Geospatially querying and analyzing large high-resolution spatial networks is critical to most of defense and security
applications to support military intelligence. However, the majority of existing solutions either store the entire
network in memory, which is not scalable, or adopt a disk-based network representation (i.e., SNDB), where routing
and spatial queries may incur high I/O overhead and hence are inefficient. In this paper, we present a flexible
architecture for large spatial network storage using quadtree. In particular, this hybrid approach preserves network
connectivity and proximity within each partition for local search while enabling heuristics to minimize the I/O
overhead for queries of large scale. We further develop efficient algorithms to process spatial queries based on this
hybrid storage schema.