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：多变量时空数据是指在不同空间位置和时间点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别研究：例如，探索单一空间位置某一变量的时间趋势，或建模某一时刻变量的空间分布。然而在某些研究中，需同时分析时空数据的不同维度，如多个变量在各地点的时序变化特征。为便于研究时空数据的局部组合与维度分割，我们提出了一种名为cubble的新型数据结构，并配套开发了一系列函数，可对时空数据的各成分进行灵活切片与切块操作。该数据结构确保数据各成分易于访问和操作，同时为数据分析提供灵活性。此外，cubble在简化数据整理与建模流程的同时，支持数据的可视化与数值探索。cubble数据结构及R包cubble配套函数赋予用户处理层次化时空结构的能力。本文通过澳大利亚气候数据的多个实例，演示了cubble数据结构及其实现工具的完整应用流程。