Applied analysis of spatial data using R (197700)

Course coordinators

Course description

Advances in technology, low prices and availability of GPS devices resulted in overall presence of georeferenced data and heightened interest for spatial analysis. The goal of those analyses is describing spatial ordering of different processes (resources, pollution, biodiversity…) and modeling samples with that spatial structure. Modifying and linking a large number of statistical methods for analyzing and presentation of spatial data trough different software can be intimidating for new users. Main goal of this course is systematical introduction of spatial data analysis using case studies and hands-on approach that will enable students to analyze, visualize and interpret spatial data.
We chose open source system of R to demonstrate the analyses of spatial data because it ensures input, manipulation, analysis and presentation of spatial data in the same environment. Broad base of R users ensure quality and fast expansion of different functions (using packages) as well as tutorials and literature in different languages (predominately English).

Type of course

ECTS: 6.00

Teaching hours: 60
Lectures: 30
Practicum: 26
Seminar: 4

Associate teacher for exercises
Grading

Sufficient (2): 60-70%
Good (3): 71-80%
Very good (4): 81-90%
Excellent (5): 91-100%

General competencies

To understand structure and content of georeferenced biological data. To critically choose and apply appropriate analysis. To show and interpret results of spatial interpolation of climatological (eg. temperature, rainfal etc.), environmental (eg. element concentration in watter and sediment, feed content etc.) and biological (species density, population density, genetic diversity etc.) spatial data. By analizing their own spatial data and using appropriate georeferenced layers to find locations uning spatial query (eg. temperature or rainfal span; habitat suitability etc.). To use open source R environment with no need to learn different interfaces of abundand special software and apps.

Types of instruction

  • Lectures
  • Other
    partial e-learning; independent assignments; multimedia and the internet
  • Seminars
  • Exercises

Learning outcomes

Learning outcome Evaluation methods
Understand spatial data and work with spatial objects using R. Seminar, written exam, oral exam
Graphicaly show their spatial dana using different graphical devices in R (screen device, .pdf, .eps, .png graphical devices). Seminar, written exam, oral exam
Use online repositories to complement their own spatial data in R. Seminar, written exam, oral exam
Apply and interpret spatial analysis using R packages. Seminar, written exam, oral exam
Analyse and interpret relevant publications on spatial analysis. Seminar, written exam, oral exam

