Spatial Estimation
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