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prepr <img src="man/figures/hex-prepr.png" align="right" height="139" />

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An R package to repair broken GIS polygons using the prepair C++ library.

Installation

The prepair C++ library need these two libraries to compile:

The R package prepr solves the CGAL dependencies by shipping with a subset of the CGAL header. We also use rwinlib to provide GDAL on Windows in order to build this package from source. You will need the latest version of rtools in order to build the source code on Windows.

prepair can also use these optional libraries:

They are disabled by default on Windows but required if you want to build the package in a Linux/OS X environment. After installing all these libraries, you can now install the development version of the prepr R package from Gitlab using the remotes R package with:

# install.packages("remotes")
remotes::install_gitlab("dickoa/prepr")
remotes::install_github("dickoa/prepr") ## mirror

A quick tutorial

This is a simple tutorial which shows you how to solve common problems we can find with polygons:

A ‘bowtie’ polygon:

library(prepr)
library(sf)

p1 <- st_as_sfc("POLYGON((0 0, 0 10, 10 0, 10 10, 0 0))")
st_is_valid(p1, reason = TRUE)
#> [1] "Self-intersection[5 5]"
p11 <- st_prepair(p1)
st_is_valid(p11)
#> [1] TRUE

st_as_text(p11)
#> [1] "MULTIPOLYGON (((0 0, 5 5, 0 10, 0 0)), ((5 5, 10 0, 10 10, 5 5)))"

par(mfrow = c(1, 2))
plot(p1, main = "RAW", col = "#D7722C")
plot(p11, main = "Repaired", col = "#D7722C")
<img src="man/figures/README-p1-1.svg" width="100%" />

Square with wrong orientation:

p2 <- st_as_sfc("POLYGON((0 0, 0 10, 10 10, 10 0, 0 0))")
st_is_valid(p2, reason = TRUE)
#> [1] "Valid Geometry"
plot(p2, main = "RAW", col = "#D7722C")
<img src="man/figures/README-p2-1.svg" width="100%" />

Inner ring with one edge sharing part of an edge of the outer ring:

p3 <- st_as_sfc("POLYGON((0 0, 10 0, 10 10, 0 10, 0 0),(5 2, 5 7, 10 7, 10 2, 5 2))")
st_is_valid(p3, reason = TRUE)
#> [1] "Self-intersection[10 2]"
p33 <- st_prepair(p3)
st_is_valid(p33)
#> [1] TRUE

st_as_text(p33)
#> [1] "POLYGON ((0 0, 10 0, 10 2, 5 2, 5 7, 10 7, 10 10, 0 10, 0 0))"

par(mfrow = c(1, 2))
plot(p3, main = "RAW", col = "#D7722C")
plot(p33, main = "Repaired", col = "#D7722C")
<img src="man/figures/README-p3-1.svg" width="100%" />

Dangling edge:

p4 <- st_as_sfc("POLYGON((0 0, 10 0, 15 5, 10 0, 10 10, 0 10, 0 0))")
st_is_valid(p4, reason = TRUE)
#> [1] "Self-intersection[15 5]"
p44 <- st_prepair(p4)
st_is_valid(p44)
#> [1] TRUE

st_as_text(p44)
#> [1] "POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0))"

par(mfrow = c(1, 2))
plot(p4, main = "RAW", col = "#D7722C")
plot(p44, main = "Repaired", col = "#D7722C")
<img src="man/figures/README-p4-1.svg" width="100%" />

Two adjacent inner rings:

p6 <- st_as_sfc("POLYGON((0 0, 10 0, 10 10, 0 10, 0 0), (1 1, 1 8, 3 8, 3 1, 1 1), (3 1, 3 8, 5 8, 5 1, 3 1))")
st_is_valid(p6, reason = TRUE)
#> [1] "Self-intersection[3 8]"
p66 <- st_prepair(p6)
st_is_valid(p66)
#> [1] TRUE

st_as_text(p66)
#> [1] "POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0), (1 1, 1 8, 3 8, 5 8, 5 1, 3 1, 1 1))"

par(mfrow = c(1, 2))
plot(p6, main = "RAW", col = "#D7722C")
plot(p66, main = "Repaired", col = "#D7722C")
<img src="man/figures/README-p6-1.svg" width="100%" />

