Spatial

Using R, I am trying to extract data from a raster layer using a polygon layer. The polygons are much smaller than the raster cells: Now I call extract() from raster library: a <- extract(raster, polygons, weights = TRUE, small = TRUE) a # ... # [[1551]] # value weight # 209 0.03 # top left cell - more than 50% of the polygon area There are two problems - the weight is the proportion of the cell area covered by the polygon, and the weights are rounded to 1/100. In my case, only the top left cell is present in the output (value 209) - the weight of 3 other cells was rounded to zero and they

Package sp provides a number of classes for different spatial concepts (points, lines, polygons). For some classes, accessing feature coordinates is straightforward, e.g. SpatialLines . All examples were taken from respective class help pages. l1 = cbind(c(1,2,3),c(3,2,2)) l1a = cbind(l1[,1]+.05,l1[,2]+.05) l2 = cbind(c(1,2,3),c(1,1.5,1)) Sl1 = Line(l1) Sl1a = Line(l1a) Sl2 = Line(l2) S1 = Lines(list(Sl1, Sl1a), ID="a") S2 = Lines(list(Sl2), ID="b") Sl = SpatialLines(list(S1,S2)) coordinates(Sl) # [prints a list of two with corresponding segments] For SpatialPolygons , coordinates() returns

I'm using Entity Framework 6 with .Net 4.5 . I have a stored procedure that selects and returns data. One of the return columns is a geography type . In Visual Studio 2015, I right click the .edmx file, click "Update Model From Database...". This action gets my stored procedure and creates a complex type of storeprocedurename_Result. All the columns are represented in the complex type objects except the geography type . At the same time one of my table also have Geography type but that table is retrieved successfuly with the geography column and it is functioning properly. So, i can guess

Correct me if I'm wrong. There are three approaches to get the nearest homes, users have created in my website: To create a table with two columns(latitude, longitude) that both of them are float and say: Here it is: $latitude = 50; $longitude = 60; SELECT * FROM my_table WHERE (latitude <= $latitude+10 AND latitude >= $latitude-10) AND (longitude <= $longitude+10 AND longitude >= $longitude-10) that 10 here means 1km for example. In this approach we can also use harvesine formula. To merge those columns(latitude, longitude) to one column named point as POINT type and again search each row one

I have occurrence points for a species, and I'd like to remove potential sampling bias (where some regions might have much greater density of points than others). One way to do this would be to maximize a subset of points that are no less than a certain distance X of each other. Essentially, I would prevent points from being too close to each other. Are there any existing R functions to do this? I've searched through various spatial packages, but haven't found anything, and can't figure out exactly how to implement this myself. An example occurrence point dataset can be downloaded here .

I am trying to write this algorithm in R. Does it exist in any package already?!? This is what I did (with help from SO and various blog posts): library(rgdal) library(ggmap) require("maptools") require("plyr") locations<- unique(cbind(data22[,1], data22[,2])) [,1] [,2] [1,] 24.9317 60.1657 [2,] 24.9415 60.1608 [3,] 24.9331 60.1577 [4,] 24.9228 60.1477 [5,] 24.9370 60.1545 [6,] 24.9491 60.1559 [7,] 24.9468 60.1591 [8,] 24.9494 60.1675 [9,] 24.9561 60.1609 [10,] 24.9218 60.1632 [11,] 24.9213 60.1605 [12,] 24.9219 60.1557 [13,] 24.9208 60.1704 [14,] 24.9233 60.1714 [15,] 24.9469 60.1737 [16,] 24