How Good Are Modern Spatial Libraries?

• Published in: Data Science and Engineering Vol. 6; no. 2; pp. 192 - 208
• Main Authors: Pandey, Varun, van Renen, Alexander, Kipf, Andreas, Kemper, Alfons
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.06.2021; Springer; Springer Nature B.V
• Subjects: Algorithm Analysis and Problem Complexity; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data management; Data Mining and Knowledge Discovery; Database Management; Geospatial data; Image management; Image retrieval; Libraries; Management systems; Physics; Queries; Query processing; Search algorithms; Spatial data; Statistics for Engineering; Systems and Data Security; Topology; New York; Spatial; Spatial data management; Spatial libraries
• ISSN: 2364-1185; 2364-1541
• DOI: 10.1007/s41019-020-00147-9

Many applications today like Uber, Yelp, Tinder, etc. rely on spatial data or locations from its users. These applications and services either build their own spatial data management systems or rely on existing solutions. JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are some of the spatial processing libraries that these systems build upon. These applications and services depend on indexing capabilities available in these libraries for high-performance spatial query processing. In this work, we compare these libraries qualitatively and quantitatively based on four different spatial queries using two real world datasets. We also compare these libraries with an open-source implementation of the Vantage Point Tree—an index structure that has been well studied in image retrieval and nearest-neighbor search algorithms for high-dimensional data. We found that Vantage Point Trees are very competitive and even outperform the aforementioned libraries in two queries.