GeoDa Project Page

GeoDa is a free, open source, cross-platform software program that serves as an introduction to spatial data analysis. It runs on different versions of Windows (including XP, Vista, 7 and 8), Mac OS, and Linux.

Live Update of GeoDa Users Across the Globe

GeoDa is the flagship program of the GeoDa Center, following a long line of software tools developed by Dr. Luc Anselin. It is designed to implement techniques for exploratory spatial data analysis (ESDA) on lattice data (points and polygons). The free program provides a user friendly and graphical interface to methods of descriptive spatial data analysis, such as spatial autocorrelation statistics, as well as basic spatial regression functionality. The latest version contains several new features such as full space-time data support in all views, a new cartogram, a refined map movie, parallel coordinate plot, 3D visualization, conditional plots (and maps) and spatial regression.

Since its initial release in February 2003, GeoDa's user numbers have increased exponentially, as the chart and map of global users above shows. 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," an "exciting development" and more.

View motion charts comparing the use of GeoDa by country and by group over time with the use of internet, GDP per capita, and IDI score - an index designed by the International Telecommunications Union of the United Nations for measuring degrees of technological development. Data for the United States and China have been removed in some of the charts due to the disproportionate number of users in these countries.


The development of GeoDa and related materials has been primarily supported by the U.S. National Science Foundation/ the Center for Spatially Integrated Social Science (CSISS) (Grant BCS-9978058).

Reference: Anselin, L., I. Syabri and Y Kho. (2005). GeoDa : An Introduction to Spatial Data Analysis. Geographical Analysis 38(1), 5-22. (This was the most cited and most read GA article in 2006.)