Calculate rolling average gradient from elevation data at segment level

Calculate rolling average gradient from elevation data at segment level

Usage

route_rolling_gradient(elevations, distances, lag = 1, n = 2, abs = TRUE)

Arguments

elevations

Elevations, e.g. those provided by the cyclestreets package

distances

Distances, e.g. those provided by the cyclestreets package

lag

The window size of the smoothing function. The default, 3, will take the mean of values before, after and including each value.

n

The window size of the smoothing function. The default, 3, will take the mean of values before, after and including each value.

abs

Should the absolute (always positive) change be returned? True by default

See also

Other route_funs: route_average_gradient(), route_rolling_average(), route_rolling_diff(), route_sequential_dist(), route_slope_matrix(), route_slope_vector()

Examples

r1 <- od_data_routes[od_data_routes$route_number == 2, ]
y <- r1$elevations
distances <- r1$distances
route_rolling_gradient(y, distances)
#> [1] 0.009112333 0.029220779 0.106132075 0.036435786 0.047012302 0.048840049
#> [7] 0.017539244          NA
route_rolling_gradient(y, distances, abs = FALSE)
#> [1] -0.009112333 -0.029220779 -0.106132075  0.036435786  0.047012302
#> [6] -0.048840049 -0.017539244           NA
route_rolling_gradient(y, distances, n = 3)
#> [1]         NA 0.02191558 0.14966741 0.03888604 0.05635534 0.01285072 0.02380952
#> [8]         NA
route_rolling_gradient(y, distances, n = 4)
#> [1]         NA 0.02371542 0.08991009 0.05085599 0.06604938 0.01523810         NA
#> [8]         NA
r1$elevations_diff_1 <- route_rolling_diff(y, lag = 1)
r1$rolling_gradient <- route_rolling_gradient(y, distances, n = 2)
r1$rolling_gradient3 <- route_rolling_gradient(y, distances, n = 3)
r1$rolling_gradient4 <- route_rolling_gradient(y, distances, n = 4)
d <- cumsum(r1$distances) - r1$distances / 2
diff_above_mean <- r1$elevations_diff_1 + mean(y)
par(mfrow = c(2, 1))
plot(c(0, cumsum(r1$distances)), c(y, y[length(y)]), ylim = c(80, 130))
lines(c(0, cumsum(r1$distances)), c(y, y[length(y)]))
points(d, diff_above_mean)
abline(h = mean(y))
rg <- r1$rolling_gradient
rg[is.na(rg)] <- 0
plot(c(0, d), c(0, rg), ylim = c(0, 0.2))
points(c(0, d), c(0, r1$rolling_gradient3), col = "blue")
points(c(0, d), c(0, r1$rolling_gradient4), col = "grey")

par(mfrow = c(1, 1))