Spatial

I need to save 100 SpatialLines objects in the list xySpatialLines . The below given code provides an error: Error in xySpatialLines[i] = spl : invalid type/length (S4/0) in vector allocation library(sp) xySpatialLines <- NULL for(i in 1:100) { x <- c(i,5,4,8) y <- c(1,3,4,i) xy <- cbind(x,y) xy.sp = sp::SpatialPoints(xy) spl <- SpatialLines(list(Lines(Line(xy.sp), ID=i))) xySpatialLines[i] = spl } Initialize xySpatialLines not as NULL but as a list, e.g. by xySpatialLines <- list() or better, pre-allocate the space you need to avoid incremental growth: xySpatialLines <- vector(mode = "list",

I have a dataset of species and their rough locations in a 100 x 200 meter area. The location part of the data frame is not in a format that I find to be usable. In this 100 x 200 meter rectangle, there are two hundred 10 x 10 meter squares named A through CV. Within each 10 x 10 square there are four 5 x 5 meter squares named 1, 2, 3, and 4, respectively (1 is south of 2 and west of 3. 4 is east of 2 and north of 3). I want to let R know that A is the square with corners at (0 ,0), (10,0), (0,0), and (0,10), that B is just north of A and has corners (0,10), (0,20), (10,10), and (10,20), and K

I'm cooking up some spatial examples and have decided to give Dapper a go, although EF has spatial support, and I'm loving having control over my SQL again (Thanks Sam & Marc). However, I need to be able to have POCO's that support the DbGeography class. For example : public class BuriedTreasure { public int PirateId { get; set; } public DbGeography MarksTheSpot { get; set; } } My Google foo has been letting me down and the closest match I can find is this question, though it only caters for adding spatial support as a parameter (so it's 50% there). Now as far as I can see I'm limited to the

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

I am drawing a polygon with the following vertices x y -0.02208709 -0.039161304 0.01184081 -0.020268029 0.04578401 -0.001351904 0.02210236 0.039176396 -0.01185226 0.020252146 -0.04578784 0.001352696 using the following code plot(x,y) polygon(x,y) points(mean(x),mean(y),col="red") Now I want to plot 50 equally-spaced points along the sides of polygon. Any suggestion how to do it? You can do this with spsample from the sp package. First we'll load the library and read in your vertices. library(sp) xy <- read.table(text='x y -0.02208709 -0.039161304 0.01184081 -0.020268029 0.04578401 -0.001351904

I have several cropped rasters with different geometry/outlines. Specifically spatial yield maps from several years of the same field, but the extent varies - the measurements were not always overall the whole field, but in some years only part of it. I want to calculate a mean value of those maps and combine them into one mean-value-raster. That does mean however, that not for every pixel in let's say 5 layers/rasters there is a value. I could accept these missing values to be NA, so the final mean value would only be calculated by let's say 3 rasters for parts of the field, where the maps