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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.

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Made available in DSpace on 2014-12-17T14:08:35Z (GMT). No. of bitstreams: 1 KleberCOCpdf.pdf: 2178209 bytes, checksum: 588b533d30c060af9cf941e7001d3372 (MD5) Previous issue date: 2009-04-22 / In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter ? (index of neighborhood). High ? shows more aggregated data, low ? indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using ? index we verify that random cluster data shows a distribution of ? that is lower than actual cluster ?. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means / Nos ?ltimos anos, o DFA introduzido por Peng, foi estabelecido como uma importante ferramenta capaz de detectar autocorrela??o de longo alcance em s?ries temporais com n?o-estacionaridade. Esta t?cnica vem sendo aplicado com sucesso a diversas ?reas tais como: Econofis?ca, Biof?sica, Medicina, F?sica e Climatologia. No presente trabalho, utilizamos a t?cnica do DFA para obter o expoente de Hurst (H) do perfil el?trico de densidade (RHOB) de 53 po?os provindos do Campo Escola de Namorado. Neste trabalho queremos saber se podemos, ou n?o, utilizar este expoente para caracterizar espacialmente o campo. Duas hip?teses surgem: Na primeira o conjunto dos H reflete a geologia local, po?os com mesmo H se encontram pertos, e ent?o se pode pensar em utilizar H em procedimentos geoestat?sticos espaciais. Na segunda hip?tese cada po?o tem seu H, a informa??o dos H de cada po?o est? descorrelacionada e o conjunto dos perfis mostra apenas flutua??es aleat?rias em H que n?o revelam qualquer estrutura espacial. A an?lise de agrupamentos ? um m?todo bastante utilizado na realiza??o de an?lises estat?sticas. Nesta disserta??o utilizamos o m?todo de agrupamento n?o hier?rquico chamado m?todo do k-m?dia. Com o objetivo de verificar se um conjunto de dados gerados pelo m?todo do k-m?dia, ou de forma aleat?ria, forma padr?es espaciais, criamos o par?metro ? (?ndice de vizinhan?a). Altos ? implicam em dados mais agregados, baixos ? em dados dispersos ou sem correla??o espacial. Com aux?lio deste ?ndice e do m?todo de Monte Carlo verificamos que os dados agrupados aleatoriamente apresentam uma distribui??o mais baixa de ? do que os obtidos dos dados concretos e agrupados pelo k-m?dia. Desta forma conclu?mos que os dados de H obtidos nos 53 po?os est?o agrupados e podem ser usados na caracteriza??o espacial de campos. A an?lise de curvas de n?vel confirmou o resultado do k-m?dia

The master's thesis deals with answer of distribution of countries, which was determined in 1965 in the theoretical concept by John Hajnal, in contemporary Europe. The main aim was to reanalyze the original division of countries using cluster analysis on the basis demographic indicators: average age at first marriage men and women, the average age of a woman at first child birth, the number of divorces per 100 marriages, the proportion of live births in marriage and out of marriage. The data used came from the Eurostat database from 1990 to 2015. Cluster analyzes of European countries were also performed according to the value orientations of their inhabitants in the area of social relations and life expectations. Respondents' statements came from the European Social Survey from 2002 to 2018. Cluster analysis of selected demographic indicators did not confirm two models of Hajnal's concept of marital behavior. Cluster analyzes of respondents' value orientations confirmed the existence of two value approaches to life priorities - a preference for traditionally accepted values and a preference for a dynamic and efficient lifestyle. Keywords Hajnal line, family, marriage, divorce rate, ESS research project, K-means cluster analysis, values

