Limited knowledge exists of the terrain variables that have an influence on gopher tortoise (Gopherus polyphemus) burrow locations. Previous studies suggest that terrain features may play a role in preference of burrow location. LiDAR- (Light Detection and Ranging) derived terrain features can be evaluated through GIS (Geographic Information System) analysis at a fine spatial scale. LiDAR data acquired at 0.5 meter post spacing over three locations on Camp Shelby Joint Forces Training Center, MS were used to develop DTMs (Digital Terrain Models) for use in burrow site characterization. Terrain variables (e.g. elevation, slope, aspect) were developed from the LiDAR DTM in ArcGIS. Burrows and randomly allocated non-burrow points were used in logistic regression analysis to model the relationship between burrow occurrence and terrain features. Four models correctly classified more than 83% of the burrow locations. The R2 were 34.83%, 49.31%, 28.09%, and 31.51%.

The purpose of this research is to identify whether synoptic patterns and variables were statistically significantly different between East Coast United States track bomb and ordinary cyclogenesis. The differentiation of East Coast track bomb and ordinary cyclogenesis was completed through the utility of the principal component analysis, a K-means cluster analysis, a subjective composite analysis, and permutation tests. The principal component analysis determined that there were three leading modes of variability within the bomb and ordinary composites. The K-means cluster analysis was used to cluster these leading patterns of variability into three distinct clusters for the bomb and ordinary cyclones. The subjective composite analysis, created by averaging all the variables from each cyclone in each cluster, identified several synoptic variables and patterns to be objectively compared through permutation tests. The permutation tests revealed that synoptic variables and patterns associated with bomb cyclogenesis statistically significantly differ from ordinary cyclogenesis.

The first step towards developing targeted policies to boost political interest is to identify the main characteristics of individuals interested (and those who are not) in politics. This article aims at exploring the common factors shared by individuals with the same political interest. This is investigated through a cluster analysis, a rarely used technique in the field of political behavior, with data from the European Social Survey. The results are in line with the initial expectations and show that civic skills, represented by the educational background and the frequency of relational meetings, are determinants of political interest. Nonetheless, for those with the same civic skills, other socio-demographic, economic, and relational factors can diverge depending on the political interest. More in detail, the main attributes distinguishing individuals with the same level of civic skills but a different level of political interest are socio-demographic. In other words, among individuals with high civic skills, the main differences between those politically interested and those who are not are on the variables about age, gender and marital status. In addition, the research highlights the relevance of cluster analysis to obtain closer details on behavioral patterns than those obtained through regression analysis.

Poor diet, sedentary behaviors, sugar‐sweetened beverages (SSB) and alcohol intake seem to co‐exist in complex ways that are not well understood. The aim of this study was to provide an understanding of the extent to which unhealthy behaviors cluster in eight Latin America countries. A secondary aim was to identify socio‐demographic characteristics associated with these behaviors by country. Data from adolescents and adults from the “Latin American Health and Nutrition Study” was used and the prevalence of screen‐time, occupational and transportation–sedentary time, socializing with friends, poor diet, SSB and alcohol intake, alone and in combination, were identified. The eight Latin America (LA) countries added to analyses were: Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela. Logistic regression was used to estimate associations between ≥2 behaviors clustering, socio‐demographics and weight status. Among 9218 individuals, the most prevalent behaviors were transportation and occupation–sedentary time, SSB and alcohol intake. Younger, female, married/living with a partner, low and middle‐income and obese individuals had higher chances for these clustering behaviors. These results provide a multi-country level of understanding of the extent to which behaviors co‐occur in the LA population. / University of San Francisco / Revisión por pares

Purpose: We investigated the associations of early onset polysubstance use prior to age 18 with the prevalence of bronchitis among U.S. adults and tested whether the associations differ by gender. Methods: A total of 77,950 adults, of them 2,653 with bronchitis in the past year, were from the combined 2013 and 2014 National Survey on Drug Use and Health data. The variable cluster analysis was used to classify nine variables about substance use prior to age 18 (cigarettes, cigars, smokeless tobacco, marijuana, cocaine, heroin, methamphetamines, ecstasy, and phencyclidine). Weighted multivariate logistic regression analysis (MLR) was used to examine the associations with bronchitis. Results: Nine variables were divided into two clusters: early onset poly tobacco use (three tobacco use variables) and early onset poly drug use (six drug use variables). The overall prevalence of bronchitis was 3.8% (5.1% for females and 2.3% for males). MLR analysis showed that being female, elderly (ages 65 and above), obese, and early onset poly tobacco use were associated with increased odds of bronchitis (p < 0.05). Gender-stratified analyses showed that early-onset poly tobacco use was significantly associated with bronchitis only in males, whereas early onset poly drug use was associated with bronchitis only in females. Moreover, obesity and tobacco use in the past year revealed associations with bronchitis regardless of gender. Conclusions: Obesity, early onset poly tobacco use prior to age 18, and tobacco use in the past year were positively associated with bronchitis; furthermore, the associations of early onset polysubstance use with bronchitis differed by gender, which indicated that gender differences should be considered in developing effective prevention strategies.

