  Many relevant applications in the environmental and socioeconomic sciences
use areal data, such as biodiversity checklists, agricultural statistics, or
socioeconomic surveys. For applications that surpass the spatial, temporal or
thematic scope of any single data source, data must be integrated from several
heterogeneous sources. Inconsistent concepts, definitions, or messy data tables
make this a tedious and error-prone process. To date, a dedicated tool to
address these challenges is still lacking. Here, we introduce the R package
arealDB that integrates heterogeneous areal data and associated geometries into
a consistent database, in an easy-to-use workflow. It is useful for harmonising
language and semantics of variables, relating data to geometries, and
documenting metadata and provenance. We illustrate the functionality by
integrating two disparate datasets (Brazil, USA) on the harvested area of
soybean. arealDB promises quality-improvements to downstream scientific,
monitoring, and management applications but also substantial time-savings to
database collation efforts.
