Wrangling multivariate spatio-temporal data with the R package cubble

30 Apr 2022  ·  H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez ·

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.

PDF Abstract


  Add Datasets introduced or used in this paper