Motivation and mathematics anxiety both impact student achievement in the early grades. However, few if any studies have examined the patterns of kindergarten students' motivation and mathematics anxiety and their associations with teachers’ practices and student demographics. In this study, I investigate both these issues. Specifically, using a diverse sample of 355 students from 24 kindergarten classrooms, I examined (a) whether there were distinct profiles of motivation and mathematics anxiety among students and (b) the associations between student profiles and teachers’ practices. Using cluster analysis, I identified three student profiles. The largest student profile (52.4%) demonstrated a positive pattern of High Motivation and Low Anxiety; whereas two other profiles had smaller numbers of students reporting negative outcomes: Low Motivation and Moderate Anxiety (32.3%) and Moderate Motivation and High Anxiety (15.3%). Student profiles were associated with students’ socioeconomic status and race. Students of distinct profiles differed significantly on their reports of mathematics motivation, mathematics anxiety, and performance on mathematics achievement – measured by standardized tests. In addition, teachers’ ratings of student motivation for mathematics were significantly different across student profiles.<br><div>To investigate the associations between teacher practices and student outcomes, I purposively selected six teachers based on a combination of multiple criteria and rated their mathematics lessons by using the Classroom Assessment Scoring System (Pianta et al., 2008). Three teachers with high concentrations of students reporting positive outcomes had more positive ratings across the domains of Emotional Support, Classroom Organization, and Instructional Support, compared to the other three teachers with high percentages of students reporting negative outcomes. I compared descriptions of teacher practices between the two groups of teachers to illustrate associations between teacher practices and student outcomes. Implications for mathematics instruction in the early elementary grades are discussed.<br></div>

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In a diverse community sample of mothers (N = 108) and their preschool‐aged children (Mage = 3.50 years), this study conducted person‐oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self‐report indicators. A model‐based cluster analysis was applied to five indicators of maternal ER: maternal self‐report, observed negative affect in a parent–child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model‐based cluster analyses revealed four maternal ER profiles, including a group of mothers with average ER functioning, characterized by socioeconomic advantage and more positive parenting behavior. A dysregulated cluster demonstrated the greatest challenges with parenting and dyadic interactions. Two clusters of intermediate dysregulation were also identified. Implications for assessment and applications to parenting interventions are discussed.

Buildings and the built environment account for a significant portion of the global energy use and greenhouse gas emissions, and reducing the energy demand in this sector is crucial for a sustainable energy transition. This highlights the need for accurate and large-scale estimations and predictions of the future energy demand in buildings. Urban building energy modeling (UBEM) is an analytical tool for precise and high-quality energy modelling of city-scale building stocks, which is growing in interest as a useful tool for researchers and decision-makers worldwide. This thesis contributes to the understanding and future development in the field of UBEM and multi-variate cluster analysis. Based on a review of contemporary literature, possible improvements and knowledge gaps regarding UBEM are identified. The majority of UBEM studies are developed for similar applications, and some challenges are close to universal. Difficulties in data acquisition and the identification and characterisation of building archetypes are frequently addressed. Drawing on conclusions from the review, a clustering methodology for identifying building archetypes for hybrid UBEM was developed. The methodology utilised the k-means cluster analysis algorithm for multiple diverse parameters, including socio-economic indicators, and is based on open data sets which eliminates data acquisition issues and allows for easy adaptation. Building archetypes were successfully identified for two large data sets, and proved to be representative of the sample building stock. The results of the analysis also show that the error metric values diverge after a certain number of clusters, for multiple runs of the algorithm. This property of the algorithm in combination with the use of both existing and novel error metrics provide a reliable method for determining the optimal number of clusters. The methodology developed in this thesis enables for an improved modelling process, as a part of a complete UBEM.