Lecture

Analysis of Geographic Information: GeoDa

Related lectures (32)

Spatial Dependence: Autocorrelation and Analysis

Introduces spatial dependence, the paradox of classical statistics, and the bias in linear regression.

Covers autocorrelation, spatial dependence, and modeling challenges in GIS, emphasizing the importance of historical data and statistical methods.

Spatial Autocorrelation: Moran's I

Explores Moran's I for assessing spatial clustering in geographical data using precipitation as an example.

Thematic attributes and classification

Covers statistical thematic mapping, types of thematic maps, and discretization methods for representing and classifying dataset characteristics.

Map Components and Functions

Covers the concept of cartography, thematic maps, and map components.

Components and Functions of Maps

Covers thematic cartography, visual variables, map production, and interactive mapping.

Interpolation: Spatial Phenomena and Methods

Covers global and local deterministic interpolation methods in geographic information systems, discussing expert knowledge, method selection, and uncertainty estimation.

Spatial Dependence: Concepts and Analysis

Explores spatial dependence, spatial autocorrelation, and bias in statistical analysis due to spatial relationships.

Thematic Attributes and Classification

Covers thematic attributes, classification, data representation, and discretization methods in geographic information systems.

Introduction: organization and requirements

Covers course organization, spatial statistics, epidemiological data analysis, and group project work.

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.