Abstract
Abstract Geoscientists are continuously confronted by difficulties involved in handling varieties of data formats. Configuration of data only in time or space domains leads to the use of multiple stand-alone software in the spatio-temporal analysis which is a time-consuming approach. In this paper, the concept of cellular time series (CTS) and three types of meta data are introduced to improve the handling of CTS in the spatio-temporal analysis. The data structure was designed via Python programming language; however, the structure could also be implemented by other languages (e.g., R and MATLAB). We used this concept in the hydro-meteorological discipline. In our application, CTS of monthly precipitation was generated by employing data of 102 stations across Iran. The non-parametric Mann–Kendall trend test and change point detection techniques, including Pettitt's test, standard normal homogeneity test, and the Buishand range test were applied on the generated CTS. Results revealed a negative annual trend in the eastern parts, as well as being sporadically spread over the southern and western parts of the country. Furthermore, the year 1998 was detected as a significant change year in the eastern and southern regions of Iran. The proposed structure may be used by geoscientists and data providers for straightforward simultaneous spatio-temporal analysis.
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