Abstract
Abstract Increasing amounts of rapidly growing data are the driving force behind proposing and automating new processing, enabling the extraction of useful information from data. One of such possibilities is determining trends to consider in terms of time and space. Thus far, the analysis of these aspects has been separate and lacked automated tools. Therefore, the authors proposed, implemented, and tested a tool for analyzing spatio-temporal linear trends. The tool was tested on PM10 concentration data in the years 2000–2018. The results, presented as cartographic visualization, were then evaluated, both in terms of time and space. The proposed approach facilitates analyzing spatio-temporal trends and assessing their accuracy; it can be developed using other types of analyzed trends or considering additional factors that influence the trend by using cokriging.
Highlights
In today’s data-filled world, analyzing and synthesizing changes occurring over time is one of the most significant challenges
The analysis of spatio-temporal trends is understood as an analysis of trends in time in a specific space (e.g. Kirk et al, 2012) with spatial aspects used as additional variables or the assessment of the spatial aspect amounts to a visual qualitative assessment
The model does not use the spatial interpolation of data to obtain information for places where no measurements have been made because the data used cover the surface continuously
Summary
In today’s data-filled world, analyzing and synthesizing changes occurring over time is one of the most significant challenges. Kirk et al, 2012) with spatial aspects used as additional variables (e.g. distances from particular objects) or the assessment of the spatial aspect amounts to a visual qualitative assessment. This approach may prove extremely useful for some purposes, it does not consider the spatial variability of temporal trends. The authors of the paper address this problem by proposing and testing the methodology for determining spatio-temporal trends. The authors present the option to model trends in time and space simultaneously and a method for evaluating the model created in such a manner. Creating the model, using it for prediction, and evaluating the obtained results are tested on real data using the implemented software
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