报告时间：2016年11月24日（星期四），上午10：00
报告地点：紫金港校区蒙民伟楼402会议室
报告题目：MapTrix: Many-to-Many Geographically-Embedded Flow Visualisation
报告人：杨亚龙　博士
主持人：巫英才　研究员

摘要：Showing flows of people and resources between multiple geographic locations is a challenging visualisation problem. We designed a new visualisation called MapTrix to present such dense many-to-many flows. Like OD Maps, MapTrix overcomes the clutter associated with a traditional flow map while providing geographic embedding that is missing in standard OD matrix representations. We conducted two quantitative user studies to evaluate different visual representations. In our first study we compared a bundled node-link flow map representation and OD Maps with MapTrix. We found that OD Maps and MapTrix had similar performance while bundled node-link flow map representations did not scale at all well. Our second study compared participant performance with OD Maps and MapTrix on larger data sets. Again performance was remarkably similar.

个人简介：Yalong Yang is a PhD Candidate at Caulfield School of Information Technology, Monash University, VIC, Australia. He is working at Immersive Analytics Initiative and MArVL: Monash Adaptive Visualisation Lab under the supervision of Prof. Tim Dwyer, Prof. Kim Marriott, Dr Caron (Haohui) Chen from Data61, CSIRO and Dr. Sarah Goodwin.
His research topic is Visualising Spatial Flow Data. Many processes that take place in our world are spatial interactions. For example migration, movement of animals or disease, movement of goods or knowledge, commuting behaviors, etc. Such processes generate a huge amount of spatial flow data. The vast amount of data alone does not have an instant positive value to society; however, the potential knowledge extracted from these data sets can make large improvements to conventional procedures in many fields. It is important for analysts (e.g., geographers, anthropologists, financial analysts studying trade) to understand flows between different geographic locations to gain insight and discover patterns in these processes to support decision making.
His recent paper "Many-to-Many Geographically-Embedded Flow Visualisation: An Evaluation" has received the IEEE InfoVis 2016 Best Paper Honorable Mention.