GeoDa

GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling. OpenGeoDa is the cross-platform, open source version of Legacy GeoDa. While Legacy GeoDa only runs on Windows XP, OpenGeoDa runs on different versions of Windows (including XP, Vista, 7, 8 and 10), Mac OS, and Linux. The package was initially developed by the Spatial Analysis Laboratory of the University of Illinois at Urbana-Champaign under the direction of Luc Anselin. From 2016 development continues at the Center for Spatial Data Science (CSDS) at the University of Chicago. GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling. OpenGeoDa is the cross-platform, open source version of Legacy GeoDa. While Legacy GeoDa only runs on Windows XP, OpenGeoDa runs on different versions of Windows (including XP, Vista, 7, 8 and 10), Mac OS, and Linux. The package was initially developed by the Spatial Analysis Laboratory of the University of Illinois at Urbana-Champaign under the direction of Luc Anselin. From 2016 development continues at the Center for Spatial Data Science (CSDS) at the University of Chicago. GeoDa has powerful capabilities to perform spatial analysis, multivariate exploratory data analysis, and global and local spatial autocorrelation. It also performs basic linear regression. As for spatial models, both the spatial lag model and the spatial error model, both estimated by maximum likelihood, are included. OpenGeoDa is released under the GNU GPL version 3.0. GeoDa replaced what was previously called DynESDA, a module that worked under the old ArcView 3.x to perform exploratory spatial data analysis (or ESDA). Current releases of GeoDa no longer depend on the presence of ArcView or other GIS packages on a system. Projects in GeoDa basically consist of a shapefile that defines the lattice data, and an attribute table in a .dbf format. The attribute table can be edited inside GeoDa. The package is specialized in exploratory data analysis and geo-visualization, where it exploits techniques for dynamic linking and brushing. This means that when the user has multiple views or windows in a project, selecting an object in one of them will highlight the same object in all other windows. GeoDa also is capable of producing histograms, box plots, Scatter plots to conduct simple exploratory analyses of the data. The most important thing, however, is the capability of mapping and linking those statistical devices with the spatial distribution of the phenomenon that the users are studying. Dynamic linking and brushing are powerful devices as they allow users to interactively discover or confirm suspected patterns of spatial arrangement of the data or otherwise discard the existence of those. It allows users to extract information from data in spatial arrangements that may otherwise require very heavy computer routines to process the numbers and yield useful statistical results. The latter may also cost the users quite a bit in terms of expert knowledge and software capabilities. A very interesting device available in GeoDa to explore global patterns of autocorrelation in space is Anselin's Moran scatterplot. This graph depicts a standardized variable in the x-axis versus the spatial lag of that standardized variable. The spatial lag is nothing but a summary of the effects of the neighboring spatial units. That summary is obtained by means of a spatial weights matrix, which can take various forms, but a very commonly used is the contiguity matrix. The contiguity matrix is an array that has a value of one in the position (i, j) whenever the spatial unit j is contiguous to the unit i. For convenience that matrix is standardized in such a way that the rows sum to one by dividing each value by the row sum of the original matrix.