Spatial analysis

Spatial analysis

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).

Spatial analysis
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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

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21b Spatial Data Analytics: Dispersion Variance

Subsurface modeling course lecture on dispersion variance.

From playlist Spatial Data Analytics and Modeling

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01d Spatial Data Analytics: Modeling Strategies

A lecture on spatial, subsurface modeling strategies and workflows.

From playlist Spatial Data Analytics and Modeling

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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

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Dimensional Analysis Intro

An introduction to the idea of Dimensional Analysis

From playlist Mathematical Physics I Uploads

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01b Spatial Data Analytics: Subsurface Data

Lecture of the data available for subsurface modeling.

From playlist Spatial Data Analytics and Modeling

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10c Data Analytics: Variogram Introduction

Lecture on the variogram as a measure to quantify spatial continuity.

From playlist Data Analytics and Geostatistics

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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

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21 Spatial Data Analytics: Spatial Scale

Subsurface modeling course lecture on scale.

From playlist Spatial Data Analytics and Modeling

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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

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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

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Sensitivity Analysis

Overview of various methods for sensitivity analysis in the UQ of subsurface systems

From playlist Uncertainty Quantification

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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

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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

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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

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05-4 Inverse modeling DF

Introduction to direct forecasting to solve UQ problems

From playlist QUSS GS 260

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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

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Analysis in CartoDB

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

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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

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01c Spatial Data Analytics: Modeling Goals

A lecture on subsurface modeling goals.

From playlist Spatial Data Analytics and Modeling

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