I Have a shapefile of polygons and another one of points that are distributed over the polygons. I would like to create a kernel density estimate for each polygon based on the points it contains. unfortunately I was only able to create squared KDEs with the kde2d function from the MASS package. I would like the KDEs to be shaped as the polygons.
Any suggestions?
kde1 <- kde2d(poly$X, poly$Y, n=100,)
enter image description here
You can use the spatstat package for this. Here is an example of reading
in a shapefile from sf, generating random points and run kernel density
estimation of the intensity of points (points per unit area):
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
nc <- st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source `/usr/lib/R/site-library/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
nc_flat <- st_transform(nc, crs = 26917)
W <- as.owin(nc_flat$geometry[1]) # First county of North Carolina data set in spatstat format
library(spatstat)
X <- runifpoint(100, win = W)
plot(X, "Random points")
D <- density(X)
plot(D, main = "KDE")
OK! I managed to use my own points by using the 'ppp' function from the spatstat package.
C <- as.owin(polygon$geometry[n])
p<- ppp(points$X,points$Y, window = C)
D <- density(p)
[enter image description here][1]
[1]: https://i.stack.imgur.com/YZN0V.png
Related
I have a spatVector composed of a single-line geometry that covers the entire road network of my study area.
I would like to create a set of N random points over this geometry. I know how to do it in QGIS but I want to do it in R since I have to iterate this process 1'000 times and I want to create a loop.
Do you know any function to do this?
EDIT
First of all, I read my line shapefile using:
Road_network <- vect("path/to/file.shp)
Then I converted it into an SF object:
Road_network_SF <- st_as_sf(Road_network)
And finally, I use both the st_sample, getting the following results:
Random_points <- st_sample(Road_network_SF, size = 1799)
Random_points
Geometry set for 46350 features (with 44694 geometries empty)
Geometry type: MULTIPOINT
Dimension: XY
Bounding box: xmin: 4503139 ymin: 2504751 xmax: 4622797 ymax: 2613276
Projected CRS: ETRS89-extended / LAEA Europe
First 5 geometries:
MULTIPOINT EMPTY
MULTIPOINT EMPTY
MULTIPOINT EMPTY
MULTIPOINT ((4503139 2574957))
MULTIPOINT EMPTY
and the st_line_sample function, getting the following error:
Random_points <- st_line_sample(Road_network_SF, n = 1799)
Error in st_line_sample(Road_network_SF, n = 1799) :
inherits(x, "sfc_LINESTRING") non è TRUE
When I converted the spatVector to an sf object, this is what I get:
Road_network_SF
Simple feature collection with 1 feature and 2 fields
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 4500176 ymin: 2504157 xmax: 4626207 ymax: 2616041
Projected CRS: ETRS89-extended / LAEA Europe
FURTHER EDIT
The workflow proposed by #Gregory work really good, my error was due to a problem with the road shapefile. I changed it and no further problems occurred, thank you!
Thanks in advance!
You can sample random points along a vector geometry (like roads) with sf::st_sample(), however the results might seem confusing depending on how you look at them. Here's a reproducible example.
library(sf, quietly = TRUE)
#> Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(tigris, quietly = TRUE)
#> To enable
#> caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
library(ggplot2)
suppressMessages(
roads <- roads(state = "NC",
county = "Mecklenburg")
)
set.seed(1)
rpoints <- st_sample(roads, size = 5)
#> although coordinates are longitude/latitude, st_sample assumes that they are
#> planar
ggplot() +
geom_sf(data = roads, color = "grey") +
geom_sf(data = rpoints, color = "black")
We see on the map that we have generated 5 random points, as intended. Surprisingly, if you examine the structure of the rpoints object you'll see that it is a multipoint of length 21672, which you might think is the number of points. However, all but 5 of them have empty geometries. The reason is that there is a geometry (empty for most) for each of the objects that makes up the roads vector.
str(rpoints)
#> sfc_MULTIPOINT of length 21672; first list element: 'XY' num[0 , 1:2] MULTIPOINT EMPTY
head(rpoints)
#> Geometry set for 6 features (with 6 geometries empty)
#> Geometry type: MULTIPOINT
#> Dimension: XY
#> Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
#> Geodetic CRS: NAD83
#> First 5 geometries:
#> MULTIPOINT EMPTY
#> MULTIPOINT EMPTY
#> MULTIPOINT EMPTY
#> MULTIPOINT EMPTY
#> MULTIPOINT EMPTY
Here's how to get the real points out of there.
