geocompx · Robinlovelace · Dec 13, 2021 · Nov 6, 2021 · Nov 8, 2021 · Nov 9, 2021
diff --git a/02-spatial-data.Rmd b/02-spatial-data.Rmd
@@ -717,11 +717,20 @@ We also encourage you to read @pebesma_spatial_2022 for the most comprehensive i
 
 ### An introduction to terra
 
-The **terra** package supports raster objects in R. 
-It provides an extensive set of functions to create, read, export, manipulate and process raster datasets.
-Aside from general raster data manipulation, **terra** provides many low-level functions that can form the basis to develop more advanced raster functionality.
+The **terra** package supports raster objects in R.
+Like its predecessor **raster** (created by the same developer, Robert Hijmans), it provides an extensive set of functions to create, read, export, manipulate and process raster datasets.
+**terra**'s functionality is largely the same as the more mature **raster** package, but there are some differences: **terra** functions are usually more computationally efficient than **raster** equivalents.
+<!-- todo: add evidence (RL 2021-11) -->
+On the other hand, the **raster** class system is popular and used by many other packages; as with **sf** and **sp**, the good news is you can seamlessly translate between the two types of object to ensure backwards compatibility with older scripts and packages, for example, with the functions `as.raster(my_rast)`  (see the previous chapter for more on the evolution of R packages for working with geographic data).
+
+```{r, echo=FALSE, eval=FALSE}
+# # test raster/terra conversions
+# See https://github.com/rspatial/terra/issues/399
+```
+
+In addition to functions for raster data manipulation, **terra** provides many low-level functions that can form a foundation for developing new tools for working with raster datasets.
 \index{terra (package)|see {terra}}
-**terra** also lets you work on large raster datasets that are too large to fit into the main memory. 
+**terra** also lets you work on large raster datasets that are too large to fit into the main memory.
 In this case, **terra** provides the possibility to divide the raster into smaller chunks, and processes these iteratively instead of loading the whole raster file into RAM.
 
 For the illustration of **terra** concepts, we will use datasets from the **spDataLarge**.
@@ -732,6 +741,7 @@ First, let's create a `SpatRaster` object named `my_rast`:
 ```{r 02-spatial-data-37, message=FALSE}
 raster_filepath = system.file("raster/srtm.tif", package = "spDataLarge")
 my_rast = rast(raster_filepath)
+class(my_rast)
 ```
 
 Typing the name of the raster into the console, will print out the raster header (dimensions, resolution, extent, CRS) and some additional information (class, data source, summary of the raster values): 

diff --git a/04-spatial-operations.Rmd b/04-spatial-operations.Rmd
diff --git a/07-read-write-plot.Rmd b/07-read-write-plot.Rmd
@@ -48,7 +48,7 @@ Various 'geoportals' (web services providing geospatial datasets such as [Data.g
 \index{geoportals}
 Some global geoportals overcome this issue.
 The [GEOSS portal](http://www.geoportal.org/) and the [Copernicus Open Access Hub](https://scihub.copernicus.eu/), for example, contain many raster datasets with global coverage.
-A wealth of vector datasets can be accessed from the National Aeronautics and Space Administration agency's (NASA) [SEDAC](http://sedac.ciesin.columbia.edu/) portal and the European Union's [INSPIRE geoportal](http://inspire-geoportal.ec.europa.eu/), with global and regional coverage.
+A wealth of vector datasets can be accessed from the [SEDAC](http://sedac.ciesin.columbia.edu/) portal run by the National Aeronautics and Space Administration (NASA) and the European Union's [INSPIRE geoportal](http://inspire-geoportal.ec.europa.eu/), with global and regional coverage.
 
