37

Both forms rely on Toblers first law of geography: things that are close are more related than things that are further apart. IDW is the simpler of the two techniques. It involves using known z values and weights determined as a function of distances between the unknown and known points. As such in IDW points that are far away have far less influence than ...


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 ...


16

This error is commonly returned because you have duplicate locations. You can check this using the sp::zerodist function. To remove duplicate locations you call sp::zerodist within a bracket index. WeatherData <- WeatherData[-zerodist(WeatherData)[,1],]


13

It is partially explained here http://www.gistutor.com/quantum-gis/20-intermediate-quantum-gis-tutorials/51-inverse-distance-weighting-idw-interpolation-using-qgis.html by first showing examples of using coefficient values of 1 and 3, and then As you can see, a larger coefficient means it takes a larger distance for the values of the surface to become ...


13

In order to interpolate prices with kriging you first need to convert your geographic coordinates to projected coordinates. Assuming you have them, below there is a reproducible example, showing a way to accomplish such task. library(sp) #Spatial data containing variables which can be interpolated. #We will use the zinc column; as an equivalent for 'price'....


12

IDW works by finding the data points located nearest each point of interpolation, weighting the data values according to a given power p of the distances to those points, and forming the weighted average. (Often p = -2.) Suppose there is some amount of distance distortion around an interpolation point that is the same in all directions. This will multiply ...


11

You may take the elevation-temperature relationship into account, especially in mountainous areas. Co-kriging or splines interpolation (e.g., 3D splines as supported by GRASS GIS) can be used for this. For larger areas further variables may play a role: distance from the sea, latitude, etc. Update: a reasonable method may also be multiple regression, for ...


10

When you run geoprocessing operations in ArcMap (e.g. tools from the ArcToolbox pane) they conform to two sets of parameters. First are the parameters in the window itself, e.g. input file, output file path, etc. Second are the parameters in the Environment Settings window (see below). These Environment Settings let you fine-tune your geoprocessing ...


10

This is not a pure GIS problem. When interpolating soil depths, you need to apply the GIS techniques on the basis of a geomorphological hypothesis. In other words, what is the geological history of the area you are mapping? What are the soil formation process in operation? Are there landscape features that limit the processes (eg. barriers such as rivers, ...


10

It appears this question is related to an earlier one that asks about disguising such data using an irregular grid. If we accept that a regular grid will be used, then it seems that Most cells should be large enough to cover five or more buildings and When cells do not cover five buildings, their values should be changed in unpredictable (but controlled) ...


10

One alternative is spline interpolation as suggested in the related post: Interpolation of multibeam bathymetry. From QGIS, use the GRASS tool v.surf.rst: Performs surface interpolation from vector points map by splines. Then, you can test different types of parameterization available within the tool. There is an option to apply a leave-one-out cross ...


9

I want to give you some hints about the differences in the methods. More information can also be found on the esri help pages An overview of the Interpolation toolset Because your variable (sunshine) depends on a second variable (level of pollution) Kriging may be a good method. You can use your second variable as an “external drift”. Kriging requires very ...


9

There are many algorithms of interpolation, each one with its characteristics. If you want to limit the results to the original values, you need to choose: the TIN algorithm (with GRASS GIS: v.delaunay + tin.to.raster.py of Antonio Alliegro) In other cases you need to use a raster MASK (see Restricting Tin interpolation in QGIS or Raster Masks in QGIS/...


9

In a nutshell, the problem lies in a mismatch between data behavior and some (strong) assumptions you are implicitly making. Diagnosis The strongest of these is that the data are one realization of a second-order stationary process. They clearly are not, as you can tell by comparing the region near (450000, 5075000) in the upper "neck" (which I will call "...


9

Heatmaps and interpolations are completely different things though they might look similar. A heatmap visualizes "hotspots" in the distribution of features on the map i.e. dense areas will be highlighted in a heatmap, based on the parameters you use for processing it. However the purpose of an interpolation is to estimate feature values at locations where ...


8

I know you mention this in your question, but I'd be very wary of down-scaling 5km precipitation grids to 100m. Precipitation is notoriously difficult to interpolate, as so many factors (local topography, temperature etc.) affect its spatial distribution. The two methods described by Aaron and Martin will work, but I wouldn't have much faith in the output if ...


