A Distributed Storage and Indexing Framework Based on Hierarchical Space-Time Grid Encoding for Digital Cities

Abstract

The rapid prolife ration of digital twin cities has resulted in an unprecedented increase in the volume, heterogeneity, and dynamics of space-time data, posing significant challenges to scalable storage, efficient indexing, and high-performance query processing in distributed environments. Existing distributed frameworks generally treat data partitioning and indexing as independent processes, making it difficult to simultaneously preserve space-time locality, achieve balanced data distribution, and support efficient multidimensional queries. To address these challenges, this paper proposes a distributed space-time storage and indexing framework based on hierarchical space-time grid encoding. The proposed framework integrates Hadoop distributed file system (HDFS) and HBase to establish a unified architecture for data partitioning, storage organization, indexing, and query processing. Specifically, a space-time grid–based partitioning strategy is designed to preserve space-time locality while maintaining load balance across distributed storage nodes. Furthermore, a hybrid interleaved space-time encoding scheme together with an optimized HBase RowKey is developed to construct a unified space-time index, enabling efficient space-time range queries and space-time k-nearest neighbor (KNN) queries in large-scale distributed environments. Extensive experiments conducted on the T-Drive trajectory dataset demonstrate that the proposed framework consistently outperforms several representative distributed space-time indexing approaches in terms of query efficiency, scalability, and overall system performance while maintaining stable indexing performance under increasing data volumes. The proposed framework provides an efficient and scalable solution for the management and retrieval of massive space-time data and offers a practical infrastructure for digital twin city applications.