M. Naser Lessani | March 18, 2024
The GIBD research lab has organized a series of virtual sessions on geospatial big data and spatial computing for the 2024 AAG Annual Meeting. The themes of these sessions are varied and cover a wide range of topics, including computing with large geospatial datasets, urban sensing through geospatial big data analytics, analyzing human mobility and health outcomes using integrated geospatial methods, and leveraging social sensing and geospatial big data analytics for disaster management. These sessions aim to provide a thorough understanding of how geospatial big data analytics and approaches can be applied across various disciplines within geography and public health domains. The selected presenters in these sessions will offer insightful perspectives from various disciplines.
- Understanding urban and social sensing for sustainable environments
Cities are intricate entities where urban life thrives within dynamic patterns and structures. To enhance urban living, there is a crucial need for urban sensing, which employs technologies to monitor and gather real-time data on city dynamics and human behavior. This approach is central to developing thriving, sustainable, and fair urban environments. The evolution of geospatial big data, including various forms of remote sensing and social media data, plays a pivotal role in urban sensing by providing extensive observations essential for understanding urban phenomena. Advances in GeoAI and AGI have significantly improved our ability to extract valuable information from these large datasets, addressing numerous technical challenges in urban sensing. Similarly, in disaster management, the effectiveness of responses hinges on the ability to make timely, well-informed decisions using real-time data. Social media and crowdsourced information have emerged as vital resources, transforming citizens into a widespread sensor network that provides immediate, site-specific details during emergencies. This citizen-sensor approach, complemented by big data computing, enables rapid, comprehensive assessments of disaster impacts, showcasing the power of integrating geospatial big data and AI to enhance both urban living and emergency response strategies.
- Urban Sensing and Understanding via Geospatial Big Data and AI (click here)
- Social Sensing and Big Data Computing for Disaster Management (click here)
- Understanding human mobility and health outcomes through geospatial big data analytics
The interplay between human mobility and public health has become increasingly significant in recent years, particularly highlighted by global challenges such as the COVID-19 pandemic. Understanding patterns of human movement is crucial for a range of applications, from urban planning and environmental management to the control of infectious diseases and disaster response. The availability of extensive geospatial big data from social media and mobile devices offers unprecedented opportunities to analyze and model human behavior on a large scale. However, this also presents challenges, including privacy concerns and the computational complexity of processing vast datasets. Advances in technology, particularly in GIScience, big data analytics, and artificial intelligence, are crucial for unlocking the potential of this data, enabling stakeholders across various sectors to make informed decisions and adapt to rapid changes in human behavior and environmental conditions. The importance of these topics lies in their ability to inform public health strategies, improve urban infrastructure, and enhance our response to environmental challenges and health emergencies.
- Geospatial Big Data for Analyzing and Understanding Human Mobility Patterns (click here)
- Integrative Approaches to Understanding Human Mobility and Health Outcomes (click here)
| Sean Reid | Neighborhood context and HIV risk hotspots in Los Angeles County |
- Big Data Computing for Geospatial Applications
The convergence of big data and spatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. Earth observation systems and model simulations are generating massive volumes of disparate, dynamic, and geographically distributed geospatial data with increasingly finer spatiotemporal resolutions. Meanwhile, the ubiquity of smart devices, location-based sensors, and social media platforms provide extensive geo-information about daily life activities. Efficiently analyzing those geospatial big data streams enables us to investigate complex patterns and develop new decision-support systems, thus providing unprecedented values for sciences, engineering, and business. However, handling the “Vs” (volume, variety, velocity, veracity, and value) of geospatial big data is a challenging task as they often need to be processed, analyzed, and visualized in the context of dynamic space and time. This section aims to capture the latest efforts on utilizing, adapting, and developing new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges for supporting geospatial applications in different domains such as climate change, disaster management, human dynamics, public health, and environment and engineering.
- Big Data Computing for Geospatial Applications 1 (click here)
- Big Data Computing for Geospatial Applications 2 (click here)
- Big Data Computing for Geospatial Applications 3 (click here)
- Big Data Computing for Geospatial Applications 4 (click here)
- Speakers from GIBD research lab
- Estimating hourly neighborhood population based on smartphone-based human mobility data (Huan Ning, Zhenlong Li, Manzhu Yu) (click here)
- Optimizing Geographical Weights in GWR Model by Incorporating Neighborhood Characteristics (M. Naser Lessani, Zhenlong Li) (click here)
- Using smartphone-based place visitation big data to improve health measure estimation (Temitope Ezekiel Akinboyewa, Huan Ning, Zhenlong Li) (click here)