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5

Step 1 Make bit rasters for each of the unique classes. This can be a 1-band rasters for each class, or a single raster with a band for each class (e.g. GeoTIFF). If using GTiff, you can use the creation option NBITS=1 to conserve space. You may also want to consider twobit rasters to store three-valued logic where the third (e.g. 2) is NODATA, which would ...


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Here you were thoroughly answered on how to add your basemap (first map) in QGIS What you need to do next is called georeferencing. Go to plugins and make sure that Georeferencer GDAL is on menu Raster\Georeferencer add your second map add at least 6 points from your second map getting coordinates for each from your first map (also feel free to add more ...


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Zonal Statistics as Table (Spatial Analyst) should work for you. In your case, the range will be represented by the min/max values within the table.


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I am not aware of any settings that would allow you to select only the top polygons because when you click on the small polygon, you also click on the underlying polygon. Workarounds: Copy top polygons (if there is a way to differentiate them either by attribute or by size) into a new layer and then make your "large polygons" layers non-selectable. Work ...


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I played with assign(), ls(), and mget() to accomplish something that I believe will improve your workflow. First I use ls() to get a list of all environment variables that start with "p": names_poly <- ls(pattern='^p.') I used combn() to find all the unique combinations of polygons combos <- combn(names_poly,2) I looped over combos using get() ...


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I wanted to point out that you can rewrite the mean function, mean, which you can write yourself to do anything you want, including ditch the 0 values and calculate the mean. For example, if you want to ignore 0s: meanIgnoringZeroes <- function(x) { mean(x[x!=0],na.rm=T) } Then you can pass the function, meanIgnoringZeroes to overlay: mean <- ...


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To do this I would use two tools: Intersect (Analysis): Computes a geometric intersection of the input features. Features or portions of features which overlap in all layers and/or feature classes will be written to the output feature class. then Summary Statistics (Analysis) Calculates summary statistics for field(s) in a table.


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Take a look at the raster function in the raster package. It will let you create a raster with a specified extent, number of rows/columns and resolution. Here I will use characteristics of your data summary to create a 100x100 raster within the specified extent. I am passing an extent object to define the x and y limits. You can also use the specific ...


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1) The easiest solution is to use the processing module in the QGIS Python console: import processing processing.runalg("qgis:joinattributesbylocation","BKMapPLUTO.shp","DCP_nyc_freshzoning.shp","['intersects']",0,"sum,mean,min,max,median",0,'result.shp') 2) Without a GIS, you can use Fiona (read and write shapefiles as Python dictionaries) and Shapely ...


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I got the tiles to overlay correctly. The problem was in the re-projection done by both ArcMap and QGIS. When I was checking the reprojected shapefiles in ArcMap and QGIS, they were overlaying correctly and had the correct SRIDs. So I imported the shapefiles in WGS84 in PostgreSQL using the SRID4326 with shp2pgsql then used ST_Transfrom to reproject the ...


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The shapefiles you linked to should be resaved using the Save As... option and with another CRS such as: EPSG: 2157, IRENEET95 / Irish Transverse Mercator I tested this and resaved the shapefiles using "NEW_" as a prefix. I then used the Join attributes by location tool: The output contains the attributes of both shapefiles with no NULL values (red box ...


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You can: Create your own solution with Leaflet http://leafletjs.com/ and one of the plugins from a list below http://leafletjs.com/plugins.html#overlay-data Use http://umap.openstreetmap.fr/ with external data overlay. Check layers. It's not as easy as paste a link into google map search, but much more power-full. Yuo could upload your data or use a link ...


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You cannot have R objects called "2000", so presumably these are fake names? Your example actually should work, so you may want to double check why you think that the results are incorrect. @aaryno's approach should work. I would do this: library(raster) s <- stack(r2000, r2001, r2002, r2003, r2004, r2005) x <- reclassify(s, cbind(0, NA)) r <- ...


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There are two possible approaches to the problem, using Intersect or Union. First it would be helpful to understand what the Overlay options you mention actually do. Intersect only returns areas of overlap, Union returns all areas from both layers. It is further worth noting that in ArcGIS you are limited to two input layers per operation unless you have an ...