8

From the website of ESRI: ArcGIS Geostatistical Analyst complements Spatial Analyst. Most of the interpolation methods available in Spatial Analyst are represented in ArcGIS Geostatistical Analyst as well, but in Geostatistical Analyst, there are many more statistical models and tools, and all their parameters can be manipulated to derive optimum ...


7

I would like to add Block Statistics as another method to alter the resolution of a raster. Depending upon your specific goals, Block Statistics allows fine control of how pixels are assigned based on: A user defined neighborhood (e.g. rectangle, circle, wedge etc) The type of statistics calculated within each block (e.g. mean, majority, variety etc). In ...


7

This is likely not entirely an issue with the interpolation model. Bathymetric data can exhibit considerable noise. Because of an equal weight associated with each TIN facet and outlier effect, A TIN base interpolation can extenuate this noise and is not recommended. I would apply a Topogrid (Topo to raster tool) Spline interpolation and then apply a ...


7

There exists an extremely efficient solution to this problem that avoids computing ten thousand grids of interpolated data. Using IDW to estimate precipitation tends to give poor results, so I will provide a more general answer that applies to better procedures such as Kriging as well as IDW. What is common to these interpolation procedures is that they ...


7

Yes, there are such algorithms (see for example here, there and here) but since you seem to have only one single road, would it not be easier to do it by hand? (!). Using for example QGIS, you could import your GPS traces, create a new layer, digitalise the centerline of the bundle, and then export it in whatever format.


7

There is not an Iterate Fields tool in ModelBuilder. I can think of two possible workarounds: Modify the model to run as a Python script. Define a list of the fields you want to use, and define a loop to go through each one and execute the IDW/export functions. I would go with this one personally, but it would be (much) easier with some Python knowledge. ...


7

Interpolate the sine and cosine of the angle, and then convert back to an angle with the atan function. These functions are available in QGIS' expression engine. There is an atan2(dy,dx) function like the one in R I use below... Here's an R function to illustrate. I've used mean here to give the interpolation: dinterp = function(d){ r=d*pi/...


6

Because these are regular points, you effectively already have a DEM; it's just in a different format than ArcGIS likes. This makes two different strategies available to you: Convert the data into a format ArcGIS can handle. One way is to set up a raster extent and cellsize that (i) cover your DEM and (ii) situate each point near the middle of its cell. ...


6

A good general-purpose solution could begin with a Euclidean minimum spanning tree. This is fast and easy to compute, using a greedy algorithm to link closest vertices together. There should be no problem processing many millions of vertices all at once. You can then isolate specific routes by eliminating the longest edges in the tree or by "exploding" ...


6

In your case, where you have a multivariate problem, ordinary Kriging is quite inappropriate. I find your interpretation of this as an "interpolation" problem is a bit off base as well. This is an estimation problem and more suited for Machine Learning or spatial regression, not geostatistics. The grey area are Splines. This can be a univariate interpolation ...


6

Yes. The standard methods used to cross-validate and assess Kriging also apply, practically with no change, to almost any other method of interpolation. These include jackknifing, a form of leave-out-one cross-validation in which each data point is systematically removed from the dataset and predicted using the chosen variogram model. The discrepancy ...


6

Noise is much more complex than a simple IDW interpolation. Sound propagation depends on many factors and distance is just one of them. Air density, temperature, humidity, terrain, wind direction and ground attenuation should all play their part in even the simplest of models. In addition to these simple factors there are issues relating to tonality of ...


6

What you need to do to create a continuous surface representing precipitation is a process called interpolation. ArcMap has a number of tools to do this, based on a variety of statistical and sampling approaches. I'd recommend inverse distance weighting (IDW) as a starting point, because it's one of the simplest to use. The input for IDW is a single feature ...


6

This is a bit of a late answer, but I thought it was worth contributing. As a polyline is just a series of points you should be able to obtain the Mean value you want by converting the Polyline nodes into points by going Vector > Geometry Tools > Extract Nodes... You can then extract the underlying Raster values for each of these points by using the Point ...


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