Polygon with an inner ring inside another inner ring:

p7 <- st_as_sfc("POLYGON((0 0, 10 0, 10 10, 0 10, 0 0), (2 8, 5 8, 5 2, 2 2, 2 8), (3 3, 4 3, 3 4, 3 3))")
st_is_valid(p7, reason = TRUE)
#> [1] "Holes are nested[3 3]"
p77 <- st_prepair(p7)
st_is_valid(p77)
#> [1] TRUE

st_as_text(p77)
#> [1] "MULTIPOLYGON (((0 0, 10 0, 10 10, 0 10, 0 0), (2 2, 2 8, 5 8, 5 2, 2 2)), ((3 3, 4 3, 3 4, 3 3)))"

par(mfrow = c(1, 2))
plot(p7, main = "RAW", col = "#D7722C")
plot(p77, main = "Repaired", col = "#D7722C")
<img src="man/figures/README-p7-1.svg" width="100%" /> <!-- ## A exemple with a real dataset --> <!-- ### Reading the data --> <!-- ```{r read_data, cache = TRUE} --> <!-- (clc1 <- read_sf("https://github.com/tudelft3d/prepair/raw/master/data/CLC2006_180927.geojson")) --> <!-- (clc2 <- read_sf("https://github.com/tudelft3d/prepair/raw/master/data/CLC2006_2018418.geojson")) --> <!-- par(mfrow = c(1, 2)) --> <!-- plot(st_geometry(clc1), main = "CLC2006_180927", col = 'lightblue', axes = TRUE, graticule = TRUE, lwd = 0.2, cex.axis = 0.5) --> <!-- plot(st_geometry(clc2), main = "CLC2006_2018418", col = "lightblue", axes = TRUE, graticule = TRUE, lwd = 0.2, cex.axis = 0.5) --> <!-- ``` --> <!-- ### Check if it's valid and repair it --> <!-- ```{r clean_it, cache = TRUE} --> <!-- st_is_valid(clc1, reason = TRUE) --> <!-- (clc1_rpr <- st_prepair(clc1)) --> <!-- st_is_valid(clc1_rpr) --> <!-- st_is_valid(clc2, reason = TRUE) --> <!-- (clc2_rpr <- st_prepair(clc2)) --> <!-- st_is_valid(clc2_rpr) --> <!-- ``` --> <!-- ### How fast is `st_prepair` ? --> <!-- `prepr::st_prepair` is fast and can be in some cases faster than `sf::st_make_valid` --> <!-- ```{r bench, cache = TRUE} --> <!-- (bnch1 <- bench::mark(st_make_valid(clc1), st_prepair(clc1), check = FALSE)) --> <!-- summary(bnch1, relative = TRUE) --> <!-- (bnch2 <- bench::mark(st_make_valid(clc2), st_prepair(clc2), check = FALSE)) --> <!-- summary(bnch2, relative = TRUE) --> <!-- ``` --> <!-- You also have cases where it's slower to `sf::st_make_valid`, let's use this data from a [closed issue](https://github.com/r-spatial/sf/issues/1280) in the `sf` R package. --> <!-- ```{r new_data, cache = TRUE} --> <!-- ## need vsicurl --> <!-- (agb <- read_sf("/vsicurl/http://files.hawaii.gov/dbedt/op/gis/data/2015AgBaseline.shp.zip")) --> <!-- all(st_is_valid(agb)) --> <!-- all(st_is_valid(st_make_valid(agb))) --> <!-- all(st_is_valid(st_prepair(agb))) --> <!-- plot(st_geometry(agb), main = "2015 Agriculture baseline", col = 'lightblue', axes = TRUE, graticule = TRUE, lwd = 0.2, cex.axis = 0.5) --> <!-- ``` --> <!-- `sf::st_make_valid` is faster with this data. --> <!-- ```{r bench2, cache = TRUE} --> <!-- (bnch3 <- bench::mark(st_make_valid(agb), st_prepair(agb), check = FALSE)) --> <!-- summary(bnch3, relative = TRUE) --> <!-- ``` -->

Details and how to cite

Details of how we automatically repair broken polygons, and what results you can expect, are available in this scientific article by the original authors of prepair:

Ledoux, H., Arroyo Ohori, K., and Meijers, M. (2014). A triangulation-based approach to automatically repair GIS polygons. Computers & Geosciences 66:121–131. [DOI] [PDF]

If you use the R package prepr for a scientific project, please cite their original work.

License

This package is released under the GPL-3 license.