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master's thesis
Prostorno-vremenski modeli u geostatici
2023. urn:nbn:hr:126:060919
Marušić, Laura
Josip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics

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Marušić, L. (2023). Prostorno-vremenski modeli u geostatici (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:060919

Marušić, Laura. "Prostorno-vremenski modeli u geostatici." Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2023. https://urn.nsk.hr/urn:nbn:hr:126:060919

Marušić, Laura. "Prostorno-vremenski modeli u geostatici." Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2023. https://urn.nsk.hr/urn:nbn:hr:126:060919

Marušić, L. (2023). 'Prostorno-vremenski modeli u geostatici', Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, accessed 16 April 2024, https://urn.nsk.hr/urn:nbn:hr:126:060919

Marušić L. Prostorno-vremenski modeli u geostatici [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics; 2023 [cited 2024 April 16] Available at: https://urn.nsk.hr/urn:nbn:hr:126:060919

L. Marušić, "Prostorno-vremenski modeli u geostatici", Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, Osijek, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:126:060919

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Metadata
TitleProstorno-vremenski modeli u geostatici
Title (english)Spatio-Temporal Models In Geostatistics
AuthorLaura Marušić
MentorDanijel Grahovac (mentor)
Committee memberNenad Šuvak (predsjednik povjerenstva)
Committee memberDanijel Grahovac (član povjerenstva)
Committee memberIvan Papić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics
Osijek
Defense date and country2023-10-24, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Applied Mathematics and Mathematical Modeling
Abstract
U ovom radu se upoznajemo s prostorno-vremenskim podacima kao proširenje vremenskih nizova, definiramo pripadne slučajne procese i navodimo razlike medu tipovima baza podataka. Nakon upoznavanja ove vrste podataka, u radu se zadržavamo na geostatističkom tipu podataka (”point referenced”). Krećemo od analize geostatističkih podataka koja se sastoji od vizualizacije i numeričkih karakteristika. Definiramo empirijsko prostorno i vremensko očekivanje, empirijsku prostornu kovarijancu i prostornovremenski variogram. Nakon toga podatke opisujemo modelom koji se najčešće koristi u geostatistici, a to je kriging, metoda interpolacije koja se dijeli na tri vrste, jednostavni, obični i univerzalni kriging. Sve prikazujemo na primjeru dnevnih maksimalnih temperatura u Europi u 2022. godini zabilježenih u klimatološkom centru američkog ratnog zrakoplovstva. Dobiveni rezultati i grafovi su dobiveni programskim jezikom R (inačica 4.2.2) te su neki dijelovi koda uključeni u radu.
Abstract (english)
In this thesis, the spatio-temporal data are introduced as an extension of time series models, we define the corresponding random processes and state the differences among the types of databases. After getting acquainted with spatio-temporal data, our focus shifts to geostatistical data (“point referenced”). We begin by analyzing geostatistical data, which involves data visualization and exploratory data analysis. We establish concepts like empirical spatial and temporal mean, empirical spatial covariance and spatio-temporal variogram. Following that, we describe the data using a widely used statistical technique called kriging, an interpolation method that comes in three variations, simple, ordinary and universal kriging. To illustrate these concepts, we use an example of daily maximum temperatures in Europe during the year 2022 obtained from the United States Air Force Climatology Center. We present our findings and graphs generated using the R programming language (version 4.2.2), and we also include some code in the thesis.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:060919
Study programmeTitle: Mathematics; specializations in: Financial and Statistical Mathematics, Mathematics and Computer Science, Industrial and Applied Mathematics
Course: Financial and Statistical Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-11-08 11:26:58