Assessing spatial predictive models in the environmental sciences: Accuracy measures, data variation and variance explained. (June 2016)

• Record Type: Journal Article
• Authors: Li, Jin
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 1
• Page End: 8
• Publication Date: 2016-06
• Subjects: Predictive accuracy -- Error measure -- Data variance -- Model assessment -- Spatial interpolation methods -- Spatial predictions; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A comprehensive assessment of the performance of predictive models is necessary as they have been increasingly employed to generate spatial predictions for environmental management and conservation and their accuracy is crucial to evidence-informed decision making and policy. In this study, we clarified relevant issues associated with variance explained ( VEcv ) by predictive models, established the relationships between VEcv and commonly used accuracy measures and unified these measures under VEcv that is independent of unit/scale and data variation. We quantified the relationships between these measures and data variation and found about 65% compared models and over 45% recommended models for generating spatial predictions explained no more than 50% data variance. We classified the predictive models based on VEcv, which provides a tool to directly compare the accuracy of predictive models for data with different unit/scale and variation and establishes a cross-disciplinary context and benchmark for assessing predictive models in future studies. Highlights: Established the relationships of VE CV with commonly used accuracy measures. Quantified the relationships of these measures with data variation. Objectively assessed predictive models based on VE CV in the environmental sciences. Provided a tool to assess predictive models for data of various unit and variation. Established a cross-disciplinary context/benchmark for assessing predictive models.