Spatial data is, of course, everywhere. For some areas it is important enough to measure and model, including: weather (temperature, precipitation, wind speed, ...), energy (solar irradiance, average wind speed, hydrocarbons, ...), minerals (rare earth metals, gold, ...), pollution (ozone, nitric oxide, ...), agriculture (soil nutrition levels, ground water levels, ...). And as the cost of getting spatial data is declining quickly, much more is available. The Wolfram Language provides the tools needed to fill in the missing values for spatial data, either using a fully automated workflow or giving you detailed control over the various elements of spatial estimation.

Spatial Estimation of Field Values

SpatialEstimate — gives a function that can be used to estimate values

VariogramFunction  ▪  SpatialTrendFunction  ▪  SpatialNoiseLevel  ▪  SpatialEstimatorFunction

Detailed Spatial Dependence Measures

VariogramModel — symbolic model of a variogram

EstimatedVariogramModel — model-based variogram estimation

BinnedVariogramList — binned variogram estimation

Curated Spatial Data

GeoElevationData — elevation above sea level at different points

WindSpeedData  ▪  AirTemperatureData  ▪  AirPressureData  ▪  WindDirectionData  ▪  WeatherData  ▪  GeomagneticModelData  ▪  GeogravityModelData

Collected Spatial Data

"Meuse River" — soil component levels near the Meuse river

"US Ozone 2021" — ozone levels over the contiguous United States

"Swiss Rainfall" — rainfall levels across Switzerland