Program at a Glance
Department of Computer Science, Center for Automation Research Institute for Advanced Computer Studies, University of Maryland, USA
Hanan Samet (http://www.cs.umd.edu/~hjs/) is a Distinguished University Professor of Computer Science at the University of Maryland, College Park and is a member of the Institute for Computer Studies. He is also a member of the Computer Vision Laboratory at the Center for Automation Research where he leads a number of research projects on the use of hierarchical data structures for database applications, geographic information systems, computer graphics, computer vision, image processing, games, robotics, and search. He received the B.S. degree in engineering from UCLA, and the M.S. Degree in operations research and the M.S. and Ph.D. degrees in computer science from Stanford University. He is the Founding Editor-In-Chief of the ACM Transactions on Spatial Algorithms and Systems (TSAS), the founding chair of ACM SIGSPATIAL, a recipient of a Science Foundation of Ireland (SFI) Walton Visitor Award at the Centre for Geocomputation at the National University of Ireland at Maynooth (NUIM), 2009 UCGIS Research Award, 2010 CMPS Board of Visitors Award at the University of Maryland, 2011 ACM Paris Kanellakis Theory and Practice Award, 2014 IEEE Computer Society Wallace McDowell Award. Spatio-Temporal Disease Tracking Using News Articles Geographical Information Systems have been increasingly used to aid the prompt detection, tracking, and analysis of disease outbreaks. Web content which is full of health-related data also serves as a useful resource for disease outbreak analysis. News posts often report the initial outbreak of diseases and contain valuable information that aids in ascertaining the time and location of the disease outbreak. The locations mentioned in the news posts are specified textually rather than geometrically thereby requiring the use of geotagging methods to detect them and to map the textual specification to the corresponding actual geometric specification. The NewsStand system which aggregates news posts by topic and location while providing a map query interface to them is enhanced to enable disease tracking and analysis by geotagging disease-related web news posts. Besides the powerful functionalities of NewsStand for news exploration, enhancements of NewsStand with respect to the analysis of temporal information are described which include a well-designed time slider, a heatmap-based visualization tool for displaying disease distribution, and intuitive spatiotemporal querying methods. Future improvements to NewsStand are also discussed. More details on NewsStand can be found in the video at http://vimeo.com/106352925 which accompanies the ``cover article'' of the October 2014 issue of the Communications of the ACM about NewsStand at http://tinyurl.com/newsstand-cacm or a cached version at http://www.cs.umd.edu/~hjs/pubs/cacm-newsstand.pdf.
Chair Professor of Geography and Dean of Social Sciences, Hong Kong Baptist University
Adrian J. Bailey is a scholar known for his research in population, migration, economic, and social geography. He is currently Chair Professor of Geography and Dean of Social Sciences at Hong Kong Baptist University. His research interests include the study of transnationalism, with his work in this area exploring the diverse ways in which the state affects life outcomes among immigrants and refugees. Bailey holds a PhD from Indiana University (Geography with a Minor in African Population Studies). He has previously worked at the University of Leeds and Dartmouth College. In 2013, the Academy of Social Sciences bestowed the status of Academician on Bailey, in recognition of the impact of his work on the social sciences. This title was later changed to Fellow by the Academy. In 1989, Bailey received the Lieber Memorial Teaching Associate Award from Indiana University, which was established to recognise outstanding teachers among the university's graduate students. Happiness, cities, and newcomer migrants Research on happiness and subjective wellbeing (SWB) suggests that, controlling for age, health of individuals and economic development, large cities are less happy places. Current analysis is inconclusive as to why. This paper studies the experiences of one cohort – newcomer migrants – to contribute to a deeper understanding of SWB in large cities. A growing body of research, much of it from China, finds empirical support for hypotheses that link the happiness of migrants to levels of social cohesion in cities, access to social capital, and relative experiences of inequality. To examine the multiple dimensions of subjective wellbeing among migrants in Hong Kong we report measures related to housing, family life, social networks, community, economic standing, and legal situation. Our findings shed light on the distinctiveness of SWB among migrants compared to the general population, on the drivers of big city SWB, and on the possible directions that policies for social integration may consider.
President of IEEE Computer Society University of Maryland at College Park, USA
Leila De Floriani is a professor at the University of Maryland at College Park. She has previously been a professor at the University of Genova (Italy) since 1990. During her career, she has also held positions at the University of Nebraska, Rensselaer Polytechnic Institute, and the Italian National Research Council. Leila De Floriani is the 2019 IEEE Computer Society (CS) President-Elect (2020 President), and has been a member of the IEEE CS Board of Governors since 2017. She is a Fellow of IEEE, a Fellow of the International Association for Pattern Recognition (IAPR), a Pioneer of the Solid Modeling Association, and an IEEE Computer Society Golden Core Member. She has been the editor-in-chief of the IEEE Transactions on Visualization and Computer Graphics (TVCG) in 2015-2018, and served as an associate editor for the same journal in 2004-2008. Leila De Floriani is an associate editor of ACM Transactions on Spatial Algorithms and Systems, GeoInformatica, and Graphical Models. She has served on the program committees of over 150 leading international Workshops, and has contributed to many Workshops in a leadership capacity. She has authored over 300 peer-reviewed scientific publications in geospatial data representation and processing, scientific data visualization, geometric modeling, shape and data analysis and understanding, garnering several best paper awards. Representations and topology-based approaches for large field data analysis and visualization Analyzing huge spatial data sets, describing terrains or 3D scalar fields, requires efficient data management techniques, powerful analysis algorithms and visualization methods, which allow domain experts to effectively interact with the data. Tools from topological data analysis, a recent machine learning discipline rooted in topology and geometry, provide a theoretically well-justified, and parameter-free way to extract the complex intrinsic structures of data in a very concise format, but are computationally intensive for current large-size data sets. This talk will discuss issues in representation and topological analysis of big spatial data. The focus will be on point data equipped with one or more function values: scalar fields (terrains, 2D or 3D images, unstructured volume data sets, etc.), and multi-fields, collections of fields with different modalities (e.g., pressure and density in physical simulations). Topology-based visual analytics approaches to support interactive data analysis will be presented and scalability issues will be discussed. Applications to environmental data will be presented.
