Physical visualizations are artifacts that materialize abstract data. They take advantage of human natural abilities to interact with information in the physical world. These visualizations present an opportunity to be applied on new application domains. With the objective of discovering if physical visualizations can support remote monitoring of biological data, a technology probe is presented in the form of a reversed voodoo doll. This probe uses the natural affordance of an anthropomorphic figure to represent a person and reverses the concept of voodoo dolls in a playful way. The scenario of safety is selected for testing physical visualizations of bio-data. Two measurements from the human body, heart rate and motion are chosen as a light way to monitor remotely over a person’s conditions. During the study, a group of six participants were exposed to the technology probe and their interactions with it were observed. The study reports on the users’ interpretations of the data and uses given to the alternative modalities of the probe. The results suggest that the data mapping to the object’s body parts was effective for conveying meaning. Additionally, the results confirm that the use of multiple modalities in physical visualizations offers an opportunity to present information in situated contexts in the real world. The degree of physicality achieved by the reversed voodoo doll and the effects of the selected metaphors are discussed. In conclusion, it is argued that the responses and interpretations from the users indicate that the reversed voodoo doll served as a means in its own right to transmit information for monitoring of bio-data. / Fysiska visualiseringar är artefakter som materialiserar abstrakt data. Genom att använda sig av mänskliga naturliga förmågor interagerar de med information i den fysiska världen. Dessa visualiseringar skapar möjligheter för appliceringar inom nya tillämpningsområden. För att undersöka om fysiska visualiseringar kan stödja fjärrövervakning av biologisk data introducerades en sond i form av en omvänd voodoodocka. Med en människolik figur representerar denna sond en verklig person. På så sätt utnyttjar den naturliga associationer till mänskliga egenskaper och omvänder konceptet vodoodockor på ett lekfullt sätt. De fysiska visualiseringarna av biologisk data testas ur ett säkerhetsperspektiv. Två värden, hjärtfrekvens och rörelse, mäts från en människokropp för att göra det möjligt att övervaka en persons tillstånd på distans. Under studien observeras sex användare då de interagerar med sonden. Studien visar hur användarna tolkar sondens data och hur användningen varierar med avseende på sondens olika modaliteter. Resultaten från denna studie tyder på att datamappningen till sondens kroppsdelar effektivt ökade förståelsen. Dessutom bekräftar resultaten att användning av flera modaliteter i fysiska visualiseringar gör det möjligt att presentera information, anpassat till olika situationer i den verkliga världen. Till vilken grad voodoodockan ger en känsla av kroppslighet samt konsekvenser av de valda metaforerna diskuteras. I slutsatsen hävdas att användarnas svar och tolkningar tyder på att den omvända voodoodockan fungerade som ett medel för att övervaka biologisk data.

This paper is a literature study with an additional empirical approach to research how to improve user experience in data visualization web applications. This research has been conducted in collaboration with Caretia AB to improve their current data visualization tool. The research studies previous research on the topics of UI design, user experience, visual complexity and user interaction in the attempt to discover what areas of design and intuitivity that improves the user experiences in these kinds of tools. The findings were then tested together with Caretia through a proof-of-concept prototype application which was implemented with said findings. The conclusion of the results is that mapping ontology groups and prior experience as well as reducing visual overload are effective ways of improving intuitivity and user experience.

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Effective data visualization is essential for digital self-tracking to help users gain insights into their behavior and habits. Personalized visualizations engage users, making the self-tracking experience more meaningful. However, potential biases and limitations should be considered to ensure an accurate and objective self-tracking process. The study aims to examine users' interactions with visualized data in the digital self-tracking process and understand their perceptions of the accuracy and objectivity of personal data visualizations and the self-tracking processes on platforms that offer self-tracking features. These platforms include applications for tracking health and fitness, habits, music listening, book reading, and movie watching. The study employs a qualitative approach using semi-structured interviews with an ethnographic approach as a data collection method. This approach was selected to investigate users' interactions and opinions on data visualizations and the digital self-tracking process.  The findings show that participants primarily use data visualizations and other personal visualizations as reminders, for comparisons, planning, and motivation. Although they do not extensively analyze the visualized data, participants report experiencing heightened self-awareness and motivation. Despite their awareness of potential inaccuracies and subjectivity in the visualizations and the self-tracking process, participants are willing to overlook these aspects due to the perceived benefits associated with the process. Moreover, participants generally express a level of trust in the accuracy of their visualized data.

