Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data or transcriptomics data. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. The most fundamental of these is the problem of defining the spatial location of the entities being studied. Classification of the techniques of spatial analysis is difficult because of the large number of different fields of research involved, the different fundamental approaches which can be chosen, and the many forms the data can take. (Wikipedia).
10b Data Analytics: Spatial Continuity
Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.
From playlist Data Analytics and Geostatistics
21b Spatial Data Analytics: Dispersion Variance
Subsurface modeling course lecture on dispersion variance.
From playlist Spatial Data Analytics and Modeling
01d Spatial Data Analytics: Modeling Strategies
A lecture on spatial, subsurface modeling strategies and workflows.
From playlist Spatial Data Analytics and Modeling
22 Spatial Data Analytics: Decision Making
Spatial data analytics course lecture on optimum decision making in the presence of uncertainty.
From playlist Spatial Data Analytics and Modeling
An introduction to the idea of Dimensional Analysis
From playlist Mathematical Physics I Uploads
01b Spatial Data Analytics: Subsurface Data
Lecture of the data available for subsurface modeling.
From playlist Spatial Data Analytics and Modeling
10c Data Analytics: Variogram Introduction
Lecture on the variogram as a measure to quantify spatial continuity.
From playlist Data Analytics and Geostatistics
01 Spatial Data Analytics: Subsurface Modeling
Lecture discussing the concept of subsurface modeling, integrating information sources, quantification over volume and properties of interest for decision support.
From playlist Spatial Data Analytics and Modeling
21 Spatial Data Analytics: Spatial Scale
Subsurface modeling course lecture on scale.
From playlist Spatial Data Analytics and Modeling
Lecture 21 (CEM) -- RCWA Tips and Tricks
Having been through the formulation and implementation of RCWA in previous lectures, this lecture discussed several miscellaneous topics including modeling 1D gratings with 3D RCWA, formulation of a 2D RCWA that incorporates fast Fourier factorization, RCWA for curved structures, truncatin
From playlist UT El Paso: CEM Lectures | CosmoLearning.org Electrical Engineering
Value of Information in the Earth Sciences
Overview, narrated by Tapan Mukerji Eidsvik, J., Mukerji, T. and Bhattacharjya, D., 2015. Value of information in the earth sciences: Integrating spatial modeling and decision analysis. Cambridge University Press.
From playlist Uncertainty Quantification
Overview of various methods for sensitivity analysis in the UQ of subsurface systems
From playlist Uncertainty Quantification
GED for spatial filtering and dimensionality reduction
Generalized eigendecomposition is a powerful method of spatial filtering in order to extract components from the data. You'll learn the theory, motivations, and see a few examples. Also discussed is the dangers of overfitting noise and few ways to avoid it. The video uses files you can do
From playlist OLD ANTS #9) Matrix analysis
Vince Calhoun - Maximizing Information in neuroimaging: approaches for analysis and visualization
Recorded 13 January 2023. Vince Calhoun of the Georgia Institute of Technology presents "Maximizing Information in neuroimaging analysis: flexible approaches for analysis and visualization" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Learn more online at: ht
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights
DIRECT 2021 08 Spatial Statistics for Modeling
DIRECT Consortium at The University of Texas at Austin, working on novel methods and workflows in spatial, subsurface data analytics, geostatistics and machine learning. This is Spatial Statistics for Characterization and Modeling Subsurface Fractures by Mahmood Shakiba, supported by th
From playlist DIRECT Consortium, The University of Texas at Austin
Introduction to direct forecasting to solve UQ problems
From playlist QUSS GS 260
ShmooCon 2014: A Critical Review of Spatial Analysis
For more information visit: http://bit.ly/shmooc14 To download the video visit: http://bit.ly/shmooc14_down Playlist Shmoocon 2014: http://bit.ly/shmooc14_pl Speakers: David Giametta | Andrew Potter Spatial Analysis is a recently proposed idea of using static analysis based byte sequence
From playlist ShmooCon 2014
This talk will specifically cover the challenges we encountered programming in the PL/Python environment, collaborations with some of the PySAL developers, and the power of having spatial statistics and machine learning capabilities baked right into a cloud database. These libraries, paire
From playlist 2016
James Thorson - Forecasting non-local climate impacts for mobile marine species using extensions...
Dr James Thorson (National Oceanic and Atmospheric Administration) presents "Forecasting non-local climate impacts for mobile marine species using extensions to empirical orthogonal function analysis", 8 May 2020.
From playlist Statistics Across Campuses
01c Spatial Data Analytics: Modeling Goals
A lecture on subsurface modeling goals.
From playlist Spatial Data Analytics and Modeling