President of Shenzhen University, China
Prof. Qing-Quan Li, the Academician of International Eurasian Academy of Sciences, and the President of Shenzhen University since 2013. Before that, Prof. Li was the vice president of Wuhan University, and the director of the Transportation Research Center at Wuhan University. Prof. Li’s research interests are spatial-temporal data analysis, multi-sensor integration, industry and engineering surveying. He has lea over 50 research projects, published 5 books/chapters and over 400 research papers, and serves on the editorial board of a number of academic journals. In recognition of his research achievements, he was awarded varies national and professional prizes. Title: TBD Abstract: TBD
Director of the Space-Time Analysis and Research Laboratory, University of Illinois at Urbana-Champaign
Mei-Po Kwan is Professor of Geography and Geographic Information Science and Director of the Space-Time Analysis and Research (STAR) Laboratory at the University of Illinois at Urbana-Champaign. She received her Ph.D. from the University of California at Santa Barbara. She had served as an editor of Annals of the American Association of Geographers for 12 years and is editor of the book series entitled SAGE Advances in Geographic Information Science and Technology. She is an associate editor of Travel Behaviour and Society, and serves on the editorial boards of Journal of Transport Geography, Applied Geography, International Journal of Geographical Information Science (IJGIS), and Geographical Analysis. Kwan is Fellow of the U.K. Academy of Social Sciences, the American Association for the Advancement of Science (AAAS), and the John Simon Guggenheim Memorial Foundation. She has received many prestigious honors and awards. Kwan's research interests include environmental health, sustainable cities, human mobility, urban/social issues in cities, and GIScience. She has made ground-breaking contributions to these areas. Her recent collaborative projects include the development of a unified cyberinfrastructure framework for scalable spatiotemporal data analytics, the development of a Geospatial Virtual Data Enclave (GVDE) for sharing and analyzing confidential geospatial data, and examination of the health risks of female sex workers, adolescent and adult participation in high-risk drug use, individual exposure to air pollution and noise, and environmental influences on physical activity. Title: TBD Abstract: TBD
Institute of Remote Sensing and Geographical Information Systems, Peking University, China
Dr. Yu Liu is Boya Professor of GIScience at the Institute of Remote Sensing and Geographical Information Systems, Peking University. His research interests focus on analytical methods for various big geo-data. The term "social sensing" coined by Dr. Liu's team has attracted much attention from academia. He has conducted about 10 research projects as PI or Co-PI granted by NSFC (National Science Foundation of China) and MOST (Ministry of Science and Technology) of China. Yu Liu has published 80+ refereed papers in international journals including International Journal of Geographical Information Science, Transactions in GIS, and Computers and Geosciences. He is currently an Associate Editor of Computers, Environment and Urban Systems. Social sensing: From places to spatial interactions Multi-source big geo-data provide a new approach to representing our socio-economic environments based on behavior patterns of large volumes of individuals. Given that spatial heterogeneity is the foundation of geography studies, we should model spatial heterogeneity with the support social sensing data. Two types of measures, first-order measures and second-order measures, help us to revisit two basic concepts: place and spatial interaction, both of which are essential to understand spatial heterogeneity. This talk will summarize a number of analytical big geo-data methods for sensing both places and spatial interactions.
Department of Geography and Environmental Studies Central Michigan University, USA
Dr. Li Bin is a Professor at Central Michigan University in the US, where he chairs the Department of Geography and Environmental Studies. Dr. Li has a Ph.D. in Geography from Syracuse University, an M.S. from University of Nebraska, and a B.S. from South China Normal University. He held an academic position with University of Miami as well as adjunct positions at Wuhan University and South China Normal University. Dr. Li was an active participant and leader of several international collaborative initiatives, serving as the co-executive director of the Sino-US Joint Research Center on Lakes and Wetlands, associate director of the International Collaborative Center on Geocomputation Studies, and president of the International Association of Chinese Professionals in GIS. Dr. Li specializes in Geographic Information Science with research and teaching experiences in Geographic Information Services, Cartography, High Performance Computing, and Spatial Statistics, with recent focuses on information redundancy in big data, visualization of spatial structures, and regression modeling with large spatial data sets. Mapping Time-Invariant Geographic Variables In mapping dynamic geographic processes, the focus has been on the variables that change over time. Though largely neglected, revealing the relatively stable geographic context is equally important. In fact, in pattern recognition, identifying the time-invariant features is an essential task. Doing so in space-time mapping requires first a quantitative representation of geographic structure that influences the distribution of a specific geographic variable. Those aspects of the geographic structure that remain relatively unchanged through a specific period of time would be considered as time-variant geographic variables. We propose to use selective eigenvectors of the spatial weights matrix to characterize the geographic structure for a given landscape. And by applying linear mixed regression to space-time data, we can identify the common eigenvectors that are associated with the distribution of the dependent variable of interest over the entire studied period. In this paper, we will explain the methodology and demonstrate the resulting maps with real world data sets.
