smooth_space_time_variables: Spatial Interpolation with Kriging

In maxikellerbauer/stAirPol: Spatio-temporal Modelling of Air Pollution Data

Description

The functions takes the delivered informations of sensors and sensor_data and calculates a spatial interpolation with kriging for each unique timestamp. For the Kriging we use automap::autoKrige

Usage

smooth_space_time_variables(sensor_data, sensors = NULL, grid, agg_info,
  mc.cores = parallel::detectCores() - 1, quantiles_to_use = c(0, 1),
  times_IQR = 2.5, ...)

Arguments

sensor_data	data as returned by get_sensor_measured_values
sensors	data as returned by get_sensors
grid	an object as returned by make_grid_traffic
agg_info	an object as returnet by aggregation_information
mc.cores	how much cores should be used for parallelization, default is one core less your maximum number of detected cores.
times_IQR	remove outliers, which are more then times_IQR * IQR away from the median
...	arguments passed to automap::autoKrige

Value

the interpolated dataset around the delivered grid

maxikellerbauer/stAirPol documentation built on May 3, 2019, 3:16 p.m.