unifies ref

Author: Nowosad

02-spatial-data.Rmd

@@ -856,7 +856,7 @@ To turn off S2 for the entirety of a project, you can create a file called .Rpro
 
 The spatial raster data model represents the world with the continuous grid of cells (often also called pixels; Figure \@ref(fig:raster-intro-plot):A)\index{raster data model}.
 This data model often refers to so-called regular grids, in which each cell has the same, constant size -- and we will focus on the regular grids in this book only.
-However, several other types of grids exist, including rotated, sheared, rectilinear, and curvilinear grids (see chapter 1 of @pebesma_spatial_2022 or chapter 2 of @tennekes_elegant_2022).
+However, several other types of grids exist, including rotated, sheared, rectilinear, and curvilinear grids (see chapter 1 of @pebesmaSpatialDataScience2023 or chapter 2 of @tennekes_elegant_2022).
 
 The raster data model usually consists of a raster header\index{raster!header}
 and a matrix (with rows and columns) representing equally spaced cells (often also called pixels; Figure \@ref(fig:raster-intro-plot):A).^[
@@ -917,7 +917,7 @@ The **terra** package mostly relies on a large number of built-in functions, whe
 On the other hand, **stars** uses some built-in functions (usually with names starting with `st_`), some existing **dplyr** functions (e.g., `filter()` or `slice()`), and also has its own methods for existing R functions (e.g., `split()` or `aggregate()`).
 
 Importantly, it is straightforward to convert objects from **terra** to **stars** (using `st_as_stars()`) and the other way round (using `rast()`).
-We also encourage you to read @pebesma_spatial_2022 for the most comprehensive introduction to the **stars** package.
+We also encourage you to read @pebesmaSpatialDataScience2023 for the most comprehensive introduction to the **stars** package.
 
 ### Introduction to terra
 

07-reproj.Rmd

@@ -100,7 +100,7 @@ These string representations, built on a key=value form (e.g, `+proj=longlat +da
 \index{CRS!proj-string}
 Recent PROJ versions (6+) still allow use of proj-strings to define coordinate operations, but some proj-string keys (`+nadgrids`, `+towgs84`, `+k`, `+init=epsg:`) are either no longer supported or are discouraged.
 Additionally, only three datums (i.e., WGS84, NAD83, and NAD27) can be directly set in proj-string.
-Longer explanations of the evolution of CRS definitions and the PROJ library can be found in @bivand_progress_2021, chapter 2 of @pebesma_spatial_2022, and a [blog post by Floris Vanderhaeghe, available at inbo.github.io/tutorials/tutorials/spatial_crs_coding/](https://inbo.github.io/tutorials/tutorials/spatial_crs_coding/).
+Longer explanations of the evolution of CRS definitions and the PROJ library can be found in @bivand_progress_2021, chapter 2 of @pebesmaSpatialDataScience2023, and a [blog post by Floris Vanderhaeghe, available at inbo.github.io/tutorials/tutorials/spatial_crs_coding/](https://inbo.github.io/tutorials/tutorials/spatial_crs_coding/).
 Also, as outlined in the [PROJ documentation](https://proj.org/development/reference/cpp/cpp_general.html) there are different versions of the WKT CRS format including WKT1 and two variants of WKT2, the latter of which (WKT2, 2018 specification) corresponds to the ISO 19111:2019 [@opengeospatialconsortium_wellknown_2019].
 
 ## Querying and setting coordinate systems {#crs-setting}

16-synthesis.Rmd

@@ -120,7 +120,7 @@ Some topics and themes appear repeatedly, with the aim of building essential ski
 
 We deliberately omitted some topics that are covered in-depth elsewhere.
 Statistical modeling of spatial data such as point pattern analysis\index{point pattern analysis}, spatial interpolation\index{spatial interpolation} (e.g., kriging) and spatial regression\index{spatial regression}, for example, are mentioned in the context of machine learning in Chapter \@ref(spatial-cv) but not covered in detail.
-There are already excellent resources on these methods, including statistically orientated chapters in @pebesma_spatial_2023 and books on point pattern analysis [@baddeley_spatial_2015], Bayesian techniques applied to spatial data [@gomez-rubio_bayesian_2020; @moraga_spatial_2023], and books focused on particular applications such as health [@moraga_geospatial_2019] and [wildfire severity analysis](https://bookdown.org/mcwimberly/gdswr-book/application---wildfire-severity-analysis.html) [@wimberly_geographic_2023].
+There are already excellent resources on these methods, including statistically orientated chapters in @pebesmaSpatialDataScience2023 and books on point pattern analysis [@baddeley_spatial_2015], Bayesian techniques applied to spatial data [@gomez-rubio_bayesian_2020; @moraga_spatial_2023], and books focused on particular applications such as health [@moraga_geospatial_2019] and [wildfire severity analysis](https://bookdown.org/mcwimberly/gdswr-book/application---wildfire-severity-analysis.html) [@wimberly_geographic_2023].
 Other topics which received limited attention were remote sensing and using R alongside (rather than as a bridge to) dedicated GIS software.
 There are many resources on these topics, including a [discussion on remote sensing in R](https://github.com/r-spatial/discuss/issues/56), @wegmann_remote_2016 and the GIS-related teaching materials available from [Marburg University](https://geomoer.github.io/moer-info-page/courses.html).