Geometry and Proximity Constrained Query Evaluations over Large Geospatial Datasets Using Distributed Hash Tables

Data volumes in the geosciences and related domains have grown significantly as sensing equipment designed to continuously gather readings and produce data streams for geographic regions have proliferated. The storage requirements imposed by these datasets vastly outstrip the capabilities of a single computing resource, leading to the use and development of distributed storage frameworks composed of commodity hardware. In this paper, we explore the challenges associated with supporting geospatial retrievals constrained by arbitrary geometric bounds, geographic proximity, and relevance rankings. Our solution involves the use of a lightweight, distributed spatial indexing structure, the geoavailability grid. Geoavailability grids provide global, coarse-grained representations of the spatial information stored within these ever-expanding datasets, allowing the search space of distributed queries to be reduced by eliminating storage resources that do not hold relevant information. The index can also be used in non-distributed settings, and performs competitively with other spatial indexing technologies.