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 and 7), 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. Development continues at the GeoDa Center for Geospatial Analysis and Computation at Arizona State University. 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

References in zbMATH (referenced in 19 articles )

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  1. Blier-Wong, Christopher; Cossette, Hélène; Lamontagne, Luc; Marceau, Etienne: Geographic ratemaking with spatial embeddings (2022)
  2. Chen, Xia; Liu, Jianmin: Fiscal decentralization and environmental pollution: a spatial analysis (2020)
  3. Ganteda, Charan Kumar; Shobhalatha, Gurram: Analysis of water quality by using spatial graph theory and metamodelling (2020)
  4. Owusu Junior, Peterson; Alagidede, Imhotep: Risks in emerging markets equities: time-varying versus spatial risk analysis (2020)
  5. Stefanie Lumnitz; Dani Arribas-Bel Renan Xavier Cortes; James Gaboardi; Verena Griess; Wei Kang; Taylor Oshan; Levi John Wolf; Sergio Rey: splot - visual analytics for spatial statistics (2020) not zbMATH
  6. Mihai Tivadar: OasisR: An R Package to Bring Some Order to the World of Segregation Measurement (2019) not zbMATH
  7. Bivand, Roger S.; Wong, David W. S.: Comparing implementations of global and local indicators of spatial association (2018)
  8. Rampaso, Renato Couto; De Souza, Aparecida Doniseti Pires; Flores, Edilson Ferreira: Bayesian analysis of spatial data using different variance and neighbourhood structures (2016)
  9. Kang, Su Yun; McGree, James; Baade, Peter; Mengersen, Kerrie: A case study for modelling cancer incidence using Bayesian spatio-temporal models (2015)
  10. Kang, Su Yun; Mcgree, James; Baade, Peter; Mengersen, Kerrie: An investigation of the impact of various geographical scales for the specification of spatial dependence (2014)
  11. Bivand, Roger S.; Pebesma, Edzer J.; Gómez-Rubio, Virgilio: Applied spatial data analysis with R (2013)
  12. Thibault Laurent; Anne Ruiz-Gazen; Christine Thomas-Agnan: GeoXp: An R Package for Exploratory Spatial Data Analysis (2012) not zbMATH
  13. Wheeler, David C.; Hickson, Demarc A.; Waller, Lance A.: Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion (2010)
  14. Julian, James T.; Young, John A.; Jones, John W.; Snyder, Craig D.; Wright, C. Wayne: The use of local indicators of spatial association to improve lidar-derived predictions of potential amphibian breeding ponds (2009) ioport
  15. Rey, Sergio J.: Show me the code: spatial analysis and open source (2009) ioport
  16. Vandersmissen, Marie-Hélène; Séguin, Anne-Marie; Thériault, Marius; Claramunt, Christophe: Modeling propensity to move after job change using event history analysis and temporal GIS (2009) ioport
  17. Buliung, Ron N.; Remmel, Tarmo K.: Open source, spatial analysis, and activity-travel behaviour research: capabilities of the \textitaspacepackage (2008) ioport
  18. Zhang, Tonglin; Lin, Ge: Identification of local clusters for count data: a model-based Moran’s (I) test (2008)
  19. Arbia, Giuseppe: Spatial econometrics. Statistical foundations and applications to regional convergence. (2006)