Spatial

lat long 7.16 124.21 8.6 123.35 8.43 124.28 8.15 125.08 Consider these coordinates, these coordinates correspond to weather stations that measure rainfall data. The intro to the gstat package in R uses the meuse dataset. At some point in this tutorial: https://rpubs.com/nabilabd/118172 , the guys makes use of a "meuse.grid" in this line of code: data("meuse.grid") I do not have such a file and I do not know how to create it, can I create one using these coordinates? Or at least point me to material that discusses how to create a custom grid for a custom area (i.e not using administrative

I have multiple sets of points (for different years ~20) I want to generate thiessen polygons for each set of points using r spatial packages. I know this can be done using GIS but as i want a batch process something in R would be helpful. You haven't given us access to your data, but here's an example for points representing cities of the world, using an approach described by Carson Farmer on his blog . Hopefully it'll get you started... # Carson's Voronoi polygons function voronoipolygons <- function(x) { require(deldir) require(sp) if (.hasSlot(x, 'coords')) { crds <- x@coords } else crds <

I would like simply delete some polygons from a SpatialPolygonsDataFrame object based on corresponding attribute values in the @data data frame so that I can plot a simplified/subsetted shapefile. So far I haven't found a way to do this. For example, let's say I want to delete all polygons from this world shapefile that have an area of less than 30000. How would I go about doing this? Or, similarly, how can I delete Antartica? require(maptools) getinfo.shape("TM_WORLD_BORDERS_SIMPL-0.3.shp") # Shapefile type: Polygon, (5), # of Shapes: 246 world.map <- readShapeSpatial("TM_WORLD_BORDERS_SIMPL

Given n points in d-dimensional space, there are several data structures, such as Kd-Trees, Quadtrees, etc. to index the points. On these data structures it is possible to implement straight-forward algorithm for nearest neighbor queries around a given input point. Is there a book, paper, survey, ... that compares the theoretical (mostly expected) runtime of the nearest neighbor query on different data structures? The data I am looking at is composed of fairly small point clouds, so it can all be processed in main memory. For the sake of simplicity, I assume the data to be uniformly

I have a brand new server which I installed the .NET 4.5 Beta redistributable on. I am getting a NotImplemented exception when trying to use the spatial features. This code... var x = DbGeography.PointFromText(string.Format("POINT({0} {1})", -45, 45), 4326); Throws this exception... System.NotImplementedException: The method or operation is not implemented. at System.Data.Spatial.DefaultSpatialServices.GeographyPointFromText(String geographyText, Int32 spatialReferenceSystemId) If I install the full VS.NET 11 Beta then the code works fine. Any ideas why? What is missing? UPDATE FOR ANSWER

I want to store latitude and longitude values of places in a mysql database table. With the future in mind I will want to be able to find these places within a certain radius of a specific location. Having said that, what datatypes should I store the latitude and longitude values in? Please could you provide me with a create table script for columns like so: place_id | lat | long Is there perhaps a column I am missing in the above table that will provide me with additional information that I may not see I need at the current time? Thanks for any help. You should store the points in a singe