Use this command to install GeoDa:

winget install --id=GeoDa.GeoDa -e

GeoDa is a free and open source software tool that serves as an introduction to spatial data science. It is designed to facilitate new insights from data analysis by exploring and modeling spatial patterns. GeoDa was developed by Dr. Luc Anselin and his team. The program provides a user-friendly and graphical interface to methods of exploratory spatial data analysis (ESDA), such as spatial autocorrelation statistics for aggregate data (several thousand records), and basic spatial regression analysis for point and polygon data (tens of thousands of records). To work with big data in GeoDa it should first be aggregated to areal units. Since its initial release in February 2003, GeoDa's user numbers have increased exponentially to over 520,000 (June 2022). This includes lab users at universities such as Harvard, MIT, and Cornell. The user community and press embraced the program enthusiastically, calling it a "hugely important analytic tool," a "very fine piece of software," and an "exciting development."

GeoDa is a free and open-source software tool designed to introduce users to spatial data science and facilitate new insights from data analysis by exploring and modeling spatial patterns. The program provides a user-friendly graphical interface to methods of exploratory spatial data analysis (ESDA), including spatial autocorrelation statistics for aggregate data, basic spatial regression analysis, and support for big data through aggregation to areal units.

Key Features:

Exploratory Spatial Data Analysis: GeoDa enables users to analyze spatial patterns using techniques like spatial autocorrelation, local Moran's I, and Geary's c.
Multi-Layer Support: Users can now load additional layers for visualization purposes, enhancing the ability to explore complex spatial relationships.
Linked Maps and Charts: The software allows users to visualize statistical results through linked maps and charts, facilitating a deeper understanding of spatial patterns.
Time Series Analysis: GeoDa supports time series analysis with features like the Time Editor and Time Player, enabling exploration of changes over space and time.
Cluster Detection: The program includes advanced cluster detection techniques, such as local join count maps for categorical data, skater, redcap, max-p, and spectral clustering.
Non-Spatial Clustering: GeoDa implements classic non-spatial clustering methods like k-means, hierarchical clustering, and principal component analysis.

Ideal for researchers, students, and professionals in fields such as geography, urban planning, public health, and social sciences, GeoDa empowers users to analyze spatial data effectively. It helps users uncover hidden patterns, test hypotheses about spatial relationships, and make informed decisions based on robust statistical analyses. GeoDa can be installed via winget, making it accessible to a wide audience.

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