GIBD is organizing a series of sessions on geospatial big data and spatial computing in the 2022 AAG annual meeting

Sponsor Group(s):  Cyberinfrastructure Specialty Group

Organizer(s):

Zhenlong Li, Shengjie Lai, Bo Huang, Kathleen Stewart

Public health is inextricably linked to geospatial context. Where, when, and how people interact with natural, social, built, economic and cultural environments directly influence human health outcomes, policy making, planning and implementation, especially for infectious diseases such as COVID-19, HIV, and influenza. Geospatial data has long been used in health studies, dating back to John Snows’ groundbreaking mapping of cholera outbreaks in London, and continuing today in a wide range of scientific inquiries, e.g., examining the effects of environmental, neighborhood, and demographic factors on health outcomes, understanding accessibility and utilization of health services, modeling the spread of infectious diseases, assessing the effectiveness of disease interventions, and developing better healthcare strategies to improve health outcomes and equity.

Emerging sources of geospatial big data, such as data collected from social sensing, remote sensing, and health sensing (health wearables) contain rich information about the environmental, social, population, and individual factors for health that are not available in traditional health data and population statistics. Along with innovative spatial and computing methodologies in GIScience, geospatial big data provides unprecedented opportunities for advancing the infectious disease research. The ongoing COVID-19 pandemic further highlights the demand on and the power of big data and spatial analysis in modeling, simulating, mapping, and predicting the spread of infectious diseases and their intervention across the world.

Along these lines, this paper session aims to capture recent advancements in leveraging geospatial big data and spatial analysis in infectious disease-related research, such as disease mapping and cluster detection, early detection and warning of disease outbreaks, and spatial analysis and modeling of disease spread and control. Potential topics include (but are not limited to) the following:

• Collection, processing, and integration of geospatial big data (e.g., satellite images, floor plans, 3D models, social media and mobile phone data) with health big data (e.g., electronic medical records) to extract geospatial context at various spatiotemporal scales (e.g., environmental risks, socioeconomic factors,and population mobility) to address infectious disease questions.
• Innovative methodologies for geospatial big data analytics in the context of infectious diseases, including geocomputation algorithms and geostatistical models. For example, assessing the effectiveness of non-pharmaceutical interventions in preventing the resurgence of COVID-19 using human mobility data.
• Combining geospatial big data with advanced computing technologies such as machine learning (ML) and geospatial artificial intelligence (GeoAI) to uncover hidden patterns and new information in infectious diseases related to, for example, the spreading, disparity, morbidity, and mortality of COVID-19.
• Developing accessible and reusable geovisualization and mapping methods, sharable data products, and online tools that help foster multidisciplinary collaborations, engage community and facilitate public understanding and decision making during disease outbreaks such as the COIVD-19 pandemic.

Sponsor Group(s): Cyberinfrastructure Specialty Group

Organizer(s):

Huan Ning, Zhenlong Li, Yuqin Jiang

Sponsor Group(s): 

Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group, Transportation Geography Specialty Group

Organizer(s):

Yuqin Jiang, Zhenlong Li, Xiao Huang

“Human mobility” is a commonly used but loosely defined term which represents the concept about people’s spatiotemporal occupation and involves interaction among human, society, and surrounding physical environment. Better understanding human mobility is essential for understanding human interactions with surrounding environment and the use of geographic space, which can benefit transportation and urban planning, political decision making, epidemiology, economic development, emergency management, and many other fields. Human activities have been producing massive amount of geospatial data. Recent technology advancements further pushed the volume, variety, and velocity of human mobility to an unprecedented level. How to efficiently process, analyze, and make sense of the massive human movement data remains challenging, especially within dynamic spatial and temporal context. This session aims to capture the latest efforts in analyzing human movement data and revealing human movement patterns that contributes to a better understanding of human activities and their surrounding environment under various circumstances and within different domains, such as transportation, social networks, public health, urban analysis, and emergency management.