rpoints <- rpoints[!st_is_empty(rpoints)]
rpoints
#> Geometry set for 5 features
#> Geometry type: MULTIPOINT
#> Dimension: XY
#> Bounding box: xmin: -81.01691 ymin: 35.07471 xmax: -80.62246 ymax: 35.2948
#> Geodetic CRS: NAD83
#> MULTIPOINT ((-80.88764 35.2948))
#> MULTIPOINT ((-80.62246 35.18395))
#> MULTIPOINT ((-81.01691 35.07471))
#> MULTIPOINT ((-80.78909 35.12663))
#> MULTIPOINT ((-80.83055 35.16959))
Created on 2023-02-01 by the reprex package (v2.0.1)
I have a shape file that I can read like this in R:
library(rgdal)
shape <- readOGR(dsn = "~/path", layer = "a")
I am interested in the whole region that cover all polygons (black curve here). How to dissolve all polygons even those separated into one polygon like this?
I am open to solutions from R or Qgis
Using R & the sf package you can make a convex hull of the unioned (if necessary) shapefile. Since you haven't included data, I've used the nc data included with the sf package to illustrate the method.
library(dplyr)
library(sf)
library(ggplot2)
# setting up sample data,
# you'll need to use st_read() to read your shapefile, not readOGR()
nc <- st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source
#> `.../sf/shape/nc.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS: NAD27
nc <- nc[c(1:30, 85:81),] #Use some non-contiguous counties
# make a convex hull of the unioned geometries
nc_hull <- st_convex_hull(st_union(nc))
ggplot() +
geom_sf(data = nc, fill = NA, color = 'red') +
geom_sf(data = nc_hull, fill = NA, color = 'black')
Created on 2022-03-18 by the reprex package (v2.0.1)
How can you smooth the polygons of a map produced with ggplot and sf?
I have used the sf package to extract the polygons from a shapefile
geomunicipios <- st_read("ruta/archivo.shp")
Reading layer `archivo' from data source
`ruta\archivo.shp'
using driver `ESRI Shapefile'
Simple feature collection with 45 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -2.344411 ymin: 37.37375 xmax: -0.647983 ymax: 38.75509
Geodetic CRS: WGS 84
And ggplot2 to plot the map:
rmurcia <- ggplot(data = geomunicipios) +
geom_sf(aes(fill=columna),color="#FFFFFF",size=1)
To perform the smoothing of the polygons I have analyzed three alternatives:
i. package "smoothr":
geosmunicipios <- smooth(geomunicipios, method = "ksmooth", smoothness = 12)
ii. package "rmapshaper": geosmunicipios <- ms_simplify(geomunicipios, keep = 0.02500, weighting = 12)
iii. package "sf": geosmunicipios <- st_simplify(geomunicipios, dTolerance = 50, preserveTopology = TRUE)
You have to try different values of the parameters to adjust to the needs and obtain the desired result.
To reproduce the case, the download can be done from: centrodedescargas.cnig.es/CentroDescargas/index.jsp
And follow the links:
Información geográfica de referencia - Límites municipales, provinciales y autonómicos - Descargar: lineas_limite.zip.
And the path in the uncompressed folder:
SIGLIM_Publico_INSPIRE - SHP_ETRS89 - recintos_municipales_inspire_peninbal_etrs89 - recintos_municipales_inspire_peninbal_etrs89.shp
Finally, for this case I have chosen to use rmapshaper, it produces a satisfactory result with a reduced size of the .pdf file, where I include the graphic.
I have a polygon (zones) and a set of coordinates (points). I'd like to create a spatial kernal density raster for the entire polygon and extract the sum of the density by zone. Points outside of the polygon should be discarded.
library(raster)
library(tidyverse)
library(sf)
library(spatstat)
library(maptools)
load(url("https://www.dropbox.com/s/iv1s5butsx2v01r/example.RData?dl=1"))
# alternatively, links to gists for each object
# https://gist.github.com/ericpgreen/d80665d22dfa1c05607e75b8d2163b84
# https://gist.github.com/ericpgreen/7f4d3cee3eb5efed5486f7f713306e96
ggplot() +
geom_sf(data = zones) +
geom_sf(data = points) +
theme_minimal()
I tried converting to ppp with {spatstat} and then using density(), but I'm confused by the units in the result. I believe the problem is related to the units of the map, but I'm not sure how to proceed.
Update
Here's the code to reproduce the density map I created:
zones_owin <- as.owin(as_Spatial(zones))
pts <- st_coordinates(points)
p <- ppp(pts[,1], pts[,2], window=zones_owin, unitname=c("metre","metres"))
ds <- density(p)
r <- raster(ds)
plot(r)
Units are difficult when you work directly with geographic coordinates (lon, lat). If possible you should convert to planar coordinates (which is a requirement for spatstat) and proceed from there. The planar coordinates would typically be in units of meters, but I guess it depends on the specific projection and underlying ellipsoid etc. You can see this answer for how to project to planar coordinates with sf and export to spatstat format using maptools. Note: You have to manually choose a sensible projection (you can use http://epsg.io to find one) and you have to project both the polygon and the points.
