TSCS {TSCS} | R Documentation |
This package provides functions to implement TSCS spatial interpolation and relevant data visualization. For TSCS method, the current version is only able to make use of spatio-temporal data whose spatial domain is a 2D or 3D rectangular grid system.
TSCS (abbr. of Time Series Cointegrated System) method is a spatial interpolation method based on analysis of historical spatio-temporal data. It can be regarded as a desirable alternative to spatio-temporal interpolation in some cases where we merely intend to interpolate a series of cross-section data at each observed time point for a given spatial domain.
The basic assumption of TSCS method is that, for any spatial location within the spatial domain of spatio-temporal data, its time series and the time series of its adjacent spatial locations are cointegrated (long-term equilibrium relationships).
As to TSCS method, package of the current version is only able to make use of spatio-temporal data whose spatial domain is a 2D or 3D rectangular grid system.
tscsRegression, tscsRegression3D
: obtains regression coefficient matrix, the first step of
TSCS for 2D and 3D rectangular grid system respectively.
tscsEstimate, tscsEstimate3D
: estimates the missing observations within a cross-section data
(pure spatial data) of a particular time point you have selected, the second step of TSCS for 2D and 3D
rectangular grid system respectively.
plot_dif, plot3D_dif
: differentiates boundary and interior spatial locations in a spatial domain.
plot_NA, plot3D_NA
: shows spatial locations with or without missing observation in a spatial domain.
plot_map, plot3D_map
: draws the spatial map for a cross-section data.
plot_compare
: visualizes the comparison between estimates and true values (if you have).
appraisal_index
: computes the two appraisal indexes used for evaluating the result of
interpolation/prediction - RMSE and standard deviation of error. (if you have the true values)
Tianjian Yang <yangtj5@mail2.sysu.edu.cn>