Origin-Destination (OD) matrix plays an important role in traffic management and urban planning. However, the OD estimation demands large data collection which has been done in past mostly by surveys with numerous limitations. With the development of communication technology and artificial intelligence technology, the transportation industry experiences new opportunities and challenges. Sensors bring big data characterized by 4V (Volume, Variety, Velocity, Value) to the transportation domain. This allows traffic practitioners to receive data covering large-scale areas and long time periods, even several years of data. At the same time, the introduction of artificial intelligence technology provides new opportunities and challenges in processing massive data. Advances from computer science have also brought revolutionary advancements in the field of transportation. All these new advances and technologies enable large data collection that can be used for extracting and estimating dynamic OD matrices for small time intervals and long time periods.Using Stockholm as the focus of the case study, this thesis estimates dynamic OD matrices covering data collected from the tolls located around Stockholm municipality. These dynamic OD matrices are used to analyze the day-to-day characteristics of the traffic flow that goes through Stockholm. In other words, the typical day-types of traffic through the city center are identified and studied in this work. This study analyzes the data collected by 58 sensors around Stockholm containing nearly 100 million vehicle observations (12GB).Furthermore, we consider and study the effects of dimensionality reduction on the revealing of most common day-types by clustering. The considered dimensionality reduction techniques are Principal Component Analysis (PCA) and its variant Kernel PCA (KPCA). The results reveal that dimensionality reduction significantly drops computational costs while resulting in reasonable day-types. Day-type clusters reveal expected as unexpected patterns and thus could have potential in traffic management, urban planning, and designing the strategy for congestion tax. / Origin-Destination (OD) -matrisen spelar en viktig roll i trafikledning och stadsplanering. Emellertid kräver OD-uppskattningen stor datainsamling, vilket har gjorts tidigare mest genom enkäter med många begränsningar. Med utvecklingen av kommunikationsteknik och artificiell intelligens upplever transportindustrin nya möjligheter och utmaningar. Sensorer ger stor data som kännetecknas av 4V (på engelska, volym, variation, hastighet, värde) till transportdomänen. Detta gör det möjligt för trafikutövare att ta emot data som täcker storskaliga områden och långa tidsperioder, till och med flera års data. Samtidigt ger introduktionen av artificiell intelligens teknik nya möjligheter och utmaningar i behandlingen av massiva data. Datavetenskapens framsteg har också lett till revolutionära framsteg inom transportområdet. Alla dessa nya framsteg och tekniker möjliggör stor datainsamling som kan användas för att extrahera och uppskatta dynamiska OD-matriser under små tidsintervall och långa tidsperioder.Genom att använda Stockholm som fokus för fallstudien uppskattar denna avhandling dynamiska OD-matriser som täcker data som samlats in från vägtullarna runt Stockholms kommun. Dessa dynamiska OD-matriser används för att analysera de dagliga egenskaperna hos trafikflödet i Stockholm genom stadens centrum. Med andra ord känns igen och studeras de typiska dagtyperna av trafik genom stadens centrum i detta arbete. Denna studie analyserar data som samlats in av 58 sensorer runt Stockholm som innehåller nästan 100 miljoner fordonsobservationer (12 GB)Dessutom överväger och studerar vi effekterna av dimensioneringsreduktion på avslöjandet av de vanligaste dagtyperna genom kluster. De betraktade dimensioneringsreduktionsteknikerna är Principal Component Analysis (PCA) och dess variant Kernel PCA (KPCA). Resultaten avslöjar att dimensioneringsreduktion avsevärt minskar beräkningskostnaderna, samtidigt som det ger rimliga dagtyper. Dagstyp kluster avslöjar förväntade som oväntade mönster och därmed kan ha potential i trafikledning, stadsplanering och utformning av strategin för trängselskatt.