 Most geoportals provide a graphical interface allowing datasets to be queried based on characteristics such as spatial and temporal extent, the United States Geological Services' [EarthExplorer](https://earthexplorer.usgs.gov/) being a prime example.
 *Exploring* datasets interactively on a browser is an effective way of understanding available layers.

diff --git a/code/04-spatial-join.R b/code/04-spatial-join.R
@@ -10,10 +10,10 @@ library(tmap)
 
 if(!exists("random_joined")) {
   set.seed(2018)
-  bb_world = st_bbox(world)
+  bb = st_bbox(world)
   random_df = tibble::tibble(
-    x = runif(n = 10, min = bb_world[1], max = bb_world[3]),
-    y = runif(n = 10, min = bb_world[2], max = bb_world[4])
+    x = runif(n = 10, min = bb[1], max = bb[3]),
+    y = runif(n = 10, min = bb[2], max = bb[4])
   )
   random_points = st_as_sf(random_df, coords = c("x", "y")) %>% 
     st_set_crs(4326)
@@ -29,26 +29,26 @@ random_joined$name_long = as.character(random_joined$name_long)
 jm0 = tm_shape(world) + tm_borders(lwd = 0.2) + tm_format("World")
 
 jm1 = jm0 +
-  tm_shape(shp = random_points, bbox = bb_world) +
+  tm_shape(shp = random_points, bbox = bb) +
   tm_symbols(col = "black", shape = 4, border.lwd = 2) +
   tm_layout(scale = 1, legend.bg.color = "white", legend.bg.alpha = 0.3, legend.position = c("right", "bottom"))
 
 jm2 = jm0 +
-  tm_shape(world_random, bbox = bb_world) +
+  tm_shape(world_random, bbox = bb) +
   tm_fill(col = "name_long", palette = "Dark2") +
   tm_layout(legend.show = FALSE)
   #tm_borders(col = "name_long", lwd = 4) + # issue with tmap: no variable border
   # tm_layout(scale = 1, legend.bg.color = "white", legend.bg.alpha = 0.3, legend.position = c("right", "bottom"))
 
 jm3 = jm0 +
-  tm_shape(shp = random_joined, bbox = bb_world) +
+  tm_shape(shp = random_joined, bbox = bb) +
   tm_symbols(col = "name_long", shape = 4, border.lwd = 2, palette = "Dark2") +
   tm_layout(legend.show = FALSE)
 # +
 #   tm_layout(scale = 1, legend.bg.color = "white", legend.bg.alpha = 0.3, legend.position = c("right", "bottom"))
 
 jm4 = jm0 +
-  tm_shape(shp = random_joined, bbox = bb_world) +
+  tm_shape(shp = random_joined, bbox = bb) +
   tm_symbols(col = "name_long", shape = 4, border.lwd = 2, palette = "Dark2") +
   tm_layout(legend.only = TRUE)
 

diff --git a/code/chapters/04-spatial-operations.R b/code/chapters/04-spatial-operations.R
@@ -117,10 +117,10 @@ lengths(sel) > 0
 
 ## ----04-spatial-operations-19--------------------------------------------
 set.seed(2018) # set seed for reproducibility
-(bb_world = st_bbox(world)) # the world's bounds
+(bb = st_bbox(world)) # the world's bounds
 random_df = tibble(
-  x = runif(n = 10, min = bb_world[1], max = bb_world[3]),
-  y = runif(n = 10, min = bb_world[2], max = bb_world[4])
+  x = runif(n = 10, min = bb[1], max = bb[3]),
+  y = runif(n = 10, min = bb[2], max = bb[4])
 )
 random_points = random_df %>% 
   st_as_sf(coords = c("x", "y")) %>% # set coordinates