Historic open data can be an asset to community gardens in land use disputes, the preservation and sharing of cultural traditions, and adaptation to climate change. Yet scholarship has not yet provided an accessible guide to the many issues of labor and technology involved in producing open data. This thesis addresses this gap by offering a guide to producing, preserving, and interpreting open data oriented toward community gardens from a public history perspective. This thesis examines the history of community gardens and related community data stretching to the Progressive Era, drawing comparisons to to that of historic open data in the gallery, library, archives, and museum (GLAM) world. The thesis also considers the worth of crowdsourcing and other volunteer labor models in data production, offers basic considerations for structuring and maintaining historic open datasets, and reviews the role of data visualization as a means of data communication and interpretation. Ultimately, I contend that open data is doable in public history and urgently worthy of consideration for gardens. / History

[pt] As visualizações de dados são cada vez mais comuns na mídia tradicional e nas redes sociais. No entanto, a alfabetização visual da população não acompanhou essa crescente popularidade. É necessário para quem cria os gráficos montar uma comunicação visual que contenha as informações necessárias de forma atrativa e de fácil compreensão. Em contrapartida, é necessário para quem os consome, captar as informações representadas pelos gráficos e extrair as análises do que vê. A importância da alfabetização visual é a capacidade de ler um gráfico, ou seja, olhar para um gráfico e identificar informações relevantes, tendências e discrepâncias em um determinado cenário. Neste trabalho, realizamos quatro estudos para explorar os fatores que influenciam o sucesso da análise de dados visuais. No primeiro estudo descobrimos como as pessoas tentam dar sentido a visualizações de dados específicas, através de perguntas que elas fazem ao encontrar uma visualização pela primeira vez. No segundo estudo exploramos como as distribuições de dados podem afetar a eficácia e eficiência das visualizações de dados. No terceiro estudo investigamos quando não especialistas identificam que uma visualização não é adequada para responder uma pergunta de análise específica, quando eles fazem boas sugestões de alteração para tornar essas visualizações adequadas e quando avaliam bem a adequação de algumas sugestões oferecidas a eles. No quarto estudo, criamos um teste para avaliar a compreensão das pessoas sobre os aspectos aplicados (responder perguntas de análise com o apoio de uma visualização) e conceituais (questões sobre a função e estrutura) da visualização de dados. Nossos resultados fornecem recursos para o desenvolvimento de material didático e ferramentas para recomendação de visualizações de dados relacionadas a perguntas que se visa responder. Uma contribuição adicional deste trabalho aos resultados dos estudos foi a estruturação de uma lista unificada de diferentes tarefas de visualização que encontramos na literatura. / [en] Data visualizations are increasingly common in traditional media and social networks. However, the visualization literacy of the population did not follow this growing popularity. It is necessary for those who create the charts to assemble a visual communication that contains the necessary information in an attractive and easy-to-understand way. By contrast, it is necessary for those who consume them to capture information represented by the charts and extract the analyses of what they see. The importance of visual literacy is the ability to read a chart, i.e., look at a chart and identify relevant information, trends, and outliers in a given scenario. In this work, we conducted four studies to explore factors related to the success of visual data analysis. We identified issues ranging from data distribution to formulating good questions to enrich exploration. The first study discovered how people try to make sense of specific data visualizations through questions they ask when they first encounter a visualization. In the second study, we explored how data distributions can affect the effectiveness and efficiency of data visualizations. In the third study, we investigated when non-experts identify that particular visualization is not adequate to answer a specific analysis question, when they make good suggestions for changes to make these visualizations adequate, and when they evaluated well the adequacy of some suggestions offered to them. In the fourth study, we created a test to assess people s understanding of the applied (answering analysis questions supported by a visualization) and conceptual (questions about function and structure) aspects of data visualization. Our results provide resources for developing of educational material and tools for recommending data visualizations to answer specific data-relation questions. An additional contribution of this work to the results of the studies was the structuring of a unified list of different visualization tasks that we found in the literature.

