convex hullpointpolygon-creationqgis
I have a shapefile layer with a set of points.
I would like to enclose them (or turn them) into the polygon (with the line in the middle).
I tried this:
which led me to the Convex Hull tool.
Unfortunately, it looks like, that this operation can be applied to the cluster of points instead of single located points.
I had no problem with the convex hull (vector > geoprocessing tools > convex hull) of QGIS 2. It used to make a convex hull around a set of points immediately. In QGIS 3, the same raises the error "Cannot calculate convex hull for a single Point feature (try 'Minimum bounding geometry' algorithm instead)."
Greg gave the answer (toolbox -> Vector Geometry -> Minimum Bounding Geometry):
In the drop box "Geometry type", choose "Convex hull".
I have a hidden function for calculating the Alpha Convex Hull (Pateiro-Lopez & Rodriguez-Casal 2009) in the development version of spatialEco. The reason this function is not in the CRAN release is due to licensing issues in the alphahull package.
Please note that this statistic only works for [x,y] data. Polygons are tricky because the bounding geometry is based on the vertices of each polygons, functionally making it based on points ([x,y] vertices). Because of this, if you want an alpha hull, it takes some extra data prep to get points representing the polygon boundaries.
Please install alphahull and remotes packages from CRAN and then install the development version of spatialEco using remotes::install_github.
remotes::install_github("jeffreyevans/spatialEco")
library(sp)
library(sf)
library(spatialEco)
library(dplyr)
Here we add some example data and create your polygons
data(meuse)
sp::coordinates(meuse) = ~x+y
meuse <- as(meuse, "sf")
meuse_poly <- sf::st_buffer(meuse, dist = meuse$elev*15)
Now, we can get [x,y] data from the polygons using the sf::st_segmentize function.
poly_points <- sf::st_segmentize(meuse_poly, dfMaxLength = 5) %>%
sf::st_coordinates() %>%
as.data.frame() %>%
dplyr::select(X, Y) %>%
sf::st_as_sf(coords = c("X", "Y"))
We can then pass the points the the convexHull function
a <- spatialEco::convexHull(poly_points,alpha = 100000, sp=FALSE)
plot(sf::st_geometry(a), cex=1.5, col="red")
plot(sf::st_geometry(meuse_poly), add=TRUE)
Let's test multiple alpha values to find an optima.
par(mfcol=c(2,2))
for (a in c(500, 1500, 5000, 100000)) {
ch <- spatialEco::convexHull(poly_points, alpha = a, sp = FALSE)
plot(sf::st_geometry(ch), cex=1.5, col="red")
plot(sf::st_geometry(meuse_poly), add=TRUE)
title(paste0("alpha=", a))
}
References
Pateiro-Lopez & Rodriguez-Casal (2009) Generalizing the Convex Hull of a Sample: The R Package alphahull. Journal of Statistical Software 34(5):1-28 http://www.jstatsoft.org/v34/i05/paper
Best Answer
Instead of using the "Convex hull" tool, use the "Minimum bounding geometry" tool by specifying the desired type of geometry output as shown in the image below.