diff --git a/code/de_9im.R b/code/de_9im.R
@@ -0,0 +1,82 @@
+#' This function visualises sf objects and returns info on the
+#' types of spatial relationship there is between them
+#'
+#' Context: [robinlovelace/geocompr#677](https://github.com/Robinlovelace/geocompr/issues/677)
+#'
+#' @examples
+#' library(sf)
+#' x = st_sfc(st_polygon(list(rbind(c(0, 0), c(1, 0), c(1, 1), c(0, 0)))))
+#' y = st_sfc(st_polygon(list(rbind(c(0, 0), c(0, 1), c(1, 1), c(0, 0)))))
+#' de_9im(x, y)
+#' p3 = st_sfc(st_polygon(list(rbind(c(0.7, 0.3), c(0.7, 0.95), c(0.9, 0.95), c(0.7, 0.3)))))
+#' p4 = st_sfc(st_polygon(list(rbind(c(0.6, 0.1), c(0.7, 0.5), c(0.9, 0.5), c(0.6, 0.1)))))
+#' p5 = st_sfc(st_polygon(list(rbind(c(0, 0.2), c(0, 1), c(0.9, 1), c(0, 0.2)))))
+#' de_9im(x, p3)
+de_9im = function(x,
+                  y,
+                  object_names = c("x", "y"),
+                  plot = TRUE,
+                  funs = list(
+                    "st_intersects",
+                    "st_disjoint",
+                    "st_touches",
+                    "st_crosses",
+                    "st_within",
+                    "st_contains",
+                    "st_contains_properly",
+                    "st_overlaps",
+                    "st_equals",
+                    "st_covers",
+                    "st_covered_by"
+                    # ,
+                    # "st_equals_exact" # requuires par argument
+                    ),
+                  include_relate = TRUE,
+                  sparse = FALSE,
+                  output = "character",
+                  collapse = " ✓\n"
+                  ) {
+  require("sf")
+  if (is(x, "sfc") && is(y, "sfc")) {
+    x = st_sf(data.frame(Object = object_names[1]), geometry = x)
+    y = st_sf(data.frame(Object = object_names[2]), geometry = y)
+  }
+  xy = rbind(x, y)
+  funs_matched = lapply(funs, match.fun)
+  res = lapply(seq(length(funs)), function(i) {
+    funs_matched[[i]](x, y, sparse = sparse)
+  })
+  res = unlist(res)
+  if(output == "character") {
+    res = unlist(funs)[res]
+  }
+  if(include_relate) {
+    relation = sf::st_relate(x, y)
+    relate_text = paste0(" \nDE-9IM string: \n", relation) 
+    res = c(res, relate_text)
+  }
+  if(plot) {
+    res_text = paste(res, collapse = collapse)
+    message("Object x has the following spatial relations to y: ", res_text)
+    res = de_9im_plot(xy, label = res_text)
+  }
+  res
+}
+
+de_9im_plot = function(xy, label = "test", alpha = 0.5, show.legend = FALSE, x = 0.1, y = 0.95, theme = ggplot2::theme_void()) {
+  require("ggplot2", quietly = TRUE)
+  # browser()
+  ggplot(xy) + geom_sf(aes(fill = Object), alpha = alpha, show.legend = show.legend) +
+    annotate("text", x = 0.1, y = 0.95, label = label, hjust = "left", vjust = "top") +
+    theme
+}
+
+# # Test code to functionalize:
+# theme_set(new = theme_void())
+# g1 = ggplot(ps1) + geom_sf(aes(fill = Object), alpha = 0.5, show.legend = FALSE)
+# # g1 + annotate("text", x = 0.3, y = 0.9, label = "st_intersects(Polygon1, Polygon2)")
+# g1 + annotate("text", x = 0.1, y = 0.95, label = "intersects TRUE\ndisjoint     FALSE\ntouches    TRUE\n", hjust = "left", vjust = "top")
+# # Try annotating only which type of relations apply
+# # g1 + annotate("text", x = 0.1, y = 0.95, label = "Relations: intersects, touches", hjust = "left", vjust = "top")
+# g1an = g1 + 
+#
diff --git a/geocompr.bib b/geocompr.bib
@@ -428,7 +428,7 @@ @article{clementini_comparison_1995
   number = {3},
   pages = {149--178},
   issn = {1069-0115},
-  doi = {10/ddtnhx},
+  doi = {10.1016/1069-0115(94)00033-X},
   url = {https://www.sciencedirect.com/science/article/pii/106901159400033X},
   urldate = {2021-11-13},
   langid = {english}
@@ -531,14 +531,6 @@ @article{douglas_algorithms_1973
   keywords = {\#nosource}
 }
 
