Browsing by Subject "cluster analysis"

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    Combining Multivariate Statistical Methods and Spatial Analysis to Characterize Water Quality Conditions in the White River Basin, Indiana, U.S.A.
    (2011-02-25) Gamble, Andrew Stephan; Babbar-Sebens, Meghna; Tedesco, Lenore P.; Peng, Hanxiang
    This research performs a comparative study of techniques for combining spatial data and multivariate statistical methods for characterizing water quality conditions in a river basin. The study has been performed on the White River basin in central Indiana, and uses sixteen physical and chemical water quality parameters collected from 44 different monitoring sites, along with various spatial data related to land use – land cover, soil characteristics, terrain characteristics, eco-regions, etc. Various parameters related to the spatial data were analyzed using ArcHydro tools and were included in the multivariate analysis methods for the purpose of creating classification equations that relate spatial and spatio-temporal attributes of the watershed to water quality data at monitoring stations. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of Kohonen Self-Organizing Maps, Cluster Analysis, and Support Vector Machines. The final models were tested with recent and independent data collected from stations in the Eagle Creek watershed, within the White River basin. In 6 out of 20 models the Support Vector Machine more accurately classified the Eagle Creek stations, and in 2 out of 20 models the Linear Discriminant Analysis model achieved better results. Neither the linear or non-linear models had an apparent advantage for the remaining 12 models. This research provides an insight into the variability and uncertainty in the interpretation of the various statistical estimates and statistical models, when water quality monitoring data is combined with spatial data for characterizing general spatial and spatio-temporal trends.
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    The Influence of Trust and Attitudes on the Purchase Frequency of Organic Produce
    (Taylor & Francis, 2017) Dumortier, Jerome; Evans, Keith S.; Grebitus, Carola; Martin, Pamela A.; School of Public and Environmental Affairs
    Growth in organic food sales is mainly due to consumers becoming more aware of health issues and environmental concerns. Understanding the drivers of organic consumption is crucial to predict future market outcomes. In this analysis, the authors expand previous research by including general and institutional trust variables in addition to consumer attitudes to examine organic food purchases. Food production is unobservable and hence, consumers need to exhibit trust with respect to organic production and certification. A bivariate ordered probit model applied to U.S. survey data confirms that organic purchases are determined by health, nutrition, and taste. In some cases, general trust and trust in media are statistically significant. Trust in institutions that are involved in the organic certification process is not statistically significant. A hierarchical cluster analysis grouping consumers based on trust and attitudes shows that (dis)trust in the organic certification and supply chain does not hinder organic food market growth.