Applied Spatial Analysis
- Credits: 3
- Ending: Examination
- Range: 2C
- Semester: summer
- Year: 1
- Faculty of Economics and Finance
Teachers
Included in study programs
Teaching results
Knowledge - The graduate will gain knowledge of data science and statistical methods of empirical analysis of spatial data.
Skills - The graduate of the course will acquire advanced skills in the acquisition, modification and analysis of spatial socio-economic and environmental data with specialized software QGIS and GeoDa.
Competences - The graduate will be able to identify the necessary data based on the assigned social or economic problem, design appropriate methods and perform analysis based on spatial data.
Indicative content
1. Introductory overview of statistical methods of applied empirical spatial analysis.
2. Basic principles of management and visualization of point, line and area spatial data
3. Transformation of spatial data.
4. Exploratory analysis of point data (eg quadrant analysis, nearest neighbor method)
5. Spatial weights based on neighborhood and distance.
6. Application of spatial weights. Exploratory analysis of area data (neighborhood analysis, spatial autocorrelation).
7. Global measurements of the spatial concentration of one and more variables (Moran's I, correlogram).
8. Local measurements of spatial association (LISA).
9. Cluster analysis (K-means, hierarchical clustering, spatial clustering)
10. Spatial econometric models (spatial lag and spatial error model).
11. Presentation of the final project.
12. Presentation of the final project.
Support literature
1. Burt, J., E., Barber, G., M., Rigby, D., L., 2009. Elementary statistics for geographers. Third Edition. The Guilford Press, New York
2. Anselin, L. et al. (2020). GeoDa Workbook. University of Chicago. Dostupná on line https://geodacenter.github.io/documentation.html
3. Fischer, M., Getis, A., ed. 2010. Handbook of Applied Spatial Analysis. Springer, Berlin.
Requirements to complete the course
individual work, mid term tests
written / combined exam
40 % quality of the assignments
60 % quality of the final project
Student workload
Student workload 78 hours (participation in seminars 26 h, preparation for assignments 13 h, elaboration of final project 39 h)
Date of approval: 11.03.2024
Date of the latest change: 21.12.2021