idw360: Inverse Distance Weighting with Directional Data

In aire.zmvm: Download Mexico City Pollution, Wind, and Temperature Data

Description

Function for inverse distance weighted interpolation with directional data. Useful for when you are working with data whose unit of measurement is degrees (i.e. the average of 35 degrees and 355 degrees should be 15 degrees). It works by finding the shortest distance between two degree marks on a circle.

Usage

idw360(values, coords, grid, idp = 2)

Arguments

values	the dependent variable
coords	the spatial data locations where the values were measured. First column x/longitude, second y/latitude
grid	data frame or Spatial object with the locations to predict. First column x/longitude, second y/latitude
idp	The inverse distance weighting power

Value

data.frame with the interpolated values for each of the grid points

Examples

library("sp")
library("ggplot2")

## Could be wind direction values in degrees
values <- c(55, 355)

## Location of sensors. First column x/longitud, second y/latitude
locations <- data.frame(lon = c(1, 2), lat = c(1, 2))
coordinates(locations) <- ~lon+lat

## The grid for which to extrapolate values
grid <- data.frame(lon = c(1, 2, 1, 2), lat = c(1, 2, 2, 1))
coordinates(grid) <- ~lon+lat

## Perform the inverse distance weighted interpolation
res <- idw360(values, locations, grid)
head(res)

## Not run: 
df <- cbind(res, as.data.frame(grid))
## The wind direction compass starts where the 90 degree mark is located
ggplot(df, aes(lon, lat)) +
  geom_point() +
  geom_spoke(aes(angle = ((90 - pred) %% 360) * pi / 180),
             radius = 1,
             arrow=arrow(length = unit(0.2, "npc")))

library("mapproj")
## Random values in each of the measuring stations
locations <- stations[, c("lon", "lat")]
coordinates(locations) <- ~lon+lat
crs_string <- "+proj=longlat +ellps=WGS84 +no_defs +towgs84=0,0,0"
proj4string(locations) <- CRS(crs_string)
values <- runif(length(locations), 0, 360)
pixels <- 10
grid <- expand.grid(lon = seq((min(coordinates(locations)[, 1]) - .1),
                              (max(coordinates(locations)[, 1]) + .1),
                              length.out = pixels),
                    lat = seq((min(coordinates(locations)[, 2]) - .1),
                              (max(coordinates(locations)[, 2]) + .1),
                              length.out = pixels))
grid <- SpatialPoints(grid)
proj4string(grid) <- CRS(crs_string)
## bind the extrapolated values for plotting
df <- cbind(idw360(values, locations, grid), as.data.frame(grid))
ggplot(df, aes(lon, lat)) +
  geom_point(size = .1) +
  geom_spoke(aes(angle = ((90 - pred) %% 360) * pi / 180),
             radius = .07,
             arrow=arrow(length = unit(0.2,"cm"))) +
  coord_map()

## End(Not run)

Example output

  pred
1   55
2  355
3   25
4   25
Loading required package: maps
Warning message:
In showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj = prefer_proj) :
  Discarded datum Unknown based on WGS84 ellipsoid in CRS definition
Warning message:
In showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj = prefer_proj) :
  Discarded datum Unknown based on WGS84 ellipsoid in CRS definition

aire.zmvm documentation built on May 2, 2019, 2:07 a.m.