SpatialEstimate computes a continuous function for sparse values given with locations:

Compute estimated value at a location:

Plot the estimated surface:

Create a continuous spatial estimator function to visualize density:

Create spatial estimator function with constant trend:

Visualize spatial estimator function in the observation region inferred from the locations:

Rainfall amounts at locations in Switzerland:

Create spatial estimator function with linear trend:

Evaluate spatial estimator function at uniform random locations in the observation region:

Create spatial estimator for random data:

Define a variogram model to use:

Create spatial estimator function with the variogram model and quadratic trend:

Use SpatialEstimate with specified variogram function:

Create spatial estimator function with linear trend:

Find the variogram model used:

Fit another variogram model:

Compute the estimator functions with the fitted variogram model:

Compare the estimator surfaces of the two variogram models:

Often spatial data is given on a regular grid with missing regions, for example, in satellite ozone readings:

Create a continuous spatial estimator:

Find the estimated ozone value for a specific location:

Evaluate the spatial estimator function at uniform random locations in the observation region:

Visualize:

Use SpatialEstimate to create a continuous estimate from sparse observation locations:

Visualize data:

Compute estimates using specific models:

Create a set of random points and compute the estimated values at these locations:

Visualize rainfall values over the whole region:

SpatialEstimate can create a smoother picture of the data:

Standard ways to visualize the data:

Spatial estimate with smoothing:

Compute estimates for random locations:

Compute an estimate of the yield of the entire field:

Locations of scallop samples in the Atlantic Ocean, annotated with the total number of specimens caught as well as pre-recruit and adult numbers:

Extract the spatial point data:

First select locations with positive catch numbers:

Compute rate of recruits relative to the catch size:

Use SpatialEstimate to create an estimate of recruit rates from sparse catch locations:

Create a set of random points and compute the estimated values at these locations:

Visualize the recruit rates over the whole observation region:

For large spatial noise levels, the spatial estimator converges to the trend function:

Compute SpatialEstimate for increasing values of noise level for specified polynomial trend order:

Visualize:

Only with zero SpatialNoiseLevel is SpatialEstimate an exact interpolator:

Compute spatial estimate for varying values of spatial noise level:

Visualize the estimates with the data:

Some variograms can be badly conditioned:

The result does not represent the data well:

Allowing positive SpatialNoiseLevel acts as a regularization:

The noise level is computed automatically: