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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 ...
17
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],]
16
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'....
14
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 ...
10
The great thing about QGIS is its modular design, based on which you can use the geoprocessing engines of various other systems directly as tools in QGIS (GRASS, SAGA, GDAL, OGR, ...). In order to do so, you need to activate the 'processing' extension. Then you can switch on the 'Geoprocessing Toolbox' via menu 'Processing' > 'Toolbox'. Searching for '...
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
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 ...
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
The explain doesn't show an index coming into play, which could be for two reasons:
You don't have one. So make one with CREATE INDEX tree_gix ON trees USING GIST (geom)
Your data is in geographic coordinates, so your spatial join isn't really doing anything selective (it's joining every tree to all other trees, every time). In that case, either (a) change ...
8
Try using v.generalize tool from the Processing Toolbox. There are a number of algorithms in there that can generalize a line nicely.
Another possible solution could be the "Generalizer" plugin which was mentioned in this post, the plugin info in QGIS suggests that the tool is based on the v.generalizer Grass module anyway.
Just for reference below ...
7
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 ...
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
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 ...
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/...
7
You can control the pixel size of an image by setting the scale parameter of ee.Image.reproject():
// Reference a Landsat scene.
var image_30m = ee.Image('LANDSAT/LC08/C01/T1/LC08_044034_20170614');
// Define visualization parameters for a true color image.
var vizParams = {'bands': 'B4,B3,B2',
'min': 5000,
'max': 30000,
...
7
Is it true that we're just using a different set of weight values than the 1/distance in IDW?
Yes, both IDW interpolation and Ordinary Kriging (OK) will calculate weights based on distance, but with different criteria. In both methods, weights do not depend on sample values. The answer from Dahn Jahn in Ordinary kriging example step by step? is very ...
6
I lack the "reputation" to Comment so...
If radiometric analysis is going to be performed on the aerial photos then it should be done prior to resampling/projecting. Otherwise you will almost certainly introduce unintended bias into the final product. As per blord-castillo's helpful comment above.
If the proximate and final uses of the aerials are for ...
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
You can likely get a reasonable interpolation using a linear regression (assuming your 30 weather stations are a representative sample) using elevation, latitude and distance from the coast as independent variables with the day as a factor. I've done this using ArcGIS and R previously.
Daily 9am and 3pm temperatures over 10 days in 2003 from weather ...
6
You have now a better way to do.
Since RFC 59.1 : GDAL/OGR utilities as a library, you can use gdalwarp from Python directly without using any call to the command line utility but using really the function from Python.
This solution is a bit "on the edge" as you need at the moment to use the latest GDAL version (version 2.1, in fact the master/trunk ...
6
I think you have a problem with your points shapefile and/or the projections (lines and points).
These commands are those of Shapely used by GeoPandas
From Coordinate of the closest point on a line
# Length along line that is closest to the point
print(line.project(point))
# Now combine with interpolated point on line
np = line.interpolate(line.project(...
6
According to the changelog, the TIN interpolation plugin has been removed, as it is now part or QGIS core.
To find it in Qgis 3.0, open the processing toolbox and search for Interpolation -> TIN interpolation.
6
Yes, you can call this from a Python script. But you don't directly call the low-level C API.
First, take a look at the GDAL Grid Tutorial for background info. From the Python library, the relevant function is gdal.Grid(destName, srcDS, **kwargs). You can see some examples of how it's used in test_gdal_grid_lib.py (from the test suite). Or a made-up example:
...
5
I know that this question is rather old, but I wanted to add my 2 cents, in case others come across this thread trying to answer the same question...
The previous answers are correct when you truly wish to RESAMPLE your data, such as if you are aggregating your data from a 30 m pixel size to a 90m pixel size. In this case you are attempting to create a new ...
5
No there isn't a QGIS analogue to TopoToRaster (actually ANUDEM)*. There isn't an analogue anywhere else that I'm aware of either for that matter.** (If you know of others please chime in.)
In brief ANUDEM "Interpolates a hydrologically correct raster surface from point, line, and polygon data."
The key point that differentiates ANUDEM from other ...
5
In sp, SpatialPoints*, SpatialPixels* and SpatialGrid* (with * omitted or replaced by DataFrame) do support more than 2 spatial dimensions, as OP has done, but SpatialPolygons* and SpatialLines* do not. With gstat you can do 3-D block kriging with 3-D blocks (using block = c(10,10,10)), but you cannot do this for non-rectangular blocks, as OP wants. It is ...
5
If you are just after a metric of performance, this is a fairly straight forward type of analysis to specify. 1) specify a model, 2) predict the model(s) at the data, 3) apply an accuracy/performance metric based on observed vs. predicted. We can step through the process thus (note; this is a dummy example so, ignore the REML errors):
First lets specify ...
5
If you follow this link you will see some duck eggs. Even if in this case it comes from inverse distance weight and not from weighted moving average. It is quite common with those two interpolation methods.
You have ellipse or circles around your isolated point, which are different from the background and therefore very visible. This is necessary the case ...
5
The short answer is: no, but it might be the best you can do without additional layers. The problem is that humans do not settle evenly. If all you have is populations of cities then IDW might be your best approximation, because population density does decay the further you go from the center of a city (as IDW models). However, population does not decay ...
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