With natural disasters occurring with more frequency and severity, understanding how to facilitate survivors’ resilience and recovery is becoming increasingly important. The Camp Fire in California, which started on November 8, 2018, was one of the most destructive wildfires in recorded history in terms of loss of life and damage to property. Aid from many types of entities (e.g., non-profits, governments, and for-profits) at various levels (e.g., local, state, and federal) was available to survivors, but perhaps the most influential source of support was Disaster Facebook Groups. In the month after the Camp Fire, around 50 Camp Fire Facebook Groups (CFFGs) were created, with over 100 CFFGs existing over the course of recovery. CFFGs are Facebook Groups with the goal of helping Camp Fire survivors. The support exchanged in CFFGs was immense and ranged from financial assistance to emotional support to community building. <br><br>This dissertation offers a mixed-method, event-specific case study of the use and utility of Disaster Facebook Groups after the Camp Fire. I examined how CFFGs offered unique and valuable spaces for connection that allowed members to engage in resilience organizing and disaster response and recovery. To conduct this case study, after engaging in observations of the Groups for over two years, I interviewed 25 administrators of CFFGs and distributed a survey in the Groups that was completed by survivors of the Camp Fire who were members of at least one CFFG during their recovery. I used network perspectives and the Communication Theory of Resilience (Buzzanell, 2010, 2019) as lenses through which administrators’ and survivors’ experiences with CFFGs was understood. I also analyzed the two datasets using multiple and mixed methods but primarily thematic analysis and path modeling. <br><br>The analyses for this case study are presented in four studies. The first two studies provide an understanding of the spaces for connection offered by CFFGs (i.e., characterizing the CFFGs and describing the spaces for connection as both helpful and hurtful), while the last two studies examine the use and utility of CFFGs (i.e., explaining the evolution of activity in CFFGs and investigating the connectivity and social support in CFFGs). <br><br>Across the four studies, I explored three central arguments, which are the primary contributions of this dissertation. First, I advocated for incorporating network thinking into resilience theorizing. With the findings of this dissertation, I extend the Communication Theory of Resilience by offering “managing communication networks” as a refinement of its fourth process of resilience (i.e., using and maintaining communication networks). Managing communication networks addresses the active strategies people use to manage their communication networks, including expanding, contracting, maintaining, and using their communication networks, as they endure and overcome hardship. I also forward the argument that people’s resilience is encompassed by their social networks, meaning their social network can be passively implicated by their resilience or actively involved in their resilience, but can also initiate resilience on their behalf.<br><br>Second, I contended Disaster Facebook Groups offer unique and valuable spaces for connection that facilitate resilience organizing and disaster response for at least five reasons. I argued that Disaster Facebook Groups empower emergent organizing; privilege local knowledge; are convenient; lack anonymity which adds authenticity; and allow for individualization. The findings of this dissertation provide evidence of how these reasons converged in CFFGs to enable members to exchange support that was not, and could not be, available elsewhere.<br><br>Third, I hypothesized that the use of Disaster Facebook Groups would predict the utility of Disaster Facebook Groups, resilience, and recovery for survivors. I tested two models that use different variables to represent the use and utility of CFFGs and recovery from the Camp Fire. The first model investigated how activity in CFFGs influenced the perceived helpfulness of CFFGs and how both the activity in and perceived helpfulness of CFFGs influenced the extent of recovery for survivors. I used retrospective data about five time points across survivors’ first two years of recovery and found the model was most explanative up to one month after the Fire. The second model assessed how various indicators of connectivity in CFFGs impacted received social support (i.e., informational, emotional, and tangible support), resilience, and satisfaction with recovery for survivors. The intensity of survivors’ connections to CFFGs, when they joined their first CFFG, and how many Facebook Friends they gained from their participation in CFFGs were the most predictive indicators of connectivity. From the Groups, survivors reported receiving informational support more than emotional support and emotional support more than tangible support.<br><br>I put the findings of the four studies, as well as the three central arguments, in conversation with each other in the discussion section, focusing on theory, practice, and methodology. Regarding theory, I contribute network thinking to resilience theorizing: I underscore resilience as an inherently networked process; I acknowledge expanding and contracting communication networks as sub-processes of resilience that complement but are distinctly different from using and maintaining communication networks; and I forward “managing communication networks” as a refinement and extension of the Communication Theory of Resilience’s fourth process of resilience (i.e., using and maintaining communication networks). Related to practice, I call for the continuation of conversations around Disaster Facebook Groups as unique and valuable spaces for connection, particularly regarding the five reasons I established. I also give suggestions for practice related to the use and utility of Disaster Facebook Groups for disaster response and recovery. For methodological considerations, I discuss the importance of forming relationships with participants when engaging in research about online communities and natural disasters and call to question the translation of findings about social media across platforms and the role of neoliberalism in resilience and disaster research and practice. Despite its limitations, this dissertation makes meaningful contributions to theory, practice, and methodology, while offering fruitful directions for future research. This mixed-method, event-specific case study brings attention to the influential citizen-driven disaster response in Facebook Groups after the Camp Fire. <br>