Spatial

I have a problem saving spatial data in CakePHP with saveAll(). I really dont want to write the query manually ( Handling spatial data in CakePHP ) because there are number of models being saved. Also I read this CakePHP and MySQL Spatial Extension but when I try to do the same, $db->expression() returns an stdClass. This is the returned object printed out: stdClass Object ( [type] => expression [value] => GeomFromText('POINT(48.18879 18.527579999999944)') ) If I use this object the way it is used in CakePHP and MySQL Spatial Extension and try to save it with saveAll() I get this error: Error

I have spent a great deal of time now trying to obtain edges from the scipy.spatial.Voronoi diagram to no avail. Here is the main documentation: http://docs.scipy.org/doc/scipy-dev/reference/generated/scipy.spatial.Voronoi.html If you create a Voronoi Diagram like so: points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]]) //Or feel free to use any set of points then you have access to the following object properties: vor.regions vor.max_bound vor.ndim vor.ridge_dict vor.ridge_points vor.ridge_vertices vor.npoints vor.point_region vor.points But is unclear

I would like to extract spatial data in a buffer of 10 km around 30 000 objects of class SpatialLines and calculate proportion of each land cover type around buffered lines. In a first time, I used the function crop to crop my raster. Then, I used the function extract (package raster) to calculate proportion of 10 land cover types. Here is my code: lapply(1:nrow(tab_lines), FUN=function(k){ First step: to build a buffer of 10 km around the line buf_line <- gBuffer(seg_line[k], width=10000) ## seg_line = Lines objects Second step: to extract land cover types in the buffer from the raster ha <

I have database with spatial data types. I use database first model and entity framework 6.0.2 and .NET 4.5. When I am trying to use the generated classes I get following error: Schema specified is not valid. Errors: The relationship 'Name_FK1' was not loaded because the type 'Model.TypeB' is not available. The following information may be useful in resolving the previous error: The property 'Position' on the type 'Data.TypeB' has a property type of 'System.Data.Spatial.DbGeography' which cannot be mapped to a primitive type. The same error is listed for all tables where I use spatial type.

I have a polygon shapefile (downloadable here ) from which I want to create a data.frame with 3 columns containing: Polygon id Centroid latitude Centroid Longitude From this answer here , I know it its quite easy to get this information as a Formal Class SpatialPoints object. And when I convert this object to a data.frame I loose the id information. # Load Shapefile Legislative_areas <- readOGR(dsn = 'C:/Users/.../Downloads/Legislative2010UTM', layer ='Legislative2010UTM') # Get centroids cent <- gCentroid(Legislative_areas, byid=TRUE) # Convert to data.frame, but loose id info cent <- as.data

Is it possible to place the spplot (spplot polygons) legend within the map, in lower left corner, like this? The closest I've been able to get is this (I am not posting my data, I just use the example data instead, so in this case, try to place the legend in top left part of the map): data(meuse.grid) gridded(meuse.grid)=~x+y spplot(meuse.grid[,'dist'], colorkey = list(space = "left", height = 0.5) ) But the legend is in the middle of the page and is outside of the map. Unfortunatelly, colorkey argument doesn't support "bottomleft", or x, y, or corner arguments (see ?levelplot ). I also tried

I am a beginner in learning image processing and I am a bit confused with the concept of spatial and temporal characterization. So, for Spatial characterization, is it like a 2D map which contains some statistical information about the map? And in terms of the temporal characterization, is the value with respect to time? What does it mean and why do we care? Thanks! Temporal characterization occurs when you have a series of images taken at different time. Correlations between the images are often used to monitor the dynamic changes of the object. Spatial characterization applies when you are