plot_map {TSCS}

Visualize Spatial(Cross-Section) Data of a Given Time Point - 2D Map

Description

plot_map draws a two-dimensional spatial map. It is plotted based on the cross-section data of a given time point, which is also often extracted from spatio-temporal data.

Usage

plot_map(newdata, xlab = NULL, ylab = NULL, title = NULL, cex = 2,
  shape = 15, low = "blue", mid = "yellow", high = "red",
  na.value = "white", midpoint = NULL)

Arguments

newdata

data frame; should only contain the three variables in order: X coordinate, Y coordinate and observation. This is the cross-section data or pure spatial data of a particular time point you have selected, with missing observations that you want to predict. (coordinates must be numeric)

xlab

a label for the x axis, defaults to the name of X coordinate.

ylab

a label for the y axis, defaults to the name of Y coordinate.

title

a main title for the plot.

cex

numeric; size of plotting point for each spatial locations. (default: 2)

shape

either an integer specifying a symbol or a single character to be used as the default in plotting points. (default: 15)

low, high

colours for low and high ends of the gradient. (default: "blue","red")

mid

colour for midpoint of the gradient. (default: "yellow")

na.value

colour for missing values/observations. (default: "white")

midpoint

numeric; the midpoint of the gradient scale, defaults to the midpoint value of index presented.

Details

plot_map is exclusive to 2D rectangular grid system. Similarly, if you want to fathom how this package handles 3D rectangular grid system, please refer to plot3D_map.

See Also

plot3D_map, plot_NA, plot_dif

Examples

## Not run: 

## TSCS spatial interpolation procedure:

basis <- tscsRegression(data = data, h = 1, v = 1, alpha = 0.01); # regression
basis$percentage # see the percentage of cointegrated relationships
est <- tscsEstimate(matrix = basis$coef_matrix, newdata = newdata, h = 1, v = 1); # estimation
str(est)

## comparison of estimates and true values:

plot_compare(est = est$estimate[,3], true = true) # graphic comparison
index <- appraisal_index(est = est$estimate[,3], true = true); # RMSE & std
index

## data visualization:

plot_dif(data = data[,1:2], h = 1, v = 1) # differentiate boundary and interior spatial locations
plot_NA(newdata = newdata) # show spatial locations with missing value, for a cross-section data
plot_map(newdata = newdata) # plot the 2D spatial map, for a cross-section data

## End(Not run)