Abstract Data Visualisation in Mobile VR Platforms

Data visualisation, as a key tool in data understanding, is widely used in science and everyday life. In order data visualisation to be effective, perceptual factors and the characteristics of the display interface play a crucial role. Virtual Reality is nowadays accepted as a valid medium for scientific visualisation, because of its inherent characteristics of real-world emulation and intuitive interaction. However, the use of VR in abstract data visualisation is still limited. In this research, I investigate the use and suitability of mobile phone-based Virtual Reality as a medium for abstract data visualisation. I develop a prototype VR Android application and visualise data using the Scatterplot and Parallel Coordinates methods. After that, I conduct a user study to compare the effectiveness of the mobile VR application compared to a similar screen-based one by implementing some data exploration scenarios. The study results, while not being statistically significant, show improved accuracy and speed in the mobile VR visualisation application. The main conclusions are two-fold: Virtual Reality is beneficial for abstract data visualisation, even in the case of limited processing power and display resolution. Mobile VR, an affordable alternative to expensive desktop VR set-ups can be utilized as a data visualisation platform

3D City Models and urban information: Current issues and perspectives

Considering sustainable development of cities implies investigating cities in a holistic way taking into account many interrelations between various urban or environmental issues. 3D city models are increasingly used in different cities and countries for an intended wide range of applications beyond mere visualization. Could these 3D City models be used to integrate urban and environmental knowledge? How could they be improved to fulfill such role? We believe that enriching the semantics of current 3D city models, would extend their functionality and usability; therefore, they could serve as integration platforms of the knowledge related to urban and environmental issues allowing a huge and significant improvement of city sustainable management and development. But which elements need to be added to 3D city models? What are the most efficient ways to realize such improvement / enrichment? How to evaluate the usability of these improved 3D city models? These were the questions tackled by the COST Action TU0801 “Semantic enrichment of 3D city models for sustainable urban development”. This book gathers various materials developed all along the four year of the Action and the significant breakthroughs

Eco-visualisation: Combining art and technology to reduce energy consumption

Artworks that display the real time usage of key resources such as electricity offer new strategies to conserve energy. These eco-visualisations-or artworks that creatively visualise ecologically significant data in real time-represent a substantial contribution to new knowledge about dynamic feedback as a tool to promote energy conservation and environmental site-based learning in this interdisciplinary project that expands and builds on prior findings from the fields of art, design, environmental psychology, and human computer interaction (HCI). The aims of this research endeavor were to locate answers to the following questions related to energy conservation in various public contexts. Might dynamic feedback from data-driven artwork create a better understanding of resource consumption patterns? Which environments are best for promoting eco-visualisation: borne, workplace, or alternative spaces? What kinds of visualisation tactics are most effective in communicating energy consumption data? These initial questions generated a four-year research project that involved an extensive literature review in both environmental psychology and art history that culminated in three different case studies, which targeted the effectiveness of eco-visualisation as an innovative conservation strategy. The three primary claims to be proven with supporting evidence from the literature reviews and case studies are: (1) eco-visualisation offers novel visual ways of making invisible energy data comprehensible, and encourages site-based learning; (2) eco-visualisation that provides real time visual feedback can increase environmental awareness and possibly increase the conservation behaviour in the viewing population; (3) eco-visualisation encourages new perceptions of linkages between the single individual and a larger community via site-based dialogue and conversation. Although the results of the three case studies are generally positive and prove the claims, there are larger social and environmental questions that will be addressed. How can eco-visualisation be productively integrated into the home or workplace without becoming a disposable gadget that represents a passing fad or fancy? Most importantly, how can energy conservation interventions be conceived to be as sustainable as possible, and non-threatening from a privacy perspective? These questions and more contribute to the discussion and analysis of the results of the three case studies that constitute the primary source of new knowledge asserted here in this dissertation

Natural landscape scenic preference: techniques for evaluation and simulation.

The aesthetic beauty of a landscape is a very subjective issue: every person has their own opinions and their own idea of what beauty is. However, all people have a common evolutionary history, and, according to the Biophilia hypothesis, a genetic predisposition to liking certain types of landscapes. It is possible that this common inheritance allows us to attempt to model scenic preference for natural landscapes. The ideal type of model for such predictions is the psychophysical preference model, integrating psychological responses to landscapes with objective measurements of quantitative and qualitative landscape variables. Such models commonly predict two thirds of the variance in the predications of the general public for natural landscapes. In order to create such a model three sets of data were required: landscape photographs (surrogates of the actual landscape), landscape preference data and landscape component variable measurements. The Internet was used to run a questionnaire survey; a novel, yet flexible, environmentally friendly and simple method of data gathering, resulting in one hundred and eighty responses. A geographic information system was used to digitise ninety landscape photographs and measure their landforms (based on elevation) in terms of areas and perimeters, their colours and proxies for their complexity and coherence. Landscape preference models were created by running multiple linear regressions using normalised preference data and the landscape component variables, including mathematical transformations of these variables. The eight models created predicted over sixty percent of variance in the responses and had moderate to high correlations with a second set of landscape preference data. A common base to the models were the variables of complexity, water and mountain landform, in particular the presence or absence of water and mountains was noted as being significant in determining landscape scenic preference. In order to fully establish the utility of these models, they were further tested against: changes in weather and season; the addition of cultural structures; different photographers; alternate film types; different focal lengths; and composition. Results showed that weather and season were not significant in determining landscape preference; cultural structures increased preferences for landscapes; and photographs taken by different people did not produce consistent results from the predictive models. It was also found that film type was not significant and that changes in focal length altered preferences for landscapes