Stéphane Dray|Thibaut Jombart
Revisiting Guerry’s data: Introducing spatial constraints in multivariate analysis
Biometry and Evolutionary Biology Laboratory
standard multivariate analysis methods aim identify summarize main structures large data sets containing description number observations several variablesin many cases spatial information also available observation map associated multivariate data settwo main objectives relevant analysis spatial multivariate data summarizing covariation structures identifying spatial patternsin practice achieving goals simultaneously statistical challenge range methods developed offer tradeoffs two objectivesin applied context methodological question remains major issue community ecology species assemblages ie covariation species abundances often driven spatial processes thus exhibit spatial patternsin paper review variety methods developed community ecology investigate multivariate spatial patternswe present different ways incorporating spatial constraints multivariate analysis illustrate different approaches using famous data set moral statistics france published andrmichel guerry 1833we discuss compare properties different approaches practical theoretical viewpoint introductiona recent study friendly 2007 revived andrmichel guerrys 1833 essai sur la statistique morale de la franceguerry gathered data crimes suicide literacy moral statistics various dpartements ie counties francehe provided first real social data analysis using graphics maps summarize georeferenced multivariate data setthe work friendly 2007 contained historical part describing guerrys life work detailin second part friendly reanalyzed guerrys data using variety modern tools multivariate spatial analysishe considered two main approaches analyzing data set involving multivariate geographical aspects datacentric multivariate analysis mapcentric multivariate mapping displaysin first approach multivariate structure first summarized using standard analysis methods eg principal component analysis hotelling 1933 visualization methods eg biplot gabriel 1971the geographic information added posteriori graphs using colors visual attributes