The demand for transparency within the fashion industry is rising, and retailers are under mounting pressure to become more sustainable. This pressure comes from both consumers becoming more aware of their carbon footprint and from new laws and regulations worldwide. However, the concept green gap indicates that consumers do not behave according to their attitudes. Also, interpreting sustainable related data, such as the amount of emitted CO2e gases, seems to be complex and challenging for consumers to comprehend. This master's thesis aims to get insight into issues regarding how retailers currently gather and communicate sustainability data. Further, this thesis explores how this data can be communicated to consumers through data visualization and user interface design. Thematic analysis is applied to data from interviews with companies and retailers. The results of these interviews point to key issues such as resistance against transparency, lack of an industry standard, and complex product life cycles. A number of prototypes are created and designed to inform users about the environmental impacts of garments and their shopping habits. The results from user tests could not measure behavioral changes. However, the tests pointed to the importance of transparent data, abstraction level in the design of environmental impact graphs, and providing context to data for users to be able to act upon.

With the rise of digital platforms for the real estate market in Sweden, and their record of transaction data, there is still a lack of proper utilization and presentation of available data. The traditional geographical city areas are usually too large and varying to perform accurate analysis on. This report explores the possibility of dividing central Stockholm’s predefined city areas into smaller submarkets using data-driven methods. The smaller submarkets would provide a more homogeneous description of their respective area and serve as a better basis for valuation estimators. The creation of the submarkets are done through clustering, a subsection of Machine Learning. Different clustering algorithms are attempted in order to test for their fit to the model. Results are evaluated by analyzing the variance of attributes within and between the clusters, ensuring that variance is low within and high between. The results are also compared to predefined city areas, in order to ascertain the improvement achieved with the data-driven model. The data output is presented graphically in Google Maps for a visual evaluation while also allowing ease-of-use for potential commercial customers. The results were an interactive map with differentiated and mostly non-overlapping clusters. The best clustering algorithm was Hierarchical clustering that lowered the internal variance by 33% and increased the external variance by 171% compared to predefined city areas. A potential future use of properly delineated submarkets could include higher precision valuation estimators or more relevant apartment recommendations for a company such as Booli. / Trots den ökande användningen av digitala plattformar för bostadsmarknaden i Sverige samt mängden transaktionsdata som finns tillgänglig, så finns det få aktörer som utnyttjar detta fullt ut. De traditionella stadsområdena är ofta alltför stora och varierande för att utföra precisa analyser på. Denna rapport utforskar möjligheterna med att dela upp centrala Stockholms fördefinierade stadsområden till mindre submarknader genom datadrivna metoder. Dessa submarknader skulle ge en mer homogen beskrivning av deras respektive områden och fungera som en bättre utgångspunkt för prisvärdering. Framtagningen av submarknaderna görs genom klustring, en del utav maskininlärning. Olika klustringsalgoritmer implementeras för att testa deras förklaringsvärde. Resultaten utvärderades genom att analysera variansen av attributen inom och mellan klustren, sett till att variansen bör vara låg inom klustren och hög mellan klustren. Resultaten jämfördes även med förbestämda stadsområden, för att säkra den datadrivna modellens förbättring. Data outputen är presenterad grafiskt i Google Maps för visuell utvärdering medan det även tillåter enkel användning för potentiella slutkonsumenter. Resultatet av detta är en interaktiv karta med differentierade och mestadels icke-överlappande kluster. Denna rapport fann att den bästa klustringsmodellen var Hierarchical klustring som hade 33% lägre varians inom klustren samt 171% högre varians mellan klustren jämfört med förbestämda stadsområden. En potentiell framtida användning av klustrade submarknader skulle kunna vara mer precisa prisvärderingar eller mer relevanta bostadsrekommendationer för bolag så som Booli.