Statistical flow data such as commuting, migration, trade and money flows has gained manyinterests from policy makers, city planners, researchers and ordinary citizens as well. Therehave appeared numerous statistical data visualisations; however, there is a shortage of applicationsfor visualising flow data. Moreover, among these rare applications, some are standaloneand only for expert usages, some do not support interactive functionalities, and somecan only provide an overview of data. Therefore, in this thesis, I develop a web-enabled,highly interactive and analysis support statistical flow data visualisation application that addressesall those challenges.My application is implemented based on GAV Flash, a powerful interactive visualisationcomponent framework, thus it is inherently web-enabled with basic interactive features. Theapplication uses visual analytics approach that combines both data analysis and interactivevisualisation to solve cluttering issue, the problem of overlapping flows on the display. A varietyof analysis means are provided to analyse flow data efficiently including analysing bothflow directions simultaneously, visualising time-series flow data, finding most attracting regionsand figuring out the reason behind derived patterns. The application also supportssharing knowledge between colleagues by providing story-telling mechanism which allowsusers to create and share their findings as a visualisation story. Last but not least, the applicationenables users to embed the visualisation based on the story into an ordinary web-pageso that public stand a golden chance to derive an insight into officially statistical flow data.

Social networks have been evolving over the past few years, leading to a rapid increase in the number and complexity of relationships among their entities. In this research, we focus on a large scale dataset known as the Digital Bibliography and Library Project or DBLP, which contains information on all publications that have been published in computer and information science related journals and conference proceedings. We model the DBLP dataset as a social network of research collaborations. DBLP is a structured and dynamic dataset stored in the XML file format; it contains over 850,000 authors and 2 million publications, and the resulting collaboration social network is a scale-free network. We define DBLP collaboration social network as a graph that consists of researchers as nodes and links representing the collaboration or co-authorship relationships among the researchers. In this work, we implement a data analysis algorithm called Multidimensional Scaling (MDS) to represent the degree of collaboration among the DBLP authors as Euclidean distances in 2-dimensional space in order to analyze, mine and understand the relational information in this large scale network in a visual way. MDS is a useful technique for data visualization and graph drawing methods, but it has high computational complexity for large scale graphs such as the DBLP graph. Therefore, we propose different solutions to overcome this problem, and improve the MDS performance. In addition, as the quality of the MDS result is measured by a metric known as the stress value, we use the steepest descent method to minimize the stress in an iterative process called stress optimization in order to generate the best geometric layout of the graph nodes in 2-dimensional space. We also propose a solution to further enhance the graph visualization by partitioning the graph into sub-graphs and using repelling forces among nodes within the same sub-graph. Finally, we developed a new visualization tool that can handle the large scale of the DBLP graph, and provides the user a number of significant features that allow them to explore, navigate and sift for information through the graph, such as graph scaling and graphical search functionality. / UOIT

Visualization enables new forms of social expression beyond the support of scientific data analysis. Focusing on the expanded roles of computational visualization, I investigate the influences of computation on the aesthetics and the rhetoric of visualization through design research methods. My design research includes 1) the construction of knowledge by synthesizing literature from digital media studies, visual rhetoric, information visualization, graphic design history, and HCI and 2) research through practices and consequent critiques. Coded visualization is a new term that I coined to integrate the rhetoric and aesthetics of data visualization. I define it as a data-based interface whose visual form is an aesthetic space where messages are coded and interpreted with cultural references. I also suggest the design criteria of coded visualization, apply them to a design project, and critique how the current design of the project can be improved to fully exemplify the concept of coded visualization. This study on the rhetoric and aesthetics of visualization through design research contributes to digital media studies, design research, as well as information visualization.

In master’s diploma work „The investigation of multidimensional data visualization methods“ the wide review of multidimensional data visualization methods is presented. The author was limited to research only two multidimensional data visualization methods such as: a parallel coordinates visualization method and Andrews curves visualization method. The program realization of both methods is realised, i. e. computer program was written for comparing those methods. To write program the tools of MS Excel and MATLAB were used. The performance of these methods is analyzed by using the mostly used data: Iris, HBK, Wood multidimensional data. The data generated by MS Excel and statistical data, taken from real life also were used. The investigations show, that the data visualization by a parallel coordinates method has some advantages comparing with Andrews curves visualization method.

The ability to visualize a solution space can be very beneficial, and it is generally accepted that the objective of visualization is to aid researchers in gathering insight. However, insight cannot be gathered effectively if the source data is misrepresented. This dissertation begins by demonstrating that the adaptive landscape visualization in widespread usage frequently misrepresents the neighborhood structure of genotypic space and, consequently, will mislead users about the manner in which solution space is traversed by the genetic algorithm. Bernhard Riemann, the father of topology, explicitly noted that a measurement of the distance between entities should represent the manner in which one can be brought towards the other. Thus, the commonly used Hamming distance, for example, is not representative of traversals of genotypic space by the genetic algorithm – a representative measure must include consideration for both mutation and recombination. This dissertation separately explores the properties that mutational and recombinational distances should have, and ultimately establishes a measure that is representative of the traversals made by both operators simultaneously. It follows that these measures can be used to enhance the adaptive landscape, by minimizing the discrepancy between the interpoint distances in genotypic space and the interpoint distances in the two-dimensional representation from which the landscape is extruded. This research also establishes a methodology for evaluating measures defining neighbourhood structures that are purportedly representative of traversals of genotypic space, by comparing them against an empirically generated norm. Through this approach it is conclusively demonstrated that the Hamming distance between genotypes is less representative than the proposed measures, and should not be used to define the neighbourhood structure from which visualizations would be constructed. While the proposed measures do not distort the data or otherwise mislead the user, they do require a significant computational expense. Fortunately, the choice to use these measures is always made at the discretion of the user, with additional costs incurred when accuracy and representativity are of paramount importance. These measures will ultimately find further application in population diversity measurement, cluster analysis, and any other task where the representativity of the neighborhood structure of the genotypic space is vital.

