Fortin2009392

Référence

Fortin, M.-J., Dale, M.R.T. (2009) Spatial autocorrelation in ecological studies: A legacy of solutions and myths. Geographical Analysis, 41(4):392-397. (Scopus )

Résumé

A major aim of including the spatial component in ecological studies is to characterize the nature and intensity of spatial relationships between organisms and their environment. The growing awareness by ecologists of the importance of including spatial structure in ecological studies (for hypothesis development, experimental design, statistical analyses, and spatial modeling) is beneficial because it promotes more effective research. Unfortunately, as more researchers perform spatial analysis, some misconceptions about the virtues of spatial statistics have been carried through the process and years. Some of these statistical concepts and challenges were already presented by Cliff and Ord in 1969. Here, we classify the most common misconceptions about spatial autocorrelation into three categories of challenges: (1) those that have no solutions, (2) those where solutions exist but are not well known, and (3) those where solutions have been proposed but are incorrect. We conclude in stressing where new research is needed to address these challenges. © 2009 The Ohio State University.

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@ARTICLE { Fortin2009392,
    AUTHOR = { Fortin, M.-J. and Dale, M.R.T. },
    TITLE = { Spatial autocorrelation in ecological studies: A legacy of solutions and myths },
    JOURNAL = { Geographical Analysis },
    YEAR = { 2009 },
    VOLUME = { 41 },
    NUMBER = { 4 },
    PAGES = { 392-397 },
    NOTE = { cited By 29 },
    ABSTRACT = { A major aim of including the spatial component in ecological studies is to characterize the nature and intensity of spatial relationships between organisms and their environment. The growing awareness by ecologists of the importance of including spatial structure in ecological studies (for hypothesis development, experimental design, statistical analyses, and spatial modeling) is beneficial because it promotes more effective research. Unfortunately, as more researchers perform spatial analysis, some misconceptions about the virtues of spatial statistics have been carried through the process and years. Some of these statistical concepts and challenges were already presented by Cliff and Ord in 1969. Here, we classify the most common misconceptions about spatial autocorrelation into three categories of challenges: (1) those that have no solutions, (2) those where solutions exist but are not well known, and (3) those where solutions have been proposed but are incorrect. We conclude in stressing where new research is needed to address these challenges. © 2009 The Ohio State University. },
    AFFILIATION = { Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada; Ecosystem Science and Management Program, University of Northern British Columbia, Prince George, BC, Canada },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/j.1538-4632.2009.00766.x },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-73649099501&doi=10.1111%2fj.1538-4632.2009.00766.x&partnerID=40&md5=5e03e3c48a39f43ecf6a331c6f5ff812 },
}

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