Spatial analysis
Objectives and Contextualisation
At the end of the course, the student will be able to:
Dominate at the practical level the different tools related to the interpolation and terrain analysis.
Use the main applications for the generation of new information from GIS data.
Identify the concepts associated with spatial analysis, its applications and its limitations.
Content
ANALYSIS IN GIS
1. General Concepts of GIS Analysis
1.1 Introduction
1.2 Specifications regarding the data model
1.3 Combining raster-vector analysis
2. Layers combinations
2.1 Variants and possibilities
2.2 Vector overlay
2.3 Transfer of attributes
2.4 Categorical data
3. Map algebra
3.1 Previous conditions
3.2 Characteristics
3.3 NODATA
3.4 Multicriteria decision analysis
4. Propagation of errors
4.1 Geometric quality criteria
4.2 Thematic quality criteria
4.3 Elimination of results by criteria of geographical insignificance
5. Analysis of the landscape
5.1 Introduction to the conceptual and methodological framework of landscape ecology
5.2 Calculation and analysis of landscape indexes at various scales
5.3 Analysis of the ecological connectivity of the landscape
6. Spatial autocorrelation
6.1 Concepts
6.2. Indicators and indexs.
7. Space interpolation
6.1 Concepts
6.2 Polygons of Thiessen
6.3 Trend surfaces
6.4 Kriging
8. Logistic regression
8.1 Characteristics
8.2 Spatial applications
8.3 Limitations and adjustments of models
9. Analysis of distances
9.1 Cartesian distances and geodesic distances
9.2 Generation of continuous and buffer maps
9.3 Anisotropic distances and cost analysis
9.4 Introduction to network analysis
DIGITAL TERRAIN MODELS
1. Concepts
1.1 Fundamental concepts and terminology (DTM, DEM, DSM, etc.)
1.2 Models of data: raster, TIN, isolines, etc. BIM and LoD#.
1.3 Vertical and geoid duck
2. Collection ofdata. Primary (field, photogrammetry, lidar, InSAR, etc.) and Secondary
3. Generation of DTM
3.1 Interpolation from points: Inverse of the weighted distance (IDW), splines, kriging. Neighborhood Statistics. Processing of lidar data
3.2 Interpolation from isolines
3.3 Generation of TIN models
4. Quality of MDT
4.1 Altimeter quality
4.2 Control of the error in the DTM
4.3 Propagation of error in derivative models
5. Derived models
5.1 Slope, orientations, curvatures, etc.
5.2 Hydrographic basins, drainage network
5.3 Illumination, shading and solar radiation
6. Applications
6.1 Applications in the processing of remote sensing images: geometric and radiometric image rectification.
6.2 Topographical profiles and visibility analysis
6.3 Three-dimensional perspectives
INTERFEROMETRY
1. Introduction
1.1. Image sensors
1.2. Spectral window
1.3. SAR missions (Synthetic Aperture Radar)
2. SAR concept
2.1. Classical radar
2.2. SAR technical concepts
3. SAR image
3.1. Spectral and reflection characteristics
3.2. Geometric distortions
3.3. Georeferencing on SAR imagery
4. SAR Interferometry (InSAR)
4.1. Basic formulation
4.2. Coherence and noise sources
4.3. How to create an Elevation Map using InSAR
5. Differential Interferometry SAR (DInSAR)
5.1. Basic formulation
5.2. How to create a ground motion map with DInSAR
5.3. Characteristics of DInSARproducts
6.PSI Techniques (Persistent Scatterer Interferometry)
6.1. Basic concepts
6.2. PSI Processor Components
6.3. Scatterers
6.4. Examples of ground motion measurements with PSI
Competences
Analyse and exploit geographic data from different sources to generate new information from pre-existing data.
Communicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.
Continue the learning process, to a large extent autonomously.
Design and apply a methodology, based on the knowledge acquired, for studying a particular use case.
Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
Use different specialised GIS and remote sensing software, and other related software.
Use different techniques and concepts for generating useful information in spatial analysis.
Write up and publicly present work done individually or in a team in a scientific, professional context.
Learning Outcomes
Communicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.
Continue the learning process, to a large extent autonomously.
Design and apply a methodology, based on the knowledge acquired, for studying a particular use case.
Exploit geographic data through map algebra, layer combination, network analysis and other techniques, taking the right decisions for each problem area based on the knowledge acquired.
Identify the concepts associated with spatial analysis, their applications and their limitations.
Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
Show expertise in using the different tools of terrain analysis and interpolation.
Use the main applications for generating new information from GIS data.
Write up and publicly present work done individually or in a team in a scientific, professional context.
Presential
TRUE
Spatial analysis
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HaDEA. Neither the European Union nor the granting authority can be held responsible for them. The statements made herein do not necessarily have the consent or agreement of the ASTRAIOS Consortium. These represent the opinion and findings of the author(s).