Once everything is in spatstat format you can use density.ppp to do kernel smoothing. The resulting grid values (object of class im) are intensities of points, i.e., number of points per square unit (e.g. square meter). If you want to aggregate over some region you can use integral.im(..., domain = ...) to get the expected number of points in this region for a point process model with the given intensity.
I'm not sure if this answers all of your question, but should be a good start. Clarify in a comment or in your question should you need a different type of output.
It removes all points that are not inside one of the 'zone' polygons, counts them by zone and plots the zones colored by the number of points that fall within.
library(raster)
library(tidyverse)
library(sf)
#> Linking to GEOS 3.6.2, GDAL 2.2.3, PROJ 4.9.3
library(spatstat)
library(maptools)
#> Checking rgeos availability: TRUE
load(url("https://www.dropbox.com/s/iv1s5butsx2v01r/example.RData?dl=1"))
# alternatively, links to gists for each object
# https://gist.github.com/ericpgreen/d80665d22dfa1c05607e75b8d2163b84
# https://gist.github.com/ericpgreen/7f4d3cee3eb5efed5486f7f713306e96
p1 <- ggplot() +
geom_sf(data = zones) +
geom_sf(data = points) +
theme_minimal()
#Remove points outside of zones
points_inside <- st_intersection(points, zones)
#> although coordinates are longitude/latitude, st_intersection assumes that they are planar
#> Warning: attribute variables are assumed to be spatially constant throughout all
#> geometries
nrow(points)
#> [1] 308
nrow(points_inside)
#> [1] 201
p2 <- ggplot() +
geom_sf(data = zones) +
geom_sf(data = points_inside)
points_per_zone <- st_join(zones, points_inside) %>%
count(LocationID.x)
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
p3 <- ggplot() +
geom_sf(data = points_per_zone,
aes(fill = n)) +
scale_fill_viridis_c(option = 'C')
points_per_zone
#> Simple feature collection with 4 features and 2 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: 34.0401 ymin: -1.076718 xmax: 34.17818 ymax: -0.9755066
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +ellps=WGS84 +no_defs
#> # A tibble: 4 x 3
#> LocationID.x n geometry
#> * <dbl> <int> <POLYGON [°]>
#> 1 10 129 ((34.08018 -0.9755066, 34.0803 -0.9757393, 34.08046 -0.975…
#> 2 20 19 ((34.05622 -0.9959458, 34.05642 -0.9960835, 34.05665 -0.99…
#> 3 30 29 ((34.12994 -1.026372, 34.12994 -1.026512, 34.12988 -1.0266…
#> 4 40 24 ((34.11962 -1.001829, 34.11956 -1.002018, 34.11966 -1.0020…
cowplot::plot_grid(p1, p2, p3, nrow = 2, ncol = 2)
It seems I underestimated the difficulty of your problem. Is something like the plot below (& underlying data) what you're looking for?
It uses raster with ~50x50 grid, raster::focal with a window of 9x9 using the mean to interpolate the data.
At the moment I'm working on a project with point pattern events on a linear network (car crashes) and I'm reading chapter 17 of spatstat book: "Spatial Point Patterns: Methodology and Applications with R".
The authors of the book explain that they defined a new class of objects called lpp for analyzing point patterns on a linear network. The skeleton of each lpp object is a linnet object and there are several functions to create a linnet object. For my application the relevant functions are linnet and as.linnet. The function linnet creates a linear network object from the spatial location of each vertex and information about which vertices are joined by an edge, while the as.linnet function can be applied to a psp object which is transformed into linnet objects inferring the connectivity of the network using a specified distance threshold.
The reason why I'm asking this question is that I don't know how to efficiently create a linnet object starting from a sf object with a LINESTRING geometry. As far as I know, it's possible to transform the sf object into an sp object (i.e. a SpatialLines object), then I can transform the sp object into a psp object (using as.psp function) and then I can transform the psp object into a linnet object using the as.psp.linnet function (which is defined in the maptools package). The main problem with this approach (as the authors of the package said in their book) is that the inferred network is wrong every time an overpass or an underpass occurs in my network data since the corresponding linnet will create artificial intersections in the nework. Moreover, as the authors said in their book, the code gets exponentially slower.
