A Five-Star Guide for Achieving Replicability and Reproducibility When Working with GIS Software and Algorithms

Datos Bibliográficos

ID3174059
AutoresJames P Wilson (0000-0001-5969-0729, University of Southern California), Katharine Butler (0000-0001-8965-2109, Environmental Systems Research Institute (United States)), Song Gao (0000-0003-4359-6302, University of Wisconsin–Madison), Yingjie Hu (0000-0002-5515-4125, University at Buffalo, State University of New York), Wenwen Li (0000-0003-2237-9499, Arizona State University), Dawn J Wright (0000-0002-2997-7611, Environmental Systems Research Institute (United States))
Año2021
Volumen111
Número5
Páginas1311-1317
Fecha de publicación2021-07-29
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaAnnals of the American Association of Geographers (JOURNAL)
Identificadores de la revistaISSN: 2469-4452 • E-ISSN: 2469-4460
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/24694452.2020.1806026
OpenAlexW3094386498
IdiomaEN
Citas recibidas17
Referencias citadas21

The availability and use of geographic information technologies and data for describing the patterns and processes operating on or near the Earth’s surface have grown substantially during the past fifty years. The number of geographic information systems software packages and algorithms has also grown quickly during this period, fueled by rapid advances in computing and the explosive growth in the availability of digital data describing specific phenomena. Geographic information scientists therefore increasingly find themselves choosing between multiple software suites and algorithms to execute specific analysis, modeling, and visualization tasks in environmental applications today. This is a major challenge because it is often difficult to assess the efficacy of the candidate software platforms and algorithms when used in specific applications and study areas, which often generate different results. The subtleties and issues that characterize the field of geomorphometry are used here to document the need for (1) theoretically based software and algorithms; (2) new methods for the collection of provenance information about the data and code along with application context knowledge; and (3) new protocols for distributing this information and knowledge along with the data and code. This article discusses the progress and enduring challenges connected with these outcomes

Algorithm · Component-based software engineering · Data mining · Data science · Geographic information system · Software analytics · Software development · Software engineering · Spatial analysis · Computer Science · Distributed and Parallel Computing Systems · Research Data Management Practices · Scientific Computing and Data Management · Software