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geohashTools
This package provides tools for working with Gustavo Niemeyer’s geohash system of nestable, compact global coordinates based on Z-order curves. The system consists of carving the earth into equally-sized rectangles (when projected into latitude/longitude space) and nesting this process recursively.
Originally, we adapted C++ source from Hiroaki Kawai, but have now rewritten the implementation completely with a new approach in C.
Encoding is the process of turning latitude/longitude coordinates into geohash strings. For example, Parque Nacional Tayrona in Colombia is located at roughly 11.3113917 degrees of latitude, -74.0779006 degrees of longitude. This can be expressed more compactly as:
library(geohashTools)
gh_encode(11.3113917, -74.0779006)## [1] "d65267"These 6 characters identify this point on the globe to within 1.2 kilometers (east-west) and .6 kilometers (north-south).
The park is quite large, and this is too precise to cover the park;
we can “zoom out” by reducing the precision (which is the number of
characters in the output, 6 by default):
gh_encode(11.3113917, -74.0779006, precision = 5L)## [1] "d6526"We can use this as a simple, regular level of spatial aggregation for spatial points data, e.g., counting presence of public art throughout the city of Chicago, as captured in a dataset
provided by the City:
NB: As of this writing, the Chicago data portal is down, apparently temporarily. However I’d like to submit this package update to CRAN in order to avoid deprecation issues around {rgdal} & co, so please check back on the website for a working version of the code below.
## first, pull the data internally from https://data.cityofchicago.org
# api_stem = 'https://data.cityofchicago.org/api/views'
# URL = file.path(api_stem, 'sj6t-9cju/rows.csv?accessType=DOWNLOAD')
# suppressPackageStartupMessages(library(data.table))
# art = fread(URL)
# count art by geohash
# gh_freq = art[, .N, by = .(geohash = gh_encode(LATITUDE, LONGITUDE, 5L))]
# only show the top 10
# gh_freq[order(-N)][1:10]This is pretty impractical per se (where is
dp3wm?); we’ll return to this once we’ve introduced more
functionality.
The reverse of encoding geohashes is of course decoding them – taking
a given geohash string and converting it into global coordinates. For
example, the Ethiopian coffee growing region of Yirgacheffe is roughly
at sc54v:
gh_decode('sc54v')## $latitude
## [1] 6.130371
##
## $longitude
## [1] 38.21045It can also be helpful to know just how precisely we’ve identified
these coordinates; the include_delta argument gives the
cell half-widths in both directions in addition to the cell
centroid:
gh_decode('sc54v', include_delta = TRUE)## $latitude
## [1] 6.130371
##
## $longitude
## [1] 38.21045
##
## $delta_latitude
## [1] 0.02197266
##
## $delta_longitude
## [1] 0.02197266In terms of latitude and longitude, all geohashes with the same
precision have the same dimensions (though the physical size of the
“rectangle” changes depending on the latitude); as such it’s easy to
figure out the cell half-widths from the precision alone using
gh_delta:
gh_delta(5L)## [1] 0.02197266 0.02197266One unfortunate consequence of the geohash system is that, while
geohashes that are lexicographically similar (e.g. wxyz01
and wxyz12) are certainly close to one another, the
converse is not true – for example, 7gxyru and
k58n2h are neighbors! Put another way, small movements on
the globe occasionally have visually huge jumps in the geohash-encoded
output.
Fret not – one tool for helping overcome this is the
gh_neighbors function (gh_neighbours is also
registered, for the Commonwealthy among us), which will return all of
the geohashes adjacent to a given geohash (or vector of geohashes) at
the same level of precision.
For example, the Merlion statue in Singapore is roughly at
w21z74nz, but this level of precision zooms in a bit too
far. The geohash neighborhood thereof can be found with:
gh_neighbors('w21z74nz')## $self
## [1] "w21z74nz"
##
## $southwest
## [1] "w21z74nw"
##
## $south
## [1] "w21z74ny"
##
## $southeast
## [1] "w21z74pn"
##
## $west
## [1] "w21z74nx"
##
## $east
## [1] "w21z74pp"
##
## $northwest
## [1] "w21z74q8"
##
## $north
## [1] "w21z74qb"
##
## $northeast
## [1] "w21z74r0"geohashTools offers several helper functions for
interfacing your geohash objects with GIS tools in R, namely
sp and sf. This will facilitate the best part
of working with GIS data – the visualizations!
Returning to public art locations in Chicago, we can visualize the
spatial aggregations carried out above by converting to sp,
combining with a shapefile of Chicago, and plotting:
library(sf)
## first, pull neighborhood shapefiles from https://data.cityofchicago.org
# tmp = tempfile(fileext = '.zip')
# shp_url = file.path(
# api_stem, '9wp7-iasj', 'files',
# 'TMTPQ_MTmUDEpDGCLt_B1uaiJmwhCKZ729Ecxq6BPfM?filename=Neighborhoods_2012.zip'
# )
# download.file(shp_url, tmp)
# chicago = paste0('/vsizip/', tmp) |>
# st_read(quiet = TRUE) |>
# st_transform(crs = 4326L)
# artSF = gh_to_sf(
# art[, .N, by = .(geohash = gh_encode(LATITUDE, LONGITUDE, 6L))],
# gh_col = 'geohash'
# )
# plot(st_geometry(chicago), lwd = 0.5, main = 'Public Art Locations in Chicago')
# plot(artSF['N'], add = TRUE)Chicago connoisseurs will recognize the biggest concentration around Lincoln Park, with another concentration along the waterfront near Millenium/Grant Parks.
The process for sf is similar; just replace
gh_to_spdf with gh_to_sf.
You might get benefit out of these more interactive online tools for working with geohashes:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.