Showing papers by "John M. Kovacs published in 2007"
TL;DR: In this paper, the authors looked for dynamics zones based on empirical orthogonal function analysis (EOF) to define the average conditions for summer and winter spring tides and showed that in general and during summer-spring tides, higher Chla concentrations are localized in the west coast, with a displacement to the south.
Abstract: The Great Islands zone, in the Gulf of California, presents high phytoplankton concentration as a result of the high Turbulent Kinetic Energy (TKE). In this work we looked for dynamics zones based on Empirical Orthogonal Function analysis (EOF). The input data were Sea Surface Temperature (SST) and Chlorophyll-a concentration (Chla) from daily MODIS-AQUA at 1 Km from 2003 to 2006. Time series were generated to define the average conditions for summer and winter spring tides. Results showed that in general and during summer-spring tides, higher Chla concentrations are localized in the west coast, with a displacement to the south. These high Chla were associated with tidal mixing. Zero EOF values in summer showed the boundary between low SST and high Chla. During winter-spring tides there were more spatial variability than during summer time. Zero EOF value in winter time showed low SST and Chla in the west coast due to stronger mixing conditions that stay longer. Results of this work emphasize that a dynamic regionalization can be used in high TKE areas and it helps to define zones with a similar response based on the input parameters chosen.
7 citations
TL;DR: This paper used negative binomial regression model to predict the spatial distribution of enrollments at East Tennessee State University (ETSU) pharmacy school and found that the most important indicators of enrollment volume for the ETSU pharmacy school were Euclidean distance, probability, driving distance between schools and home and tuition costs.
Abstract: Having the ability to predict enrollment is an important task for any school’s recruiting team. The purpose of this study was to identify significant factors that can be used to predict the spatial distribution of enrollments. As a case study, we used East Tennessee State University (ETSU) pharmacy school, a regional pharmacy school located in the Appalachian Mountains. Through the application of a negative binomial regression model, we found that the most important indicators of enrollment volume for the ETSU pharmacy school were Euclidean distance, probability (based on competing pharmacy schools’ prestige, driving distance between schools and home and tuition costs), and the natural barrier of the Appalachian Mountains. Using these factors, together with other control variables, we successfully predicted the spatial distribution of enrollments for ETSU pharmacy school. Interestingly, gender also surfaced as a variable for predicting the pharmacy school’s enrollment. We found female students are more sensitive to the geographic proximity of home to school.
4 citations