spatstat

How to generate a list of segments from a list of random self-intercepting lines (psp in R)?

由 拜拜、爱过 提交于 2019-12-11 15:03:56
问题 I'm using X=rpoisline(4) to generate lines and plot them with plot(X) . With X$ends I have their coordinates and their intersection points with selfcrossing.psp(X) (In R with spatstat : library(spatstat) ). I need to get a list of segments and their coordinates and be able to manipulate them (change their orientation, position, intersection...). Those segments have to be defined by the intersection of a line with an other line and with the window. So, am I missing a simple way to convert a

How can I include survey weights in a poisson pint process model fitted to a logistic regression quadrature scheme?

由 ε祈祈猫儿з 提交于 2019-12-11 14:37:08
问题 Is it possible to include weights in a poisson point process model fitted to a logistic regression quadrature scheme? My data is a stratified sample and I would like to account for this sampling strategy in order to have valid population level predictions. 回答1: Here are a few lines to elaborate on the answer by @adrian-baddeley. If you have the setup of your related question and we imagine you have the weights and two covariates in a data.frame in the same order as the points of your

Can I convert kernel density plots to raster and then overlay them using map algebra?

由 回眸只為那壹抹淺笑 提交于 2019-12-11 06:15:19
问题 I am currently exploring three shapefiles, each with point data, and all confined to the same window. I also have them in ppp format which I've used to create kernel density maps. plot(density.ppp(smktppp, 0.5, edge=T), main="Supermarket Density") plot(density.ppp(tptppp, 0.5, edge=T), main="Transport Density") plot(density.ppp(farmppp, 0.5, edge=T), main="Urban Farm Density") I would like to overlay these plots, using map algebra, or fuzzy logic, etc, to create one output map showing the

directional testing of spatial clustering by distance from source

由 試著忘記壹切 提交于 2019-12-07 14:56:04
问题 I have a spatial dataset of animal locations, as (x,y) points around a source (circular pattern with 5 km radius). I need to test whether the points are clustered (or repulsed) around the source relative to farther from the source, while accounting for directionality. Things I tried: checked out nearest neighbour and Ripley's K - could not figure out how to incorporate distance from source or directionality (plus, it looks like Ripley's is expecting a rectangular window, whereas mine is

directional testing of spatial clustering by distance from source

由 别来无恙 提交于 2019-12-05 21:57:33
I have a spatial dataset of animal locations, as (x,y) points around a source (circular pattern with 5 km radius). I need to test whether the points are clustered (or repulsed) around the source relative to farther from the source, while accounting for directionality. Things I tried: checked out nearest neighbour and Ripley's K - could not figure out how to incorporate distance from source or directionality (plus, it looks like Ripley's is expecting a rectangular window, whereas mine is circular) divided data into cardinal directions (N, E, S, W) and distance bins and calculated density of

Unit length in spatstat

由 六月ゝ 毕业季﹏ 提交于 2019-12-04 06:28:02
问题 I have what may be a very simplistic question on the KEST function in Spatstat.KEST graph output I'm using the KEST function in Spatstat to assess spatial randomness in a dataset. I have uploaded lat and long values spread over London and converted them to a PPP object, using the ripras function to specify the spatial domain. When I run my KEST analysis on my ppp, and plot the graph, I end up with an r value on the x, but although I know this is a distance measurement, I don't know what units

Unit length in spatstat

由 只愿长相守 提交于 2019-12-02 09:56:24
I have what may be a very simplistic question on the KEST function in Spatstat. KEST graph output I'm using the KEST function in Spatstat to assess spatial randomness in a dataset. I have uploaded lat and long values spread over London and converted them to a PPP object, using the ripras function to specify the spatial domain. When I run my KEST analysis on my ppp, and plot the graph, I end up with an r value on the x, but although I know this is a distance measurement, I don't know what units it's using. I get this summary output: Planar point pattern: 113 points Average intensity 407.9378

plotting and coloring data on irregular grid

由 帅比萌擦擦* 提交于 2019-11-30 02:23:14
I have data in the form (x, y, z) where x and y are not on a regular grid. I wish to display a 2D colormap of these data, with intensity (say, grey scale) mapped to the z variable. An obvious solution is to interpolate (see below) on a regular grid, d <- data.frame(x=runif(1e3, 0, 30), y=runif(1e3, 0, 30)) d$z = (d$x - 15)^2 + (d$y - 15)^2 library(akima) d2 <- with(d, interp(x, y, z, xo=seq(0, 30, length = 30), yo=seq(0, 30, length = 50), duplicate="mean")) pal1 <- grey(seq(0,1,leng=500)) with(d2, image(sort(x), sort(y), z, useRaster=TRUE, col = pal1)) points(d$x, d$y, col="white", bg=grey(d$z

plotting and coloring data on irregular grid

由 痴心易碎 提交于 2019-11-28 23:14:22
问题 I have data in the form (x, y, z) where x and y are not on a regular grid. I wish to display a 2D colormap of these data, with intensity (say, grey scale) mapped to the z variable. An obvious solution is to interpolate (see below) on a regular grid, d <- data.frame(x=runif(1e3, 0, 30), y=runif(1e3, 0, 30)) d$z = (d$x - 15)^2 + (d$y - 15)^2 library(akima) d2 <- with(d, interp(x, y, z, xo=seq(0, 30, length = 30), yo=seq(0, 30, length = 50), duplicate="mean")) pal1 <- grey(seq(0,1,leng=500))

Voronoi diagram polygons enclosed in geographic borders

由 人走茶凉 提交于 2019-11-28 20:46:38
I am trying to create Voronoi polygons (aka Dirichlet tessellations or Thiessen polygons) within a fixed geographic region for a set of points. However, I am having trouble finding a method in R that will bound the polygons within the map borders. My main goal is to get accurate area calculations (not simply to produce a visual plot). For example, the following visually communicates what I'm trying to achieve: library(maps) library(deldir) data(countyMapEnv) counties <- map('county', c('maryland,carroll','maryland,frederick', 'maryland,montgomery', 'maryland,howard'), interior=FALSE) x <- c(
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