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# Tag Info

37

The idea with hexagons is to reduce sampling bias from edge effects of the grid shape, which is related to high perimeter:area ratios. A circle is the lowest ratio, but cannot form a continuous grid, and hexagons are the closest shape to a circle that can still form a grid. Also, if you are working over a larger area, a square grid will suffer more from ...

30

When you use "default values" you aren't really kriging, you're just applying the kriging algorithm--which as you have found, is poor when used with these data. (I will step up on a soapbox for a brief rant: in my opinion, the fastest way to get bad results with a computer program is to accept its default parameters. ArcGIS is one of the richest, most ...

25

One of the benefits, that I've seen when doing wildlife or habitat modelling especially, is that hexagons allow patterns in the data (ex, edge of a field or any other patch) to be seen more easily than what squares would of offered. Think of a soccer ball too, though not always hexagons, those geometric shapes fit to a curved surface quite nicely. In your ...

17

The hexagon is the most complex regular polygon that can fill a plane (without gaps or overlap). I can see two advantages: It is closer to a circle than the square in terms of shape, so you suffer less from orientation bias (lower anisotropy with hexagons) and it is more compact (lower shape index: perimeter²/area). It therefore provides more accurate ...

12

Averaging only makes sense if you assume that the "noise" in your location measurements is roughly symmetrical - evenly distributed in every direction. That is, for any one measurement, it's equally likely to be wrong in any particular direction. It is probably possible that you could get a noise distribution that isn't symmetrical. For example, if your GPS ...

8

fTools is still there under the Vector menu. The sampling tool you need is Vector|Research Tools|Random Points. You need to create a new attribute (like 'points') for your polygon layer; for each stratum, you enter the number of points you need to be generated for that stratum. In the dialog you select that new attribute in the 'value from input field' ...

6

You can use Random points inside polygons (fixed) from Processing Toolbox. If you don't see Processing Toolbox panel, activate it from menu Processing --> Toolbox. For distances in meters you have to use projected CRS (e.g some UTM) for more information about CRS see QGIS Doucumentation Point sampling tool that you mentioned, is good for retrieving ...

6

"No issue?" Hardly. Any time you generate random data you should do a chi-squared test to verify the randomness of your generator. Your distribution will be skewed toward the north or south poles, because the values were uniformly generated in Mercator (which infinitely exaggerates areas at the poles). If you use an equal area projection with a uniform ...

5

A key disadvantage of grid squares is that the sample rate is substantially lower along the diagonal vectors to those of the four sides (Jasons point above). If you have some regular linear pattern to your data the orientation of the grid affects the effective sample rate of each context. For example if you have a series of ridges and valleys, orienting ...

4

Just pad your desired number of random samples and then sample back down to the correct n. This should account for the occasional NA that are produced and subsequently removed with the na.rm=TRUE argument. require(raster) # Create example data r1 <- raster(ncols=500, nrows=500, xmn=0) r1[] <- runif(ncell(r1)) r2 <- raster(...

4

It looks like this is an artifact of the sampleRandom package you are using. If you check the documentation, it states that: With argument na.rm=TRUE, the returned sample may be smaller than requested Random sampling of raster using R? might provide you with an alternative way to perform this analysis.

4

If you have ArcInfo, 3D or Spatial analyst, you can use the create random point tool to generate your points. First I would start with the intersection between your fishnet and a dissolve of your other feature class. This will give you one multipart polygon per grid cell including some of the yellow polygons. Then you can place one point for each of the ...

4

If you can't connect to the online repository, download the plugin manually: Access the Point sampling tool plugin from the repository. Click the version required: Click the Download button: Extract the plugin folder from the zip file and copy/move it to your .qgis2 directory (e.g. C:\Users\You\.qgis2\python\plugins Restart/load QGIS and it should appear ...

4

How much do leaves on/off affect the number of LiDAR ground returns? It does affect significantly. Take a look at Wasser et al. (2013)'s figure 4 (adapted) and see how the frequency of returns are much more concentrated in the upper strata of canopy in leaf-on than leaf-off: During leaf-off season there will be more LiDAR returns hitting the ground due to ...