Potential topics include, but not limited to, the following:
• Human mobility data capturing, storing, processing and analyzing
• Methodological improvements in human movement data mining, pattern analysis, and visualization
• Understanding changes in human mobility patterns under the COVID-19 pandemic
• Quantifying human mobility pattern changes
• Modeling human mobility during different events, for instance, hurricane evacuation
• Interdisciplinary applications with spatiotemporal human mobility data

Sponsor Group(s): 

Geographic Information Science and Systems Specialty Group, Cyberinfrastructure Specialty Group, Spatial Analysis and Modeling Specialty Group

Organizer(s):

Xiao Huang, Xinyue Ye, Zhenlong Li

Although the Big Data Era provides countless opportunities with the emerging of innovative data sources, it also poses new challenges, among which reproducibility and replicability (R & R) are facing a growing awareness. The extensive usage of urban monitoring big data, such as satellite imagery, location-based services, street views, to list a few, uniquely emphasizes the importance of R & R in Urban Science from the intertwining perspectives of location privacy, geospatial data quality, computing scalability, geoinformation shareability, and conclusion generalizability. To support reproducible computational studies, Choi et al. (2021) identified three thrusts: 1) open sharing of data and models online; 2) encapsulating computational models through containers and self-documented tutorials; 3) developing Application Programming Interfaces (APIs) for programmatic control of complex computational models. In addition, other venues exist where R & R can be promoted, such as the development of visualization frameworks, data-sharing portals, and integrated cyberinfrastructures. In response to the R & R challenges in Urban Science and the growing open-sourcing trend in academia, this session encourages the submission of abstracts that focus on tackling urban issues and problems by designing shareable data/products, developing analytical tools, launching online data visualization portals, constructing integrated cyberinfrastructures, and so on. Submitted abstracts could cover but are not limited to the following themes:

• Shareable urban monitoring data and products that benefit urban science communities.
• Online visualization, analytical, and data-sharing platforms that promote and facilitate data- and knowledge-sharing for both academia and the public.
• Development of reusable and interoperable analytical tools, packages, models, and data-accessing portals/APIs that advance urban sciences.
• Applied urban studies using designed data products, models, tools, and platforms.
• Research agenda and visions related to reproducibility and replicability in urban science.
• Urban monitoring and analytics using sharable data and platforms

Sponsor Group(s): 

Geographic Information Science and Systems Specialty Group, Hazards, Risks, and Disasters Specialty Group

Organizer(s):

Edwin Chow, Zhenlong Li, Qunying Huang

The growth of information and communication technologies (ICT) has enabled citizen participation in scientific investigation (a.k.a. citizen science) and sharing of data and information via social media (e.g., Twitter) and social networking sites (e.g., Facebook). The advancements in Internet of Things (IoTs) and connected devices including drones and aerial robotics have enabled the use of social media generated big data to understand human dynamics, and their interaction with the built environments. Significant advancements have been made to collect and analyze these data for emergency response, risk communication, mobility studies among others.

The big data derived from citizen sensors tend to suffer from a myriad of uncertainties in terms of positional accuracy, context ambiguity, credibility, reliability, representativeness and completeness. Moreover, there are also serious concerns about data provenance and privacy. While there is no shortage in big data applications, the quality issue of these data remains an intellectual and practical challenge. A lack of data provenance for these data combined with unavailability of high-quality reference data appropriate to its enormous volume, heterogeneous structure in near real-time make it difficult to evaluate the quality of these data. Moreover, the notion of “ground truth” in social science research is subjected to the discourse of space-place dichotomy, the spatial and contextual randomness in human behaviors. The heterogeneous nature of these data in terms of data structure and content requires tremendous amount of processing at various stages of analytics before the data could be integrated with other geospatial datasets for decision-making purposes. Privacy awareness is of increasing importance to data management, dissemination and distribution in many research projects. Although aggregation, permutation or masking techniques can be used to protect data privacy without compromising the overall quality of data, its effectiveness depends on the degree of distribution heterogeneity of the geographic phenomenon. This session welcomes basic and empirical research that advances existing understanding and techniques to address the quality issue of big data generated from social media and its impact on applications pertaining to human dynamics, built environments and hazards. Possible topics may include but are not limited to:

• Quality issues in social media big data
• Challenges in collecting, processing and analyzing big data for real-time applications
• Big data quality and its impact in decision making
• Calibration and validation techniques/approaches in big data
• Data fusion of multi-source and/or heterogeneous datasets
• Big data analytics in hazards and built-environment
• Big data analytics in human movements and behaviors during disasters
• Geo-visualization techniques to analyze and visualize social media data
• Privacy and big data management
• Provenance and metadata generation
• Applications of machine-learning and computer vision in disaster research
• New methods to measure social media credibility of social media content and users
• Influential social media user detection