How Does Policy Conceptualise Citizen Science? A Qualitative Content Analysis of International Policy Documents

Policy and science show great interest in citizen science as a means to public participation in research. To recognize how citizen science is perceived to foster joint working at the science-society-policy interface, a mutual understanding of the term “citizen science” is required. Here, we assess the conceptualisation and strategic use of the term “citizen science” in policy through a qualitative content analysis of 43 international policy documents edited by governments and authorities. Our results show that most documents embrace the diversity of the research approach and emphasize the many benefits that citizen science may provide for science, society, and policy. These include boosting spatio-temporal data collection through volunteers, tapping into distributed knowledge domains, increasing public interest and engagement in research, and enhancing societal relevance of the respective research. In addition, policy documents attribute educational benefits to citizen science by fostering scientific literacy, individual learning, and skill development, as well as by facilitating environmental stewardship. Through active participation, enhanced ownership of research results may improve policy decision-making processes and possibly democratise research as well as public policy processes, although the latter is mentioned only in a few European Union (EU) documents. Challenges of citizen science mentioned in the analysed policy documents relate mainly to data quality and management, to organisational and governance issues, and to difficulties of the uptake of citizen science results into actual policy implementation due to a lack of citizen science alignment with current policy structures and agendas. Interestingly, documents largely fail to address the benefits and challenges of citizen science as a tool for policy development, i.e., citizen science is mainly perceived as only a science tool. Overall, policy documents seem to be influenced strongly by the citizen science discourse in the science sector, which indicates a joint advocacy for citizen science

Visual Analytics for Understanding Spatial Situations from Episodic Movement Data

Continuing advances in modern data acquisition techniques result in rapidly growing amounts of geo-referenced data about moving objects and in emergence of new data types. We define episodic movement data as a new complex data type to be considered in the research fields relevant to data analysis. In episodic movement data, position measurements may be separated by large time gaps, in which the positions of the moving objects are unknown and cannot be reliably reconstructed. Many of the existing methods for movement analysis are designed for data with fine temporal resolution and cannot be applied to discontinuous trajectories. We present an approach utilizing Visual Analytics methods to explore and understand the temporal variation of spatial situations derived from episodic movement data by means of spatio-temporal aggregation. The situations are defined in terms of the presence of moving objects in different places and in terms of flows (collective movements) among the places. The approach, which combines interactive visual displays with clustering of the spatial situations, is presented by example of a real dataset collected by Bluetooth sensors

10 Gbit/s based NRZ DWDM systems using polarisation switching in single wavelength channel

It is experimentally demonstrated that the nonlinear tolerance of 10 Gbit/s/ch NRZ based DWDM systems over 1500 km standard singlemode fibre can be significantly improved through the use of orthogonal polarisation switching between adjacent bits in a single wavelength channel

Speech Communication

Contains research objectives and reports on two research projects.U.S. Air Force (Electronic Systems Division) under Contract AF19(604)-6102National Science Foundation (Grant G-10800)National Science Foundation (Grant G-7364)National Science Foundation (Grant G-16526)National Institutes of Health (Grant MP-4737

Speech Communication

Contains reports on six research projects.U. S. Air Force Command and Control Development Division under Contract AF19(604)-6102National Science Foundatio

Topic modelling for spatial insights: Uncovering space use from movement data

We present a novel approach to understanding space use by moving entities based on repeated patterns of place visits and transitions. Our approach represents trajectories as text documents consisting of sequences of place visits or transitions and applies topic modelling to the corpus of these documents. The resulting topics represent combinations of places or transitions, respectively, that repeatedly co-occur in trips. Visualisation of the results in the spatial context reveals the regions of place connectivity through movements and the major channels used to traverse the space. This enables understanding of the use of space as a medium for movement. We compare the possibilities provided by topic modelling to alternative approaches exploiting a numeric measure of pairwise connectedness. We have extensively explored the potential of utilising topic modelling by applying our approach to multiple real-world movement data sets with different data collection procedures and varying spatial and temporal properties: GPS road traffic of cars, unconstrained movement on a football pitch, and episodic movement data reflecting social media posting events. The approach successfully demonstrated the ability to uncover meaningful patterns and interesting insights. We thoroughly discuss different aspects of the approach and share the knowledge and experience we have gained with people who might be potentially interested in analysing movement data by means of topic modelling methods

Extracting Movement-based Topics for Analysis of Space Use

We present a novel approach to analyze spatio-temporal movement patterns using topic modeling. Our approach represents trajectories as sequences of place visits and moves, applies topic modeling separately to each collection of sequences, and synthesizes results. This supports the identification of dominant topics for both place visits and moves, the exploration of spatial and temporal patterns of movement, enabling understanding of space use. The approach is applied to two real-world data sets of car movements in Milan and UK road traffic, demonstrating the ability to uncover meaningful patterns and insights