By Shahabuddin Amerudin
Abstract
In recent times, the realm of spatial data science has witnessed an unprecedented surge, propelled by the exponential growth of spatial data and its potential applications across diverse domains. This review article delves into the multifaceted world of spatial data science, spanning its foundational principles, practical applications, inherent challenges, and the evolving research trends that are shaping its trajectory. By exploring the intricate interplay of spatial data, complexities, and novel methodologies, this review aims to provide a holistic understanding of this dynamic and interdisciplinary field.
Unveiling the Essence of Spatial Data Science
The advent of the digital age has ushered in an era of unprecedented data generation and availability. In response to this data deluge, spatial data science has emerged as a multidisciplinary discipline, seamlessly integrating methodologies from computer science, statistics, mathematics, and various specialized domains. This holistic approach is harnessed to acquire, store, preprocess, and unearth previously obscured insights from spatial data. The lifecycle of spatial data science encompasses five vital stages, namely spatial data acquisition, storage and preprocessing, spatial data mining, validation of outcomes, and the interpretation within the specific domain. Across various sectors, ranging from national security and public health to transportation and public safety, the pivotal role of spatial data science in shaping informed decisions and policies is increasingly evident.
The Landscape of Challenges in Spatial Data Science
The interdisciplinary essence of spatial data science brings forth a spectrum of challenges that must be effectively navigated. Its core engagement with tangible objects and phenomena necessitates a profound grasp of the underlying physics or theories within the pertinent domain, resulting in results that are not only interpretable but also trustworthy. The complexities posed by diverse spatial data types—ranging from object data types (such as points, lines, and polygons) to field data types like remote sensing images and digital elevation models—exceed those found in non-spatial data science. Further complexity arises from the distinctive attributes of spatial data, including spatial autocorrelation and heterogeneity. Tobler’s first law of geography—asserting that “everything is related to everything else, but near things are more related than distant things”—pervades spatial phenomena and influences analyses. The transition from discrete data inputs to continuous spatial datasets introduces an added layer of intricacy, rendering conventional non-spatial methods less applicable.
Navigating Emerging Research Trajectories in Spatial Data Science
This review article spotlights the emerging frontiers steering the evolution of spatial data science research. A key trajectory revolves around the integration of spatial and temporal information in observational data, unlocking new dimensions of understanding spatiotemporal patterns, associations, tele-coupling, prediction, forecasting, partitioning, and summarization. Expanding the realm of exploration, spatial data science is making strides within spatial networks. Cutting-edge methodologies, such as network K function and network spatial autocorrelation, are being developed to tackle spatial network data challenges. Innovations extend to the resolution of intricate puzzles like the linear hotspot discovery problem within spatial networks. An exciting avenue unfurls with spatial prediction within spatial networks, utilizing the wealth of information from GPS trajectories and on-board diagnostics (OBD) data collected from vehicles. Pioneering work by Li et al. (2018, 2019 and 2023) introduces an energy-efficient path selection algorithm grounded in historical OBD data.
Charting the Course Forward
As spatial data science continues to evolve, its centrality in diverse sectors remains pivotal. The capacity to extract actionable insights from spatial data empowers decision-makers to reimagine how they perceive and address challenges across domains. Yet, the enduring interdisciplinary nature and intrinsic attributes of spatial data pose ongoing challenges that require thoughtful consideration. By embracing these challenges and capitalizing on emerging trends, spatial data science stands poised to redefine the manner in which spatial information is harnessed. This review endeavors to guide both researchers and practitioners in navigating the intricate terrain of spatial data science, offering insights into its foundation, applications, challenges, and future horizons.
References
Li, Y., Shekhar, S., Wang, P., Northrop, W.: Physics-guided Energy-efficient Path Selection: A Summary of Results. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’18, pp. 99–108. ACM, Seattle, WA, USA (2018). https://doi.org/10.1145/3274895.3274933
Li, Y., Kotwal, P., Wang, P., Shekhar, S., Northrop, W.: Trajectory-aware Lowest-cost Path Selection: A Summary of Results. In: Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD ’19, pp. 61–69. ACM, Vienna, Austria (2019). https://doi.org/10.1145/3340964.3340971
Li, Y., Xie, Y., Shekhar, S. (2023). Spatial Data Science. In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-24628-9_18
Suggestion for Citation: Amerudin, S. (2023). Navigating the Expansive Horizon of Spatial Data Science. [Online] Available at: https://people.utm.my/shahabuddin/?p=6707 (Accessed: 21 August 2023).