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Two ways: use select by location or intersect. Good tutorials on ESRI's site in the hyperlinks, so I won't give instructions here.


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Perhaps you need to first look at how ifelse works. I get the same results when I use it "stand-alone" and within a call to raster::overlay. a <- rep(2, 5) b <- rep(1, 5) d <- c(2, NA, 2, NA, 2) library(raster) r <- raster(nrow=1, ncol=5) A <- setValues(r, a) B <- setValues(r, b) D <- setValues(r, d) s <- stack(A,B,D) ifelse(a==2 ...


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The issue is that the two geometries don't share common nodes. For instance, A has vertices mid-way that don't exist in B. While they visually overlap, there are tiny round-off errors from the interpolation used in the algorithm to determine differences, and you see unexpected results. To avoid the round-off errors with overlay operators available with GEOS ...


2

You should be able to use the "KML to layer" tool in ArcGIS for Desktop. As long as there is meta data associated with your image this tool will convert the images from KML and then they should plot over your basemap.


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There are several ways you can do this, but the most appropriate for you, in my opinion, is to do classification on your data, Image classification techniques group pixels to represent different features based on different DN Values. You should be able to classify your data if the features show a consistent difference. There are three main image ...


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To put things clear, I assume that you want to compute the area which responds to the following conditions: located inside your DEM altitude interval located inside your vector boundaries located where your second raster (let's call it raster2) has values other than "nodata" (this is the unclear part of your post, feel free to correct me if I ...


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Also, my original question had also asked how to do this in QGIS, but somebody came and removed that for some reason ... for people who are interested in doing this using QGIS, I found how to do this from this Stack Overflow post: Selecting features within polygon from another layer using QGIS? You can use the "Vector->Research tools->Select by Location" ...


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I've found my answer: MgSelection mgSelection = new MgSelection(map); mgSelection.Save(resourceService, strMapName);


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Look at this topic: Remove islands and completely surrounded polygons after polygonization with QGIS There is answer how to select surrounded polygons.After that you can make a new layer from selection. (For QGIS)


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It's possible but not trivial. A way to do it is to subclass L.TileLayer in such a way that each tile is wrapped in a <canvas> (like https://github.com/aparshin/leaflet-boundary-canvas/blob/master/src/BoundaryCanvas.js#L244 does), then attaching events to the canvases to fetch the pixel value of a given pixel. You might also run into CORS issues when ...


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If i got you, your question itself is answering the question. You used A∩B i.e. Intersection symbols so use intersection analysis to separate out common areas, for A∩B run intersection between A and B, for A∩B∩C run intersection between A, B and C- mind intersection operation input can be multiple.Documentation is at here.


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Depending on whether you want a mean or a spatially weighted mean there are a couple things you can do using the rgeos and raster packages library(rgeos) proj4string(grid) <- proj4string(nuts) # I assumed these were the same projection? First find out which grid cells intersect your NUTS polygons grid_nuts <- gIntersects(grid,nuts,byid = TRUE) ...


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If I understand your desired outcome correctly, you would like a count (richness) of species for each grid cell in the defined raster. I cannot speak to the differences between R and QGIS but I came up with a much more optimized and faster way to conduct your analysis. I leverage the raster package and use a raster stack to accumulate species. The workflow ...


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Generally with Leaflet, if you want to show something UNchecked in the overlay by default, you simply add the layer to the layers control, but do not add it to your map. So don't worry about "without removing them from the map" because you will not add them in the first place (until they are going to be displayed, via the layers control). To wit: var ...


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Perhaps a bit late to the party, but for future reference: a simple Spatial Join will do the trick. Define a Merge rule to the concatenate the IDs of multiple features into a single field. Target features: Polyline layer Join features: Polygon layer Join type: One-to-One Field map: right-click a textual ID field of your Polygon layer and set Merge rule ...


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There is an extremely simple solution using the spatialEco library. library(spatialEco) # intersect points in polygon pts <- point.in.poly(pts, ply) # check plot plot(ply) plot(a, add=T) # convert to data frame, keeping your data pts<- as.data.frame(pts) Check the result: pts > x y var1 var2 polyvar > 2 ...



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