The 2019 International Workshop on Geocomputation for Social Sciences and Intelligent Geospatial Information Service was held at Wuhan University on July 7-9, 2019. More than 100 experts, scholars, and postgraduates from home and abroad attended the workshop.
The aim of this workshop was to cope with the new developments, demands, technologies, and challenges of the research field in geocomputation for social sciences and intelligent geospatial services and to promote the interdisciplinary research and academic exchanges among researchers.
The workshop was organized by Wuhan University, and co-organized by the State Key Laboratory of Surveying and Mapping Remote Sensing Information Engineering, School of Remote Sensing and Information Engineering, School of Resource and Environmental Sciences, Geocomputation Center for Social Sciences, and Collaborative Innovation Center of Geospatial Technology. The workshop was chaired by Prof. Gong Jianya, Dean of School of Remote Sensing and Information Engineering, and co-chaired by Prof. Luc Anselin, University of Chicago, USA and Dr. Bao Shuming, China Data Institute, USA.
Prof. Gong Jianya, a member of Chinese Academy of Sciences and the dean of the School of Remote Sensing and Information Engineering, made a welcome speech at the beginning of the opening ceremony. He introduced the background of the workshop, and the recent development of Geocomputation Center for Social Sciences, established in Jan. 2018, an interdisciplinary and open international research cooperation platform around geospatial science, and innovation platform based on modern GIS and intelligent spatial service. The international cooperation cases with the University of Chicago, and Harvard University were also introduced.
Professor Li Fei, Vice President of Wuhan University, delivered a speech at the opening ceremony. He said, “Wuhan University is one of the most influential bases of scientific research, personnel training, academic exchanges and achievements transformation in the field of remote sensing geographic information in China and even in the world. Especially in the field of remote sensing, it has ranked first in the world for three years. In the field of Humanities and Social Sciences, Wuhan University, after years of development, has also set up a scientific discipline system and cultivated a strong faculty, which has a wide range of social influence at home and abroad.” He hoped that through the interdisciplinary research of social geographic computing and the continuous in-depth application of spatial information intelligent services, the scientific research level of spatial data in Wuhan University and even in China could be effectively improved.
Professor Li Deren, a member of Chinese Academy of Sciences and Chinese Academy of Engineering, pointed out that with the continuous development of big data strategy, higher requirements had been put forward for interdisciplinary, data fusion and service intelligence. The deep integration of geospatial disciplines and social disciplines became an obvious development trend and research hotspot. “Our school has strong strength and excellent team in both disciplines,” he added. At last, he hoped through interdisciplinary research, brilliant academic results will be achieved.
At the end of the opening ceremony, Spatial Data Lab, which was jointly developed by Wuhan University and China Data Institute, USA, was officially released.
On July 8th, the following keynote reports have been given:
Hanan Samet, a professor at University of Maryland in the US, introduced 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. The analysis of temporal information is described which include a well-designed time slider, a heatmap-based visualization tool for displaying disease distribution, and intuitive spatiotemporal querying methods.
Professor Adrian Bailey, dean of the Faculty of Social Sciences of Hong Kong Baptist University, has studied the experiences of one cohort – newcomer migrants – to contribute to a deeper understanding of subjective wellbeing 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. The 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.
Leila De Florian, a professor at University of Maryland, presented a report on representation and topology analysis for large spatial data. She discussed issues in representation and topological analysis of big spatial data. The focus is 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 were presented and scalability issues were discussed. Applications to environmental data was presented.
Professor Guan Mei Bao of University of Illinois, Urbana-Champaign discussed some of the new geospatial methods for collecting and analyzing complex, and often real-time, space-time data for this purpose. She draws upon recent conceptual and methodological developments to examine how integrating the spatial and temporal dimensions and taking human mobility into account can advance our understanding of human mobility and health behaviors in urban areas. She also discussed how the collection and analysis of high-resolution space-time data enabled by advanced geospatial and mobile technologies can provide new insights on the relationships between people’s mobility, health behaviors, and the complex spatiotemporal dynamics of environmental influences.
Professor Li Qingquan, President of Shenzhen University, discussed the positive effects of internet of things, artificial intelligence, digital economy and automatic driving on the construction of urban informationization facing the development of urban informationization. Cites are experiencing disruptive innovations. ICT, IoT, artificial intelligence, digital economy, and autonomous vehicles redefine urban living and reshape urban forms. These advanced technologies enable us to produce multi-source urban data and make the city towards future smart cities. They are also transforming urban studies and applications into the paradigm of urban informatics. Prof. Li shared recent studies in urban informatics of his groups, such as urban analytic, geo-intelligence, and social computing. He also introduced the education practice in urban informatics at Shenzhen University.
Professor Li Bin of University of Central Michigan in the US studied the selective eigenvectors of spatial weight matrix to describe the geographical structure from the technical level. Prof. Li proposed 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, the common eigenvectors can be identified that are associated with the distribution of the dependent variable of interest over the entire studied period. In his presentation, Prof. Li explained the methodology and demonstrate the resulting maps with real-world data sets.
Professor Liu Yu of Peking University, starting from the dimension of social perception and the spatial heterogeneity of geographic research, explores the basic concepts of location and spatial interaction and their relationship with spatial heterogeneity. 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, spatial heterogeneity with the support social sensing data should be modeled. Two types of measures, first-order measures and second-order measures help to revisit two basic concepts: place and spatial interaction, both of which are essential to understand spatial heterogeneity.
On July 9th, technical sessions on 8 topics were launched. The topics covered Geospatial knowledge sharing, collaboration and service computing, Multiscale spatio-temporal statistical models and their applications, Spatiotemporal analysis based on social sensing big data, Research and application of night-light remote sensing images and miscellaneous, Integrated Geo-computation towards Social Justice in Spatial Planning, Internet of Things and Smart Sustainable Cities, Space-time analysis for human dynamics, GeoComputation and Social Science in the Belt and Road Initiative.
（通讯员:石立特、邵远征; 摄影：张华; 编辑：周丽园, 谢金龙）