Areal data are a rather frequent type of data in many applications of
the environmental and socioeconomic sciences, where various aspects are
summarized for particular areas such as administrative territories. Many
of those applications surpass the spatial, temporal or thematic scope of
any single data source, so that data must be harmonised and normalised
across many distinct standards. arealDB
has been developed for the
purpose of building a standardised database encompassing all issues that
come with this. In the current, revised version, it makes use of the
ontologics
R-package to harmonise the names of territories (from
geometries) and the target variables (from tables). Moreover, it uses
the tabshiftr
R-package to reshape disorganised tabular data into a
common format.
1) Install the official version from CRAN:
install.packages("arealDB")
or the latest development version from github:
devtools::install_github("luckinet/arealDB")
2) Read the paper for a more scientific background, or study the vignette on the arealDB pipeline.
To study how arealDB
works, one can make use of the function
makeExampleDB()
, where the full process of building an areal database
can be “simulated” with dummy data. This can be used to train yourself
on a particular step based on a fully valid database up until a certain
stage of the process. For instance, to set up database that has merely
just been started, but doesn’t contain any thematic data yet, one would
use
makeExampleDB(path = paste0(tempdir(), "/newDB"), until = "start_arealDB")
.
In principle, arealDB
follows a simple process involving three stages:
This work was supported by funding to Carsten Meyer through the Flexpool mechanism of the German Centre for Integrative Biodiversity Research (iDiv) (FZT-118, DFG).
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