Innovative visualizations shed light on avian nocturnal migration

Globally, billions of flying animals undergo seasonal migrations, many of which occur at night. The temporal and spatial scales at which migrations occur and our inability to directly observe these nocturnal movements makes monitoring and characterizing this critical period in migratory animals’ life cycles difficult. Remote sensing, therefore, has played an important role in our understanding of large-scale nocturnal bird migrations. Weather surveillance radar networks in Europe and North America have great potential for long-term low-cost monitoring of bird migration at scales that have previously been impossible to achieve. Such long-term monitoring, however, poses a number of challenges for the ornithological and ecological communities: how does one take advantage of this vast data resource, integrate information across multiple sensors and large spatial and temporal scales, and visually represent the data for interpretation and dissemination, considering the dynamic nature of migration? We assembled an interdisciplinary team of ecologists, meteorologists, computer scientists, and graphic designers to develop two different flow visualizations, which are interactive and open source, in order to create novel representations of broad-front nocturnal bird migration to address a primary impediment to long-term, largescalenocturnal migration monitoring. We have applied these visualization techniques to mass bird migration events recorded by two different weather surveillance radar networks covering regions in Europe and North America. These applications show the flexibility and portability of such an approach. The visualizations provide an intuitive representation of thescale and dynamics of these complex systems, are easily accessible for a broad interest group, and are biologically insightful. Additionally, they facilitate fundamental ecological research, conservation, mitigation of human–wildlife conflicts, improvement of meteorological products, and public outreach, education, and engagement

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

Designing visual analytics methods for massive collections of movement data

Exploration and analysis of large data sets cannot be carried out using purely visual means but require the involvement of database technologies, computerized data processing, and computational analysis methods. An appropriate combination of these technologies and methods with visualization may facilitate synergetic work of computer and human whereby the unique capabilities of each “partner” can be utilized. We suggest a systematic approach to defining what methods and techniques, and what ways of linking them, can appropriately support such a work. The main idea is that software tools prepare and visualize the data so that the human analyst can detect various types of patterns by looking at the visual displays. To facilitate the detection of patterns, we must understand what types of patterns may exist in the data (or, more exactly, in the underlying phenomenon). This study focuses on data describing movements of multiple discrete entities that change their positions in space while preserving their integrity and identity. We define the possible types of patterns in such movement data on the basis of an abstract model of the data as a mathematical function that maps entities and times onto spatial positions. Then, we look for data transformations, computations, and visualization techniques that can facilitate the detection of these types of patterns and are suitable for very large data sets – possibly too large for a computer's memory. Under such constraints, visualization is applied to data that have previously been aggregated and generalized by means of database operations and/or computational techniques

Software Engineering in the IoT Context: Characteristics, Challenges, and Enabling Strategies

L'abstract è presente nell'allegato / the abstract is in the attachmen

Cross-display attention switching in mobile interaction with large displays

Mobile devices equipped with features (e.g., camera, network connectivity and media player) are increasingly being used for different tasks such as web browsing, document reading and photography. While the portability of mobile devices makes them desirable for pervasive access to information, their small screen real-estate often imposes restrictions on the amount of information that can be displayed and manipulated on them. On the other hand, large displays have become commonplace in many outdoor as well as indoor environments. While they provide an efficient way of presenting and disseminating information, they provide little support for digital interactivity or physical accessibility. Researchers argue that mobile phones provide an efficient and portable way of interacting with large displays, and the latter can overcome the limitations of the small screens of mobile devices by providing a larger presentation and interaction space. However, distributing user interface (UI) elements across a mobile device and a large display can cause switching of visual attention and that may affect task performance. This thesis specifically explores how the switching of visual attention across a handheld mobile device and a vertical large display can affect a single user's task performance during mobile interaction with large displays. It introduces a taxonomy based on the factors associated with the visual arrangement of Multi Display User Interfaces (MDUIs) that can influence visual attention switching during interaction with MDUIs. It presents an empirical analysis of the effects of different distributions of input and output across mobile and large displays on the user's task performance, subjective workload and preference in the multiple-widget selection task, and in visual search tasks with maps, texts and photos. Experimental results show that the selection of multiple widgets replicated on the mobile device as well as on the large display, versus those shown only on the large display, is faster despite the cost of initial attention switching in the former. On the other hand, a hybrid UI configuration where the visual output is distributed across the mobile and large displays is the worst, or equivalent to the worst, configuration in all the visual search tasks. A mobile device-controlled large display configuration performs best in the map search task and equal to best (i.e., tied with a mobile-only configuration) in text- and photo-search tasks

