GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data

Since its introduction more than 15 years ago, the GeoDa software for the exploration of spatial data has transitioned from a closed source Windows-only solution to an open source and cross-platform product that takes on the look and feel of the native operating system. This article reports on the evolution in the functionality and architecture of the software and pays particular attention to its new implementation as a library, libgeoda. This library, through a clearly structured API, can be integrated into other software environments, such as R (rgeoda) and Python (pygeoda). This integration is illustrated with two small empirical examples, investigating local clusters in a historical London cholera data set and among socioeconomic determinants of health in Chicago. A timing experiment demonstrates the competitive performance of GeoDa desktop, libgeoda (C++), rgeoda and pygeoda compared to established solutions in R spdep and Python PySAL, evaluating conditional permutation inference for the Local Moran statistic.

Automated summary

The GeoDa software has evolved from a closed-source Windows-only solution to an open-source cross-platform product as a library (libgeoda) which can be integrated into other software environments. Two empirical examples were used to investigate local clustering and socioeconomic health determinants, while a timing experiment demonstrated competitive performance compared to established solutions in R and Python.