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Questions tagged [spatial-interpolation]

Estimating new data points based on existing spatial observations

Spatial interpolation is a method of estimating new data points based on existing spatial interpolation. This tag focusses on spatial interpolation (data with a geographic component), although interpolation in a general also applies to e.g. timeseries.

Commonly, spatial interpolation involves using of the surrounding observations to estimate a new data point. This can be done using some kind of weighted mean (inverse distance weighted interpolation), by fitting a mathematical function through the existing points (splines), or a combination of both (kriging with external drift).

More information regarding which packages can be used to perform interpolation can be found on the Spatial Task View on CRAN, but in general the gstat, automap, Fields, and geoR packages are a good start for a wide range of spatial interpolation methods.

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spatial interpolation for scattered data using natural neighbor in python

I'm trying to interpolate temperatures in a massive body made out of a scintillator called PbWO4. There are 10 outer temperature sensors called 'satellites' which help me refer to the inner temperatures at only 4 points. The main problem I'm facing…
samaetti
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R-interp: Same input & output coords (x,y,xo,yo), multiple data value vectors (z). Can I skip some steps?

I have to perform multiple 2-d linear interpolations from the same input coordinates (x,y) to the same output coordinates (xo,yo). Only the data values (z) change every time (z1,z2,z3,...). I have been using package interp on R. So far, I have been…
kdouv
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Spatial interpolation on a pandas Dataframe with a specific structure using a dictionary

Say I have a pd DataFrame that looks like this: price volume cat_count zipcode date 91111.0 01/01/2018 10 5 NaN 02/10/2018 NaN 9 NaN 94312.0 …
Mark Keane
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Reclassify values in a RasterBrick by the use of an additional Raster (Digital elevation model)

I have a RasterBrick consisting of daily snow cover data with the values 1, 2 and 3 (1= snow, 2= no snow, 3= cloud-obscured). Example of snow cover of one day: > snowcover class : Large RasterBrick dimensions : 26, 26, 2938 (nrow, ncol,…
user0405
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Haversine distance in sklearn.gaussian_process.kernels

Is there a builtin way to pass custom distance functions to be used by the kernels you could use for Gaussian Process Models? In particular, I have geographic data in lat/lon coordinates, so using Euclidean distances would not give accurate…
Ian Bolliger
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Interpolation across planar narrow-band level sets in OpenVDB

I'm aware of OpenVDB's voxel value interpolation for sparse point clouds, but I'm still learning. I have a triangle, and I find the narrow band level set of voxels it intersects. I assign the points in that level set to a point group and assign…
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How slow is too slow when kriging with gstat in R

I am trying to use the krige function in the gstat package of R to interpolate some spatial ocean depth data in R. I am finding for more than about ~1000 points, the function starts taking unreasonable amounts of time to finish (i.e., hours to days…
user3004015
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How to simplify a spline?

I have an interesting algorithmic challenge in a project I am working on. I have a sorted list of coordinate points pointing at buildings on either side of a street that, sufficiently zoomed in, looks like this: I would like to take this zigzag and…
Aleksey Bilogur
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Spatial interpolation with Copula

I was wondering if anyone knows about a library for spatial interpolation using Copulas in Python. I have already tried the copulalib and consequently ambhas, but none of them are suited for spatial interpolation. A reference to the problem can be…
Juan Chacon
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R: Autokrige.cv function in automap package generates NaNs

I’m fairly new to R and I am trying to make interpolations of temperature measurements that where gathered from different station across the Netherlands. I have data for about 35 stations that make measurements every 10 minutes covering a timespan…
T.Merkus
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Duplicate data when using gstat or automap package in R

I am trying to using ordinary kriging to spatially predict data where an animal will occur based on predictor variables using the gstat or automap package in R. I have many (over 100) duplicate coordinate points, which I cannot throw out since those…
ms.elasmo
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Interpolation resampling large irregular matrix or surface data points to regular grid

I am lost in all interpolation methods provided by great SciPy and can't find optimal way for my case. I have millions of XYZ points in matrix which has different intervals between points (cells) and also is rotated. Generally big datasets of points…
Miro
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R Spatial Interpolation of irregular grid data in R

Probably it is a newbie question, but I search for a while and unfortunately I did not find anything, which fits to my problem. I want to do a spatial interpolation between one finer irregular grid to one coarser irregular gird. The Problem: I have…
Boje
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Interpolation technique for weirdly spaced point data

I have a spatial dataset that consists of a large number of point measurements (n=10^4) that were taken along regular grid lines (500m x 500m) and some arbitrary lines and blocks in between. Single measurements taken with a spacing of about 0.3-1.0m…
Leo
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Spatial Overlay for Kriging Predictions in R

I have data that are structured as follows: winter.dat<-structure(list(ELON = c(-98.02325, -96.66909, -99.33808, -98.70974, -98.29216, -97.08568, -99.90308, -100.53012, -99.05847, -95.86621, -97.25452, -102.49713, -96.63121, -97.69394, -96.35404,…
Joe Ripberger
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