-@manual{dunnington_ggspatial_2021,
-  type = {manual},
-  title = {Ggspatial: Spatial Data Framework for Ggplot2},
-  author = {Dunnington, Dewey},
-  date = {2021},
-  url = {https://CRAN.R-project.org/package=ggspatial}
-}
-
 @article{eddelbuettel_extending_2018,
   title = {Extending {{R}} with {{C}}++: A {{Brief Introduction}} to {{Rcpp}}},
   shorttitle = {Extending {{R}} with {{C}}++},
@@ -561,8 +553,7 @@ @inproceedings{egenhofer_mathematical_1990
   booktitle = {Proc. the Fourth International Symposium on Spatial Data Handing},
   author = {Egenhofer, Max and Herring, John},
   date = {1990},
-  pages = {803--813},
-  keywords = {⛔ No DOI found}
+  pages = {803--813}
 }
 
 @article{essd-11-647-2019,
@@ -624,14 +615,6 @@ @book{gillespie_efficient_2016
   keywords = {\#nosource}
 }
 
-@manual{giraud_mapsf_2021,
-  type = {manual},
-  title = {Mapsf: Thematic Cartography},
-  author = {Giraud, Timothée},
-  date = {2021},
-  url = {https://CRAN.R-project.org/package=mapsf}
-}
-
 @article{goetz_evaluating_2015,
   title = {Evaluating Machine Learning and Statistical Prediction Techniques for Landslide Susceptibility Modeling},
   author = {Goetz, J.N. and Brenning, A. and Petschko, H. and Leopold, P.},
@@ -757,14 +740,6 @@ @book{hijmans_geosphere_2016
   keywords = {\#nosource}
 }
 
-@manual{hijmans_terra_2021,
-  type = {manual},
-  title = {Terra: Spatial Data Analysis},
-  author = {Hijmans, Robert J.},
-  date = {2021},
-  url = {https://rspatial.org/terra/}
-}
-
 @book{hollander_transport_2016,
   title = {Transport {{Modelling}} for a {{Complete Beginner}}},
   author = {Hollander, Yaron},
@@ -1251,14 +1226,6 @@ @article{moreno-monroy_public_2017
   keywords = {\#nosource,Accessibility,Inequality,Latin America,Public transport,Schools}
 }
 
-@manual{morganwall_rayshader_2021,
-  type = {manual},
-  title = {Rayshader: Create Maps and Visualize Data in {{2D}} and {{3D}}},
-  author = {Morgan-Wall, Tyler},
-  date = {2021},
-  url = {https://CRAN.R-project.org/package=rayshader}
-}
-
 @article{muenchow_geomorphic_2012,
   title = {Geomorphic Process Rates of Landslides along a Humidity Gradient in the Tropical {{Andes}}},
   author = {Muenchow, Jannes and Brenning, Alexander and Richter, Michael},
@@ -1498,23 +1465,8 @@ @article{pebesma_simple_2018
 @book{pebesma_spatial_2022,
   title = {Spatial {{Data Science}} with Applications in {{R}}},
   author = {Pebesma, Edzer and Bivand, Roger},
-  date = {2022}
-}
-
-@manual{pebesma_stars_2021,
-  type = {manual},
-  title = {Stars: Spatiotemporal Arrays, Raster and Vector Data Cubes},
-  author = {Pebesma, Edzer},
-  date = {2021},
-  url = {https://CRAN.R-project.org/package=stars}
-}
-
-@manual{pedersen_gganimate_2020,
-  type = {manual},
-  title = {Gganimate: A Grammar of Animated Graphics},
-  author = {Pedersen, Thomas Lin and Robinson, David},
-  date = {2020},
-  url = {https://CRAN.R-project.org/package=gganimate}
+  date = {2022},
+  url = {https://r-spatial.org/book}
 }
 
