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.

环境和社会经济科学中的许多相关应用程序都使用区域数据，例如生物多样性清单，农业统计数据或社会经济调查。对于超出任何单个数据源的空间，时间或主题范围的应用程序，必须从多个异构源中集成数据。不一致的概念，定义或混乱的数据表使此过程变得乏味且容易出错。迄今为止，仍缺乏解决这些挑战的专用工具。

在这里，我们介绍了R包arealDB，它在易于使用的工作流程中将异构面数据和关联的几何图形集成到一致的数据库中。它对于协调变量的语言和语义，将数据与几何图形相关以及记录元数据和出处很有用。我们通过在大豆收割面积上整合两个不同的数据集（巴西，美国）来说明功能。arealDB承诺提高下游科学，监控和管理应用程序的质量，同时也为数据库整理工作节省大量时间。