Data mining algorithms analyze and mine databases for discovering implicit, previously unknown and potentially useful knowledge. Frequent pattern mining algorithms discover sets of database items that often occur together. Many of the frequent pattern mining algorithms represent the discovered knowledge in the form of a long textual list containing these sets of frequently co-occurring database items. As the amount of discovered knowledge can be large, it may not be easy for most users to examine and understand such a long textual list of knowledge. In my M.Sc. thesis, I represent both the original database and the discovered knowledge in pictorial form. Specifically, I design a new interactive visualization system for viewing the original transaction data (which are then fed into the frequent pattern mining engine) and for revealing the interesting knowledge discovered from the transaction data in the form of mined patterns.

Manufacturing Industry as an important part of European and Swedish economy faces new challenges with the daily growing global competition. An enabler of overcoming these challenges is a rapid transforming to a value-based focus. Investment in innovation tools for production system development is a crucial part of that focus which helps the companies to rapidly adapt their production systems to new changes. Those changes can be categorized to incremental and radical ones. In this research we studied the Obeya concept as a supporting tool for production system development with both of those approaches. It came from Toyota production system and is a big meeting space which facilitates communication and data visualization for a project team. Four lean companies have been studied to find the role of such spaces in production development. Results indicate a great opportunity for improving those spaces and their application to radical changes in production development projects / EXPRES

Data mining algorithms analyze and mine databases for discovering implicit, previously unknown and potentially useful knowledge. Frequent pattern mining algorithms discover sets of database items that often occur together. Many of the frequent pattern mining algorithms represent the discovered knowledge in the form of a long textual list containing these sets of frequently co-occurring database items. As the amount of discovered knowledge can be large, it may not be easy for most users to examine and understand such a long textual list of knowledge. In my M.Sc. thesis, I represent both the original database and the discovered knowledge in pictorial form. Specifically, I design a new interactive visualization system for viewing the original transaction data (which are then fed into the frequent pattern mining engine) and for revealing the interesting knowledge discovered from the transaction data in the form of mined patterns.

Rates of recorded crime have been falling in many countries in Western Europe, including Scotland, since the early 1990s. This marks the reversal of a trend of increasing levels of crime seen since the 1950s. Despite this important recent change, most analyses of the ‘crime drop’ have focused on recorded crime or victimisation rates aggregated to national or regional level. It is little known how patterns of offending or conviction have changed at the individual level. As a result it is not known how the crime drop is manifest in changing offending or conviction rates, or how patterns of criminal careers have changed over this period. The aim of this thesis is to explore trends in convictions across a number of criminal careers parameters – the age-crime curve, prevalence and frequency, polarisation and conviction pathways – over the course of the crime drop in Scotland. The results presented here are based on a secondary analysis of the Scottish Offenders Index, a census of convictions in Scottish courts, between 1989 and 2011. Analysis is conducted using a range of descriptive statistical techniques to examine change across age, sex and time. Change in the age-crime curve is analysed using data visualisation techniques and descriptive statistics. Standardisation and decomposition analysis is used to analyse the effects of prevalence, frequency and population change. Trends in conviction are also examined between groups identified statistically using Latent Class Analysis to assess the polarisation of convictions, and trends in the movement between these groups over time provides an indication of changing pathways of conviction. This thesis finds a sharp contrast between falling rates of conviction for young people, particularly young men, and increases in conviction rates for those between their mid-twenties and mid-forties, with distinct periods of change between 1989- 2000, 2000-2007 and 2007-2011. These trends are driven primarily by changes in the prevalence of conviction, and result in an increasingly even distribution of convictions over age. Analysis across latent classes shows some evidence of convictions becoming less polarised for younger men and women but increasingly polarised for older men and women. Similarities in trends analysed across latent classes between men and women of the same age suggest that the process driving these trends is broadly similar within age groups. Increases in conviction rates for those over 21 are explained by both greater onset of conviction and higher persistence in conviction, particularly between 1998 and 2004. The results of this thesis suggest that explanations of the crime drop must have a greater engagement with contrasting trends across age and sex to be able to properly explain falling conviction rates. These results also reinforce the need for criminal careers research to better understand the impact of recent changes social context on patterns of convictions over people’s lives. The distinct periods identified in these results suggest a potential effect of changes in operation of the justice system in Scotland leading to high rates of convictions in the early 2000s. However, the descriptive focus of this analysis and its reliance upon administrative data from a single country mean this thesis cannot claim to definitively explain these trends. As a result, replication of this research in another jurisdiction is encouraged to assess whether trends identified are particular to Scotland.

This paper looks into how data is best visualized and how visualizations should be designed to be most easily perceived. Furthermore the study looks into what tools there are available on the market today for visualizing data in angular 5 applications. With regards to a client, a developer team from the swedish police IT-department, the tools are evaluated and the one most suitable for the client is found. The paper also looks into how a dynamic data solution can be developed in angular 5. A solution where data can be selected in one component and displayed in another. To answer the questions sought a study of previous research into data visualization was done as well as a look into how angular 5 applications can be developed. Interviews with the clients were held where their specific requirements on visualization tools were identified. After searching and listing available visualization tools on the market the tools were evaluated against the clients requirements and a prototype application were developed. Showcasing both the most suitable tool and its integration but also a dynamic data solution in angular 5. As a conclusion data visualizations should be made as simple as possible with the main focus on the data. When it comes to tools the one most suitable to the client was Chart.js that easily integrated into an angular 5 application. An application that thanks to angular’s features is well equipped for handling and developing dynamic data solutions.