Applications of geospatial big data in the Internet of Things

Trajectory data, increasingly available due to location tracking technologies, holds immense potential for intelligent traffic management and urban planning. Traditional 'attractive region' mining methods often rely on density-based clustering, neglecting the inherent path information within trajectories. To address this, we propose a novel graph-based approach for attractive region discovery. By transforming trajectory data into graphs, we effectively leverage path and connectivity information for clustering with locality-sensitive hashing. Our study introduces the pARM, pgARM, and hgARM algorithms, demonstrating their superiority over GridDBScan through experiments on real-world datasets. We employ Davies–Bouldin metric and visualization techniques to highlight the robustness of our approach, especially for datasets with varied degree distributions. Although our method may have slightly longer processing times for smaller grid sizes, it achieves execution times comparable to GridDBScan for larger grids. We rigorously analyze performance variations within our algorithms using execution time, clustering coefficient, and modularity scores, providing guidance for their optimal application. 

Trajectory-based clustering for enhanced attractive region mining in urban taxi services

With the development of IoT techniques, urban computing has become an emerging topic in academia and industry. The goal of urban computing is to address some issues of urban planning by using the big data generated in urban facilities. The massive trajectory data processing is viewed as an important issue in urban computing. To satisfy the storage and processing requirements of massive trajectory data, a distributed system is usually adopted. However, existing distributed systems face challenges of data locality aware partitioning and various trajectory queries. In this paper, we propose a distributed framework of massive trajectory data analysis based on HBase, to realize spatio-temporal query more effectively. We first design a temporal-based pre-partitioning strategy to improve the performance of data written. Then we develop a Multi-Level Index to speed up the process of spatio-temporal query. Extensive experiments on real trajectory datasets demonstrate that the proposed framework significantly improves efficiency and usability.

An Effective Spatio-Temporal Query Framework for Massive Trajectory Data in Urban Computing

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An Effective Spatio-Temporal Query Framework for Massive Trajectory Data in Urban Computing