Geospatial Data Analysis: GeoDa Software

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Related lectures (32)

Components and Functions of Maps

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

Map Components and Functions

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

Graph Mining: Social Networks and Data Analysis

Explores graph mining in social networks, covering data analysis and community detection.

Data Science Fundamentals

Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.

Digitization of Vector Objects

Covers the digitization of vector objects in GIS, including creation, editing, and topology management.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Thematic Attributes and Classification

Covers thematic attributes, classification, and data discretization in thematic cartography using QGIS for proportional symbol mapping.

Graphical Analysis of Full Factorials

Explores the graphical analysis of full factorial experiments, emphasizing efficient data analysis and interpretation of main effects and interactions.

Thematic Attributes and Classification

Covers statistical thematic mapping, types of maps, discretization methods, and proportional symbols in maps.

John Snow Spatial Investigations

Explores John Snow's spatial investigations of cholera outbreaks, challenging prevailing beliefs and revolutionizing epidemiology.

Spatial Autocorrelation: Moran's I

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

Statistics: Exploratory Data Analysis

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

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