The recent digital technological revolution has enabled the creation and collection of large, diverse geospatial data from satellite and drone images, social and news media platforms, smartphone applications, onsite or portable devices, surveillance vehicles, sensor networks, and crowdsourcing tools. These data, referred to as geospatial big data, offer a unique lens to rapidly, timely, and multi-dimensionally observe the dynamics of human behaviors, urban development, and environmental systems. Consequently, a growing interest from academia, government, organizations, and the general public leverages geospatial big data to observe the social and environmental systems and support decision-making.
Previous research and practices have successfully applied geospatial big data in solving multiple real-world challenges, e.g., disaster management, pandemic control, smart city, urban planning, precise agriculture, etc. However, existing literature also identifies limitations and new challenges in the theory, methods, and applications of geospatial big data. Initially, the definition of geospatial big data is conceptualized from the four Vs of big data, but the definition is vague and inconsistent. In addition, although the use of geospatial big data has been ubiquitous, the methodologies of collecting, analyzing, and visualizing geospatial big data, especially those newly emerged data, remain technically challenging and are usually subjectively determined by researchers or practitioners. Meanwhile, uncertainties and ethical concerns should be considered in geospatial big data and analysis methods but are paid less attention in previous studies. Finally, more interdisciplinary explorations of geospatial big data applications in different fields are needed to fully unleash their potential.
This forthcoming Special Issue invites manuscripts elaborating on the theory and analysis methods of geospatial big data and their applications in various disciplines. Specifically, potential topics include but are not limited to the following:
- Theories on geospatial big data concepts and analysis methods
- Platforms for geospatial big data collection, storage, processing, and sharing
- Innovative resources and approaches to harvesting geospatial big data
- Algorithms to fuse multi-source geospatial big data with traditional data
- Uncertainties in geospatial big data, e.g., data quality, representativeness, and biases
- Ethics in geospatial big data, e.g., privacy preservation and data sharing
- Novel geospatial big data analysis methods, e.g., GeoAI, machine learning, and deep learning
- Challenges in geospatial big data analysis, e.g., scale effects, MAUP, and MTUP
- Cutting-edge visualizations and visual analytics for geospatial big data
- Applications and case studies leveraging geospatial big data
- Reviews on geospatial big data
