548-0044/08 – Spatial Data Analysis (PAD)
| Gurantor department | Department of Geoinformatics | Credits | 5 |
| Subject guarantor | prof. Ing. Jiří Horák, Dr. | Subject version guarantor | prof. Ing. Jiří Horák, Dr. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2021/2022 | Year of cancellation | |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The objective is to learn student how to utilize selected methods of spatial analysis not included in other courses. It is focused on circular statistics, modelling of spatial distribution of events and relevant inferential methods to analyse their randomness, including multiple events. Large attention is dedicated to graph theory and its application for spatial tasks, statistical description of networks (local and global measures), selected tasks in graphs. Students get acqauinted with locational and alocational tasks, utilization of gravity theory, selected analysis for areal data, multivariate techniques for spatial data and a logistic regression.
Teaching methods
Lectures
Tutorials
Summary
The subject represents an advanced course of spatial analytical methods. It contains descriptive statistics for dots, circular statistics, methods of modelling of spatial distribution of events, inferential methods to analyse randomness of single dots as well as multiple events, explains basic terms of the graph theory, indicators used for local and global description of networks namely social networks, it introduces to the evaluation of transport accessibility, it explains selected tasks in graphs, basic methods for locational and alocational tasks, utilization of gravity theory, introduces selected analysis for areal data and multivariate techniques for spatial data, a basic predictive model for categorised variables using the logistic regression.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Students are asked about knowledge from areas that they should have already known from previous lectures. Students also work on individual tasks. Tasks are frequently based on understanding of previous, simpler tasks. Student must pass writing and oral exam.
E-learning
Other requirements
No additional requirements are imposed on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Spatial analysis. Objectives and types of analysis. Descriptive statistics for point pattern.
2. Circular statistics.
3. Modelling of point spatial patterns – theoretical models.
4. Inferential statistical tests for point pattern. Analysis of multivariable point events.
5. Introduction to the graph theory. Graph types, spatial structures.
6. Statistical description of graphs and networks. Transport accessibility.
7. Selected tasks in graphs (MST, Gabriel network, Steiner tree, optimal route, traveler salesman problem).
8. Theoretical models of networks
9. Location and allocation tasks. Gravity theory. Analysis of interaction data.
10. Selected analysis for polygons (Areal interpolation. Districting, regionalization. Smoothing. Regression).
11. Multivariate techniques for spatial data – PCA, FA, DA
12. Multivariate techniques for spatial data - hierarchical and non-hierarchical spatial clustering
13. Logistic regression
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction