No description available.

Existing studies of household overcrowding in Sweden are often descriptive and examine patterns at a large scale. Levels of overcrowding have increased since the mid-1980s and the highest shares are found in the largest cities among residents with a low income, a migration background, living in rental apartments, and often with children. The aim of this thesis is to increase the understanding of the measurements of household overcrowding, its development over time, its spatial patterns and its determinants at a small-scale neighborhood level with application to the City of Stockholm. It examines how the associations between overcrowding and other neighborhood characteristics can be understood in different neighborhood settings and what the implications are of using different scales and definitions of overcrowding. Cluster, correlation and regression analyses have been conducted using administrative data aggregated to key code areas and city districts. The results demonstrate that there are two types of overcrowding within the City of Stockholm, which are spatially separated and associated differently with socio-economic, demographic and housing characteristics of neighborhoods. It is suggested that explanatory segregation theories related to preference and economic and discriminatory structures are needed to understand the uneven spatial distribution of overcrowding in the City of Stockholm.

Analysis of EU divorce rates in socio-demographic perspective Abstract The aim of this diploma thesis is to analyze the basic trends in the development of the divorce rate in selected countries of the European Union, in the period of 1995 through 2015 and to find similar or different trends among them. The work is focused not only on the development of individual indicators of the divorce intensity, but also on the possible causes of changes in family behavior, both from a demographic and sociological point of view. Therefore, the second part of the work focuses on attitude analysis of the evaluators by particular country of the European Union, namely the specific attitudes of choosing a family, marriage and an opinion on divorce. The intensity of divorce rates increases during the observed period in all monitored countries, therefore the share of divorced persons in the European population increasing, too. The reasons are mainly the social and economic changes in society, which go along with the changes in family behavior. Divorce is tolerated by society and as behavior is justifiable for most people. On the other hand, family and marriage are still very important values for the people of European countries. Keywords: divorce rate, family, Europe, attitudes, binary logistic regression, cluster analysis

<p>The purpose of this research is to reveal and compare the language proficiency profiles of Purdue’s Chinese and Indian graduate applicants in various disciplines to take a step towards the development of Language Proficiency Literacy (LPL) of graduate admissions decision makers. The study argues that before being able to offer LPL development opportunities to admissions decision-makers, language testers need to gain admissions literacy in their specific academic context. One way this can be achieved is by analyzing graduate admissions data to see patterns of test score use in each discipline and to reveal language proficiency profiles of graduate applicants. Providing admissions decision makers with information about the linguistic characteristics of their applicants can be a very helpful step towards enhancing LPL in the context of graduate admissions. </p> <p>One of the analyses conducted towards the goal LPL development in the context of graduate admissions was a Cluster Analysis procedure followed by a Chi-square analysis to compare the language proficiency profiles of graduate applicants from various L1 backgrounds based on scores on the Test of English as a Foreign Language (TOEFL). The study found three language proficiency profiles in graduate applicants’ TOEFL data: 1) the ‘unbalanced’ profile, which consists of applicants who have higher scores in the subskills of reading and listening, and comparatively lower scores on speaking and writing, 2) the ‘balanced medium’ profile, which represents students who have moderate scores across all four subskills, and 3) the ‘balanced high’ profile, which consists of applicants who have high scores across all four subskills. The study found evidence for the interaction between graduate applicant test-takers’ L1 background and belonging to a balanced or an unbalanced language proficiency profile, which highlights the importance of considering subskill scores in addition the total score when using language proficiency test scores to select graduate students from specific L1 backgrounds.</p>

Digital twins have become a large part of new cyber-physical systems as they allow for the simulation of a physical object in the digital world. In addition to the new approaches of digital twins, machines have become more intelligent, allowing them to produce more data than ever before. Within the area of digital twins, there is a need for a less complex approach than a fully optimised digital twin. This approach is more like a digital shadow of the physical object. Therefore, the focus of this thesis is to study machine states and statistical distributions for all sensors in a machine. Where as majority of studies in the literature focuses on generating data from a digital twin, this study focuses on what characteristics a digital twin have. The solution is by defining a term named digital machine template that contains the states and statistical properties of each sensor in a given machine. The primary approach is to create a proof of work application that uses traditional data mining technologies and clustering to analyze how many states there are in a machine and how the sensor data is structured. It all results in a digital machine template with all of the information mentioned above. The results contain all the states a machine might have and the possible statistical distributions of each senor in each state. The digital machine template opens the possibility of using it as a basis for creating a digital twins. It allows the time of development to be shorter than that of a regular digital twin. More research still needs to be done as the less complex approach may lead to missing information or information not being interpreted correctly. It still shows promises as a less complex way of looking at digital twins since it may become necessary due to digital twins becoming even more complex by the day.