The Datafied Society. Studying Culture through Data

As more and more aspects of everyday life are turned into machine-readable data, researchers are provided with rich resources for researching society. The novel methods and innovative tools to work with this data not only require new knowledge and skills, but also raise issues concerning the practices of investigation and publication. This book critically reflects on the role of data in academia and society and challenges overly optimistic expectations considering data practices as means for understanding social reality. It introduces its readers to the practices and methods for data analysis and visualization and raises questions not only about the politics of data tools, but also about the ethics in collecting, sifting through data, and presenting data research. AUP S17 Catalogue text As machine-readable data comes to play an increasingly important role in everyday life, researchers find themselves with rich resources for studying society. The novel methods and tools needed to work with such data require not only new knowledge and skills, but also a new way of thinking about best research practices. This book critically reflects on the role and usefulness of big data, challenging overly optimistic expectations about what such information can reveal, introducing practices and methods for its analysis and visualization, and raising important political and ethical questions regarding its collection, handling, and presentation

Discovering and developing primary biodiversity data from social networking sites

An ever-increasing need exists for fine-scale biodiversity occurrence records for a broad variety of research applications in biodiversity and science more generally. Even though large-scale data aggregators like GBIF serve such data in large quantities, major gaps and biases still exist, both in taxonomic coverage and in spatial coverage. To address these gaps, in this dissertation, I explored social networking sites (SNS) as a rich potential source of additional biodiversity occurrence records. In my first chapter, I explored the idea of discovering, extracting, and organizing massive numbers of biodiversity occurrence records now available on SNSs. I presented a proof-of-concept with Flickr as the SNS and Snowy Owls (Bubo scandiacus) and Monarch Butterflies (Danaus plexippus) as target species. The methods presented in this chapter can easily be used for any other SNS, region, or species group. These approaches are broadly applicable to animal and plant groups that are photographed, and that can be identified from photographs with some degree of confidence (e.g., birds, butterflies, cetaceans, orchids, dragonflies, amphibians, and plants). SNS thus offer a rich new source of biodiversity data. To understand the strengths and weaknesses of biodiversity data, we need effective tools by which to explore and visualize these data. I developed a suite of such tools in an R package called bdvis, which is described in chapter two. The package allows users to explore spatial, temporal, and taxonomic dimensions of biodiversity data sets to highlight gaps and identify strengths. In the third chapter, I explored Flickr further as a source of biodiversity data for the birds of the world, to assess the potential of augmenting the largest portal to biodiversity occurrence data, i.e., the Global Biodiversity Information Facility (GBIF). GBIF provides access to ~190 x 106 bird records, compared to ~7 x 106 that I could discover from Flickr, out of which only ~1.3 x 106 were geotagged. However, the Flickr data showed the potential to add to knowledge about birds in terms of geographic, taxonomic, and temporal dimensions, as Flickr data tended to be complementary to the GBIF-derived information. Finally, I developed a case study to investigate the quantity of records existing, and the quality of identifications by users on Flickr. I developed a detailed case study of Indian swallowtail butterflies, and implemented a crowd-sourcing platform to recruit identification expertise and apply it to butterfly photographs from the SNS. Results were encouraging, with 93% correct identities for records of this family of butterflies from across India