3

The "extract" function in the raster package will extract raster values to points for a stack or single raster.

3

With ArcGIS, you can 1) convert your land cover image to polygons 2) dissolve all polygons based on the land cover field 3) generate 50 random point for each land cover polygon (select your land cover feature class as "constraining feature class") 4) Extract multiple values to points (this will give you the 70 band values as fields in the point feature ...

3

Using the raster package in R you could apply a pixel-wise regression estimate of NDVI ~ time. Here is an example for a linear model, locally-weighted polynomial regression and regression coefficients. # Create some example data r <- raster(nrow=100, ncol=100) r[] <- runif(ncell(r),-1,1) rt <- stack(r) for(i in 2:26) { r &...

3

I'd suggest the spatstat package. Perhaps check out the quadratresample function. They also have several others to simulate random patterns that may fit your need (e.g. rstrat and rsyst). Random sampling should be pretty trivial to accomplish on your own (see the sample function in base R)

3

And another way: Do a spatial join of the polygon data to the points and set the field mapping merge rules to 'Max'.

3

I am assuming you are calculating yield from one time period. In this case, you can essentially "clean" your polygons so that they do not overlap and increase the error rate of your calculations. There are three ways to go about this type of analysis. The first is to use a tool in ArcGIS called Eliminate. A second option similar to Eliminate is to use ...

3

Problem is in the CRS. Layers geometry must be in same CRS (like in other overlay analysis). OTF (on the fly) transformation doesn't matter since it not change the gometry in the layer file. You have to reproject your point layer (Layer --> Save as..) or raster image (Raster --> Projections --> Warp (Reproject)... to same CRS. In other words you are now ...

3

Use the Random selection within subsets tool which allows you to select the ID field (containing the unique values for your electoral area) and the number of points:

3

In QGIS: Use the Raster Calculator to create a raster that is equal to 1 anywhere the population density > 1000 condition is satisfied and where the altitude raster's condition (< 1000) is also satisfied. Polygonize the output raster. Use a definition query on the result to only show the areas with a value of 1. This area is now a polygon ...

3

The tool will always return the same results unless you set the Random Generator environment value. Click the Environments... button at the bottom of the tool and select the Random Numbers section. See the help page for the Random Number Generator for details.

3

sample() uses 1 region (either points or polygons) and does exhaustive sampling in that region (all pixels) unless you specify a smaller number of points. But the random sampling it does isn't optimal. sampleRegions uses multiple regions (either points or polygons) and does exhaustive sampling in each region (all pixels). There are no options for ...

3

Yes, gstat::idw can predict at any x,y location, but if you want to show that as a continuous map then you need a raster. The example does: # Interpolate the grid cells using a power value of 2 (idp=2.0) P.idw <- gstat::idw(Precip_in ~ 1, P, newdata=grd, idp=2.0) to predict at a regular grid of x,y coordinates generated by spsample It then converts the ...

3

Given you already have your RED 6km span points which I am referring to red_points, you can do something like this to obtain your 3X3 200m grey_points. (please, adjust the sign (+/-) as you wish to achieve your result. Also, here 200 is in meters as long as the units of your reference system is meter.) import arcpy red_points = "your_red_points_fc_here" ...

2

Here is a method you can use: Make sure point layer has a unique ID field Run Intersect tool on both point and polygon layer (point layer as first input) Open attribute table of intersect result layer and right click on the ID field and select Summarize Within Summarize dialog expand yield field and check maximum Join summary result table back to original ...

2

The tool you are looking for is the "Sample"(Spatial analyst>Extraction>Sample). Add all your rasters, then the shape file with the locations.

2

I was able to reproduce the problem in the third example. A workaround uses built-in procedures. There are several options, but one convenient method is just to select each cell in the grid uniformly at random and independently with a probability large enough to assure at least n=2000 (or whatever) non-null cells will be selected, but not much more than ...

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