<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Determination and evaluation of spatio-temporal trends on the example of PM10 concentration in Poland (2000−2018)

Polish Cartographical Review

This work is licensed under the Creative Commons Attribution 4.0 International License.

Increasing amounts of rapidly growing data are the driving force behind proposing and automating new processing, enabling the extraction of useful information...

Determination and evaluation of spatio-temporal trends on the example of PM10 concentration in Poland (2000−2018)

Online veröffentlicht: 05. Okt. 2021

Seitenbereich: 37 - 48

Eingereicht: 21. Juni 2021

Akzeptiert: 13. Aug. 2021

DOI: https://doi.org/10.2478/pcr-2021-0004

Schlüsselwörter
PM10 concentration, spatio-temporal trends, interpolation

© 2021 Malwina Jackowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Determination and evaluation of spatio-temporal trends on the example of PM10 concentration in Poland (2000−2018)

Online veröffentlicht: 05. Okt. 2021

Seitenbereich: 37 - 48

Eingereicht: 21. Juni 2021

Akzeptiert: 13. Aug. 2021

DOI: https://doi.org/10.2478/pcr-2021-0004

SchlüsselwörterPM10 concentration, spatio-temporal trends, interpolation

© 2021 Malwina Jackowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
PM10 concentration, spatio-temporal trends, interpolation