRS to another and, furthermore, highlights specific issues that can arise due to ignoring CRSs that you should be aware of, especially if your data is stored with lon/lat coordinates. This chapter builds on that knowledge and goes further. Section 2.4 introduced coordinate reference systems (CRSs), with a focus on the two major types: geographic (‘lon/lat’, with units in degrees longitude and latitude) and projected (typically with units of meters from a datum) coordinate systems.
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