Methods of grading

Evaluation elements Maximum points or Share in evaluation Grade rating scale Grade Direct teaching hours Total number of average student workload ECTS
Pohađanje nastave predavanja+vježbe 5% 26 26 0,7
Aktivno sudjelovanje na nastavi 5% 4 0,1
Seminarski rad (S) (priprema+prezentacija) 10% 4 10 0,5
Parcijalni ispit 1 (PI1) 35% 60-70%
71-80%
81-90%
91-100%
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
0 20 0,7
Parcijalni ispit 2 (PI2) 35% 60-70%
71-80%
81-90%
91-100%
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
0 20 0,7
Usmeni ispit (UI) 10% 60-70%
71-80%
81-90%
91-100%
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
0 10 0,3
UKUPNO 100% (S+PI1+PI2+UI/4) 30 90 3
Evaluation elements Description Deadline Recoupment
Pohađanje nastave predavanja+vježbe Na nastavi se redovito bilježe nazočni studenti (na početku i kraju bloka) i prati sposobnost discipliniranog i aktivnog praćenja nastave Može se opravdati izostanak do 20% predavanja, 15% vježbi i 15% seminara (čl. 12 Pravilnika o studiranju…. na AFZ-u) Semestar (30 sati izravne nastave) Moguća putem samostalnog zadatka (0.5 ECTS)
Aktivno sudjelovanje na nastavi Studenti se potiču sudjelovati u raspravama, prezentaciji ideja i problemskih rješenja, argumentiranju mišljenja i stavova. Prati se usvajanje teorijskih i činjeničnih znanja, prezentacijskih i komunikacijskih vještina, kritičkog mišljenja, timskog rada i društvene odgovornosti. Prati se sposobnost samostalnog izvođenja laboratorijskih vježbi. Zapažena aktivnost na satu bilježi se u studentskoj evidenciji (+), što omogućuje korekciju konačne ocjene naviše (++) ili beneficiju na usmenom ispitu (+++). Kontinuirano tijekom izvođenja nastave Moguća putem samostalnog zadatka (0.1 ECTS)
Seminarski rad (S) (priprema+prezentacija) Seminarski rad na početku semestra zadužuje svaki student. Pisani rad se predaje asistentici na pregled najmanje tjedan dana prije izlaganja. Korigirani rad predaje se pri izlaganju. Izlaganja seminarskih radova počinju u 14. tjednu nastave u semestru prema dogovorenom rasporedu. Pri izlaganju seminarskog rada ocjenjuju se prezentacijske vještine, analitičnost i sposobnost zaključivanja (sinteze). 1. tjedan 14. tjedan 15. tjedan Moguća putem samostalnog zadatka (0,5ECTS)
Parcijalni ispit 1 (PI1) Obuhvaća prvi programski dio modula:teoriju iz domene voćnih i povrtnih sirovina koje se koriste za dekorativnu primjenu, mogućnosti i primjena procesa dorade i prerade voća i povrća za dekorativnu primjenu (Voća). Pitanja iz teorijskog dijela su otvorenog tipa i ispituju poznavanje i razumijevanje činjenica. 4.tjedan
Parcijalni ispit 2 (PI2) Obuhvaća drugi programski dio modula: teoriju iz domene procesa sušenja,hlađenja, utjecaja biljnih hormona na fiziološke procese te kandiranje pojedinih voćnih vrsta te njihova primjena u dejkorativne svrhe (Voća). Pitanja iz teorijskog dijela su otvorenog tipa i ispituju poznavanje i razumijevanje činjenica. 15.tjedan
Usmeni ispit (UI) Usmeni ispit se sastoji od tri, eventualno dva pitanja (+++), ovisno o prethodnoj aktivnosti studenta . Testira se usvojenost teorije i činjenica, analitičnost, kritičko mišljenje, kreativnost i društvena odgovornost. Ispitni rokovi
Nadoknada (samostalni zadatak) Ukoliko student ne ostvari nužna 2 ECTS boda kao preduvjet izlaska na usmeni ispit, jedan bod je moguće nadoknaditi dodatnim samostalnim zadatkom, npr: prijevod stručnog teksta s engleskog jezika i izlaganje pred nastavnikom, prikaz članka ili knjige, projektna ideja i sl. Tijekom ispitnih rokova, prije usmenog ispita

Weekly class schedule

  1. Structure of spatial data and available systems for their visualization and analysis
  2. Spatial data in R
  3. Defining coordinate systems and spatial projections to R spatial objects
  4. Spatial data visualization using R
  5. Using spatial software trough R without learning new interfaces
  6. Introduction to spatial analysis and methods depending on the type of data
  7. Data connectedness, autocorrelation with nominal scale attributes
  8. Point processes
  9. Topology
  10. Spatial regression
  11. Similarity and suitability models
  12. Clustering spatial data
  13. Introduction to digital image analysis
  14. Student seminars and homework
  15. Exam

Obligatory literature

  1. Bivand RS., Pebesma EJ., Gómez-RubioV. (2013). Applied Spatial Data Analysis with R (Use R). Springer.
  2. Malvić T. (2013). Rječnik osnovnih geostatističkih pojmova.
  3. Safner T., Miller MP., McRae BH., Fortin MJ., Manel S. (2011) Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics. International Journal of Molecular Sciences 12 (2), 865-889.
  4. Safner T., Miaud C., Gaggiotti O., Decout S., Rioux D., Zundel S., Manel S. (2011) Combining demography and genetic analysis to assess the population structure of an amphibian in a human-dominated landscape. Conservation genetics 12 (1), 161-173.
  5. Šprem N., Frantz AC., Cubric Curik V., Curik I. (2013) Influence of habitat fragmentation on population structure of red deer in Croatia; Mammalian Biology - Zeitschrift für Säugetierkunde.

Recommended literature

  1. Spatial statistics. Bryan Ripley (http://www.people.fas.harvard.edu/~zhukov/spatial.html)

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