 @book{perpinan_rastervis_2016,
@@ -1601,19 +1553,6 @@ @book{rodrigue_geography_2013
   pagetotal = {432}
 }
 
-@article{Roussel2020,
-  title = {{{lidR}}: An r Package for Analysis of Airborne Laser Scanning ({{ALS}}) Data},
-  author = {Roussel, Jean-Romain and Auty, David and Coops, Nicholas C. and Tompalski, Piotr and Goodbody, Tristan R.H. and Meador, Andrew Sánchez and Bourdon, Jean-François and de Boissieu, Florian and Achim, Alexis},
-  options = {useprefix=true},
-  date = {2020-12},
-  journaltitle = {Remote Sensing of Environment},
-  volume = {251},
-  pages = {112061},
-  publisher = {{Elsevier BV}},
-  doi = {10/ghddxb},
-  url = {https://doi.org/10.1016/j.rse.2020.112061}
-}
-
 @inproceedings{rowlingson_rasp:_2003,
   title = {Rasp: A {{Package}} for {{Spatial Statistics}}},
   booktitle = {Proceedings of the 3rd {{International Workshop}} on {{Distributed Statistical Computing}}},
@@ -1679,7 +1618,7 @@ @article{shen_classification_2018
   number = {2},
   pages = {514--541},
   issn = {1467-9671},
-  doi = {10/gnhcx9},
+  doi = {10.1111/tgis.12328},
   url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12328},
   urldate = {2021-11-13},
   langid = {english},

diff --git a/index.Rmd b/index.Rmd
@@ -34,12 +34,11 @@ This is the online home of *Geocomputation with R*, a book on geographic data an
 <a href="https://www.routledge.com/9781138304512"><img src="images/cover.png" width="250" height="375" alt="The geocompr book cover" align="right" style="margin: 0 1em 0 1em" /></a>
 
 **Note**: The first edition of the book has been published by CRC Press in the [R Series](https://www.routledge.com/Chapman--HallCRC-The-R-Series/book-series/CRCTHERSER).
-You can buy the book from [CRC Press](https://www.routledge.com/9781138304512), or [Amazon](https://www.amazon.com/Geocomputation-R-Robin-Lovelace-dp-0367670577/dp/0367670577/), and see the archived first edition on the open source book platform [bookdown.org](https://bookdown.org/robinlovelace/geocompr/). 
+You can buy the book from [CRC Press](https://www.routledge.com/9781138304512), or [Amazon](https://www.amazon.com/Geocomputation-R-Robin-Lovelace-dp-0367670577/dp/0367670577/), and see the archived **First Edition** hosted on [bookdown.org](https://bookdown.org/robinlovelace/geocompr/). 
 
-Inspired by [**bookdown**](https://github.com/rstudio/bookdown) and the Free and Open Source Software for Geospatial ([FOSS4G](https://foss4g.org/)) movement, this book is open source.
-This ensures its contents are reproducible and publicly accessible for people worldwide.
-
-The online version of the book is hosted at [geocompr.robinlovelace.net](https://geocompr.robinlovelace.net) and kept up-to-date by [GitHub Actions](https://github.com/Robinlovelace/geocompr/actions), which provides information on its 'build status' as follows:
+Inspired by [**bookdown**](https://bookdown.org/) and the Free and Open Source Software for Geospatial ([FOSS4G](https://foss4g.org/)) movement, the code and prose underlying this book is open source, ensuring it's reproducible, accessible and modifiable (e.g. in case you find an inevitable typo) for the benefit of people worldwide.
+The online version of the book is hosted at [geocompr.robinlovelace.net](https://geocompr.robinlovelace.net) and kept up-to-date by [GitHub Actions](https://github.com/Robinlovelace/geocompr/actions).
+Its current 'build status' as follows:
 
 [![Actions](https://github.com/Robinlovelace/geocompr/workflows/Render/badge.svg)](https://github.com/Robinlovelace/geocompr/actions)
 ```