Groundwater analyses have revealed high concentrations of the toxic element arsenic around Stockholm and Mälardalen, a problem that often is linked to high levels of arsenic in the bedrock and which could be escalated by the many construction projects in the same region. However, it is unknown what part of the bedrock is causing the contamination. The aim of this thesis is to identify the chemical elements that associate with arsenic and study how the rock types differ in their content of elements and compounds. The highest median concentration of arsenic is found in quartz-feltspar-rich sedimentary rock, while intrusive rock types reveal the lowest levels. Using cluster analysis, arsenic is placed in a group including nine other elements, to which the strongest correlations are found with antimony, bismuth and silver. A moderate correlation with sulphur is also observed. The associations between groupings of elements are analysed using measures of dependence, which reveal relatively strong associations. Dimension reduction and ordination techniques provide further insight to the typical appearances of elements and reveal two groups of similar rock types.

Background: No previous study has focused on the inter-relationship among alcohol and drug use variables in the past year. This study aimed to classify the past year alcohol and drug use variables and investigate the selected variables in past year alcohol and drug use with the unmet need for mental health services among US adults. Methods: Data came from the 2015 National Survey on Drug Use and Health (NSDUH). Oblique principal component cluster analysis (OPCCA) was used to classify 37 variables on alcohol and drug use in the past year into disjoint clusters. Weighted multiple logistic regression analysis was used to examine the associations of selected variables with the unmet need. Results: 37 alcohol and drug use variables were divided into 7 clusters. The variable with the lowest 1-R2 ratio (R2 is the squared correlation) from each cluster was selected as follows: tobacco use, pain reliever use, tranquilizer use, stimulant use, zolpidem products use, illicit drug and alcohol use, and benzodiazepine tranquilizers misuse. Multiple logistic regression analysis showed that pain reliever use (OR = 1.33, 95% CI = 1.17–1.50), tranquilizer use (OR = 2.49, 95% CI = 2.16–2.86), stimulant use (OR = 1.22, 95% CI = 1.01–1.47), and illicit drug and alcohol use (OR = 1.54, 95% CI = 1.34–1.77) revealed positive associations with the unmet need for mental health services. Conclusion: This is the first study using OPCCA to reduce the dominations of alcohol and drug use; several alcohol and drug use variables in the past year were associated with unmet need of mental health services.

In cancer research, class discovery is the first process for investigating a new dataset for which hidden groups there are by similar attributes. However datasets from gene expressions, RNA microarray or RNA-sequence, are high-dimensional. Which makes it hard to perform clusteranalysis and to get clusters that are well separated. Well separated clusters are wanted because that tells that objects are most likely not placed in wrong clusters. This report investigate in an experiment whether using K-Means and hierarchical are suitable for clustering gene expressions in RNA-sequence data from various tumors. Dimensionality reduction methods are also applied to see whether that helps create well-separated clusters. The results tell that well separated clusters are only achieved by using PCA as dimensionality reduction and K-Means on correlation. The main contribution of this paper is determining that using K-Means or hierarchical clustering on the full natural dimensionality of RNA-sequence data returns unwanted silhouette average width, under 0,4.

The purpose of this work is a detailed description of spring precipitation totals in the Czech Republic, which has not been examined in more detail yet. The main aims of the diploma thesis are i) comparison of precipitation characteristics at individual stations for the period 1984-2014 and analysis of temporal and spatial variability of precipitation, ii) division of stations into three categories (lowland stations, stations in middle positions, mountain stations) based on altitude, average spring total precipitation and surrounding relief, and iii) division of stations by cluster analysis and comparison of precipitation characteristics between individual categories of stations obtained by subjective and objective methods. Statistical and precipitation characteristics include monthly and seasonal precipitation total, numbers of wet and dry days, numbers of days with a total above 5,10,15, and 20 mm, frequency and length of dry and wet periods, index of torrential rainfall and trends of these characteristics. The results show increasing linear trends of spring precipitation totals in all station categories, however these trends are statistically insignificant. The driest stations are lowland stations with an average spring precipitation total of 139,3 mm, the highest median of 169 dry periods, and...

The present thesis utilises dynamic time warping and cluster analysis with the aim of discovering different touristic profiles. GPS data of cruise passengers intra-destination movement at the destination of Visby, Gotland, was used in the analysis. Further stop detection was performed so as to compare stop activity and stop allocation between the clusters. Four tourist profiles were derived by juxtaposing the category of attractions/areas where high stop densities were found, with the spatial dispersal of stop activity, denoted as either exhibiting a concentrated or exploring pattern. Some key influencers of tourists' spatio-temporal behaviour were also identified. These included whether the cruise passengers appeared to have taken some mode of transportation upon their on-shore visit, whether the area was dense in activities/facilities oriented towards tourists and the time spent at the destination. The contribution of this thesis is twofold. First this thesis contributes to previous research by developing and testing a methodological approach utilising dynamic time warping to investigate cruise passengers' spatio-temporal behaviour at a destination. Second, the results of the thesis may aid destination managers in finding tools and strategies that are tailored after the unique opportunities and challenges posed by different tourist profiles.