{"title":"GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data","authors":"Luc Anselin, Xun Li, Julia Koschinsky","doi":"10.1111/gean.12311","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"54 3","pages":"439-466"},"PeriodicalIF":3.3000,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geographical Analysis","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/gean.12311","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 15
Abstract
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.
期刊介绍：
First in its specialty area and one of the most frequently cited publications in geography, Geographical Analysis has, since 1969, presented significant advances in geographical theory, model building, and quantitative methods to geographers and scholars in a wide spectrum of related fields. Traditionally, mathematical and nonmathematical articulations of geographical theory, and statements and discussions of the analytic paradigm are published in the journal. Spatial data analyses and spatial econometrics and statistics are strongly represented.
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