Multivariate spatio-temporal data refers to multiple measurements taken across space and time. For many analyses, spatial and time components can be separately studied: for example, to explore the temporal trend of one variable for a single spatial location, or to model the spatial distribution of one variable at a given time. However for some studies, it is important to analyse different aspects of the spatio-temporal data simultaneouly, like for instance, temporal trends of multiple variables across locations. In order to facilitate the study of different portions or combinations of spatio-temporal data, we introduce a new data structure, cubble, with a suite of functions enabling easy slicing and dicing on the different components spatio-temporal components. The proposed cubble structure ensures that all the components of the data are easy to access and manipulate while providing flexibility for data analysis. In addition, cubble facilitates visual and numerical explorations of the data while easing data wrangling and modelling. The cubble structure and the functions provided in the cubble R package equip users with the capability to handle hierarchical spatial and temporal structures. The cubble structure and the tools implemented in the package are illustrated with different examples of Australian climate data.

翻译：多维时空数据指在空间和时间维度上采集的多个测量值。对于许多分析而言，空间和时间成分可以分开研究：例如，探索单个空间位置上一个变量的时间趋势，或建模给定时间点某个变量的空间分布。然而，在某些研究中，需要同时分析时空数据的不同方面，例如多个变量在跨地点上的时间趋势。为便于研究时空数据的不同部分或组合，我们提出了一种新的数据结构cubble，并配套一组函数，实现对时空数据不同成分的灵活切分与重组。该cubble结构确保数据的所有成分易于访问和操作，同时为数据分析提供灵活性。此外，cubble在简化数据处理和建模的同时，促进了数据的可视化与数值探索。cubble结构及cubble R包中的函数使用户能够处理分层空间和时间结构。通过澳大利亚气候数据的不同实例，本文展示了cubble结构及其实现工具的实用性。