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Spatial Data Advanced Analysis Methods

Course Code:

GEO9130

Semester:

9th Semester

Specialization Category:

S.

Course Hours:

4

ECTS:

5

Eclass Link:

https://eclass.uniwa.gr/courses/GEO234/


LEARNING OUTCOMES

The course aims to familiarize students with geospatial data advanced analysis methods of both Statistics and Informatics. After the successful completions of the course, students acquire a set of knowledge and skills that allow them to:

  • Recognize the peculiarities of geographical data in relation to classical statistical analysis techniques
  • Evaluate statistical analysis results based on statistical tests
  • Propose different mathematical analysis models depending on the nature of the data
  • Understand scientific publications that apply advanced statistical analysis methods

 

General Competences

  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Individual work
  • Production of free, creative and inductive thinking

 

SYLLABUS

  1. Multiple regression analysis: variable selection methods, analysis of the residuals, spatial autocorrelation
  2. Specialized regression models: trend surface analysis, regression models for discrete variables, spatial regression models
  3. Multivariate analysis methods: factor analysis, cluster analysis, discriminant analysis
  4. Geostatistical methods: variogram, spatial interpolation (kriging), cokriging methods
  5. Geocomputation: neural networks, fuzzy logic, genetic algorithms, cellular automata, agent-based models

 

STUDENT PERFORMANCE EVALUATION

Language of evaluation: Greek
Methods of evaluation:

  • Written exam at the end of the semester (50%) which includes questions and exercises on both theoretical and practical objectives related to the course
  • Intermediate written exam (10%) which includes questions and exercises on both theoretical and practical objectives related to the course
  • Semester project (40%)

 

ATTACHED BIBLIOGRAPHY

1. Iliopoulou P. 2015. Spatial Anlaysis. [e-book] Athens Hellenic Academic Libraries Link (Heal Link). Available at http://hdl.handle.net/11419/2059
2. Kalogirou, S. (2015). Spatial Analysis (in Greek). [ebook] Athens: Hellenic Academic Libraries Link. Available online at: http://hdl.handle.net/11419/5029
3. Koutsopoulos, Κ. (2009). Discourse essay on Spatial Analysis, Volumes Α ́ and Β ́ (in Greek). Athens: Papasotiriou Publications.
4. Roiger, R. J. & Geatz, M.W. (2008). Data Mining: A Tutorial-Based Primer (in Greek). Athens: Klidarithmos Publications
5. Abrahart, R. J. & See, L. (2014). Geocomputation (2nd ed.). Boca Raton, FL: CRC Press.
6. Anselin, L. & Rey, S. J. (2014). Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL, GeoDa Press LLC, ISBN:0986342106
7. Fotheringham, S. A., Brudson, C. & Charlton, M. (2000). Quantitative Geography- Perspectives on Spatial Data Analysis, London: SAGE Publications.
8. Haining, R. (2004). Spatial data analysis. Theory and practice. Cambridge, UK: Cambridge University Press.
9. Isaaks, E. H. & Srivastava, M. R. (1989). Applied geostatistics. New York: Oxford University Press.
10. O’ Sullivan, D. & Unwin, D.J. (2010). Geographic Information Analysis, John Wiley.