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Look up timezones of point coordinates
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README.md

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lutz (look up timezones)

Input latitude and longitude values or an sf/sfc POINT object and get back the timezone in which they exist. Two methods are implemented. One is very fast and uses the V8 package to access the tz-lookup.js javascript library. However, speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone.

The other method is slower but more accurate - it uses the sf package to intersect points with a detailed map of time zones from here.

Installation

You can install lutz from CRAN with:

install.packages("lutz")

Or you can install the development version from github with:

# install.packages("devtools")
devtools::install_github("ateucher/lutz")

Examples

There are only two functions in this package: tz_lookup() which works with both sf/sfc and SpatialPoints objects, and tz_lookup_coords for looking up lat/long pairs. Use the method argument to choose the "fast" or "accurate" method.

With coordinates. They must be lat/long in decimal degrees:

library(lutz)
tz_lookup_coords(49.5, -123.5, method = "fast")
#> [1] "America/Vancouver"
tz_lookup_coords(49.5, -123.5, method = "accurate")
#> [1] "America/Vancouver"

tz_lookup_coords(lat = c(48.9, 38.5, 63.1, -25), lon = c(-123.5, -110.2, -95.0, 130))
#> [1] "America/Vancouver"    "America/Denver"       "America/Rankin_Inlet"
#> [4] "Australia/Darwin"

With sf objects:

library(sf)
library(ggplot2) # this requires the devlopment version of ggplot2

# Create an sf object out of the included state.center dataset:
pts <- lapply(seq_along(state.center$x), function(i) {
  st_point(c(state.center$x[i], state.center$y[i]))
})
state_centers_sf <- st_sf(st_sfc(pts))

# Use tz_lookup_sf to find the timezones
state_centers_sf$tz <- tz_lookup(state_centers_sf)
state_centers_sf$tz <- tz_lookup(state_centers_sf, method = "accurate")

ggplot() + 
  geom_sf(data = state_centers_sf, aes(colour = tz)) + 
  theme_minimal() + 
  coord_sf(datum = NA)

With SpatialPoints objects:

library(sp)
state_centers_sp <- as(state_centers_sf, "Spatial")

state_centers_sp$tz <- tz_lookup(state_centers_sp)

ggplot(cbind(as.data.frame(coordinates(state_centers_sp)), tz = state_centers_sp$tz), 
       aes(x = coords.x1, y = coords.x2, colour = tz)) + 
  geom_point() + 
  coord_fixed() + 
  theme_minimal()

We can compare the accuracy of both methods to the high-resolution timezone map provided by https://github.com/evansiroky/timezone-boundary-builder. This is the map that is used by lutz for the "accurate" method, but in lutz it is simplified by about 80% to be small enough to fit in the package.

## Get the full timezone geojson from https://github.com/evansiroky/timezone-boundary-builder
download.file("https://github.com/evansiroky/timezone-boundary-builder/releases/download/2018d/timezones-with-oceans.geojson.zip",
                destfile = "tz.zip")
unzip("tz.zip", exdir = "data-raw/dist/")
library(lutz)
library(sf)
library(rmapshaper)
library(purrr)

tz_full <- read_sf("data-raw/dist/combined-with-oceans.json")
# Create a data frame of 500000 lat/long pairs:
set.seed(1)
n <- 500000
ll <- data.frame(lat = runif(n, -90, 90), lon = runif(n, -180, 180))
ll_sf <- st_as_sf(ll, coords = c("lon", "lat"), crs = 4326)

# Overlay those points with the full high-resolution timezone map:
ref_ll_tz <- sf::st_join(ll_sf, tz_full)
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar

# run tz_lookup with both `"fast"` and `"accurate"` methods and compare with 
# the timezones looked up with the high-resolution map:
tests <- map_df(c("fast", "accurate"), ~ {
  time <- system.time(test_ll_tz <- tz_lookup(ll_sf, method = .x, warn = FALSE))
  comp <- ref_ll_tz$tzid == test_ll_tz
  matches <- sum(comp, na.rm = TRUE)
  mismatches <- sum(!comp, na.rm = TRUE)
  list(
    method = .x,
    time = time["elapsed"],
    matches = matches,
    mismatches = mismatches,
    accuracy = matches / (matches + mismatches),
    ref_nas = sum(is.na(ref_ll_tz$tzid)),
    fun_nas = sum(is.na(test_ll_tz))
    )
})
knitr::kable(tests)
method time matches mismatches accuracy ref_nas fun_nas
fast 2.721 384735 115265 0.769470 0 0
accurate 47.587 499956 44 0.999912 0 0
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