Analysis of Geographic Information: GeoDa

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Spatial Autocorrelation: An Introduction to GIS

Covers spatial autocorrelation in GIS, including global autocorrelation, weighting schemes, Moran's I, and contiguity ratio.

Visual Variables

Introduces visual variables in thematic maps and the semiology of graphics.

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Components and Functions of the Map

Covers the components and functions of thematic mapping, including visual variables, map production, and interactive mapping.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Linear Regression: Basics and Applications

Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.

Linear Regression: Foundations and Applications

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Spatial regression or spatial lag model

Explores spatial regression models, addressing spatial autocorrelation challenges and the concept of spatial lag models to correct biases and improve inference accuracy.

Derived variables: thematic applications

Explores thematic variables derived from digital altitude models for various applications.

Spatial Autocorrelation and Dependence

Covers spatial autocorrelation, dependence in GIS, and biases in classical statistics.