The following code is a simplified version of what I did so far but I think that there must be an easier and better way to create a linnet object from an sf object. I would use the linnet function but the problem is that I don't know how to create a (sparse) adjacency matrix for the corresponding vertices of the network or a matrix of links between the edges of the network.
# packages
library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3
library(spatstat)
#> Loading required package: spatstat.data
#> Loading required package: nlme
#> Loading required package: rpart
#>
#> spatstat 1.61-0 (nickname: 'Puppy zoomies')
#> For an introduction to spatstat, type 'beginner'
#>
#> Note: spatstat version 1.61-0 is out of date by more than 11 weeks; a newer version should be available.
library(maptools)
#> Loading required package: sp
#> Checking rgeos availability: TRUE
library(osmdata)
#> Data (c) OpenStreetMap contributors, ODbL 1.0. http://www.openstreetmap.org/copyright
# download data
iow_polygon <- getbb("Isle of Wight, South East, England", format_out = "sf_polygon", featuretype = "state") %>%
st_transform(crs = 27700)
iow_highways <- st_read("https://download.geofabrik.de/europe/great-britain/england/isle-of-wight-latest.osm.pbf", layer = "lines") %>%
st_transform(crs = 27700)
#> Reading layer `lines' from data source `https://download.geofabrik.de/europe/great-britain/england/isle-of-wight-latest.osm.pbf' using driver `OSM'
#> Simple feature collection with 44800 features and 9 fields
#> geometry type: LINESTRING
#> dimension: XY
#> bbox: xmin: -5.716262 ymin: 43.35489 xmax: 1.92832 ymax: 51.16517
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +datum=WGS84 +no_defs
# subset the data otherwise the code takes ages
iow_highways <- iow_highways[iow_polygon, ] %>%
subset(grepl(pattern = c("primary|secondary|tertiary"), x = highway))
# transform as sp
iow_highways_sp <- as(iow_highways %>% st_geometry(), "Spatial")
# transform as psp
iow_highways_psp <- as.psp(iow_highways_sp)
# transform as linnet
iow_highways_linnet <- as.linnet.psp(iow_highways_psp, sparse = TRUE)
I can extract the coordinates of each vertex of the network
stplanr::line2points(iow_highways)
#> Simple feature collection with 2814 features and 1 field
#> geometry type: POINT
#> dimension: XY
#> bbox: xmin: 430780.7 ymin: 75702.05 xmax: 464851.7 ymax: 96103.72
#> epsg (SRID): 27700
#> proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs
#> First 10 features:
#> id geometry
#> 1 1 POINT (464851.7 87789.73)
#> 2 1 POINT (464435.4 88250.85)
#> 3 2 POINT (464390.9 87412.27)
#> 4 2 POINT (464851.7 87789.73)
#> 5 3 POINT (462574.6 88987.62)
#> 6 3 POINT (462334.6 88709.92)
#> 7 4 POINT (464066.9 87576.84)
#> 8 4 POINT (464390.9 87412.27)
#> 9 5 POINT (464420 88227.79)
#> 10 5 POINT (464398.7 88225.33)
but then I don't know how to build the adjacency matrix.
Created on 2019-12-02 by the reprex package (v0.3.0)
I’m not sure why you go through the psp format on the way to linnet.
Try to replace the last two lines of your first code chunk by:
iow_highways_linnet <- as.linnet.SpatialLines(iow_highways_sp)
This converts SpatialLines directly to linnet and fuses lines
that share a vertex. I don’t think an underpass will be fused to an
overpass unless both lines have a vertex at the intersection point.
See example below:
l1 <- sf::st_linestring(matrix(c(-1,1,-1,1,1,1,-1,-1), ncol = 2))
l2 <- sf::st_linestring(matrix(c(-1,-1,1,1,2,1,-1,-2), ncol = 2))
l_sf <- sf::st_sf(id = 1:2, geom = sf::st_sfc(l1,l2))
l_sp <- sf::as_Spatial(l_sf)
l <- maptools::as.linnet.SpatialLines(l_sp)
plot(l)
Just confirming that the spatstat package does not provide functions for handling other formats; our expectation is that maptools or other packages will provide format conversion code; this is not yet available for sf object formats, presumably because sf is relatively new.
The key question is whether an sf object with LINESTRING geometry contains enough information to determine connectivity of the network. If so, then I suggest you make a 2-column matrix listing all pairs of vertices which are joined by edges, and invoke spatstat::linnet. If not, then the available data are not sufficient...
Finally please note that the current development version of spatstat(1.61-0.061) available from GitHub is very much faster than the current release (1.61-0) for many operations on linear networks. It will be released publicly soon.