Biospytial is a modular open source knowledge engine designed to import, organise, analyse and visualise big spatial ecological datasets using the power of graph theory. It handles
species occurrences and their taxonomic classification for performing ecological analysis on biodiversity and species distributions.

The engine uses a hybrid graph-relational approach to store
and access information linked with relationships that are stored in a graph database,while tabular and geospatial (vector and raster) data are stored in a relational database management system (Postgis 9.x) . The graph data structure provides a scalable design that eases the problem
of merging datasets from different sources.

The linkage relationships use semantic structures (objects and predicates) to answer scientific questions represented as complex data structures stored in the graph database.

Biospytial comprises three interconnected components:

  1. Geospatial Processing unit (GPU) supported by a RDBMS with geoprocessing capabilities
  2. Graph Storage and Querying Unit (supported by Neo4J)
  3. A graph-relational package, The Biospytial Computing Engine
    (BCE) that integrates all the system’s components. It also includes tools like: interactive notebooks

Software availability

The project is hosted in here. .

Instructions for installing and running the engine are located in the project’s homepage. The container images are located in the public Docker Hub registry (docker pull [URL] ). The available URLs are:

Data availability

The data for running the example is located here PUT ADDRESS HERE

The data is a compressed file that includes the data for both databases, the RDBMS and the Graph database.

Videos and examples

This is a video I made for the GBIF Young Researchers Award 2016 explaining some of its functionalities.

Short version



Biospytial: spatial graph-based computing for ecological Big Data Juan M Escamilla Molgora, Luigi Sedda, Peter M Atkinson GigaScience, Volume 9, Issue 5, May 2020, giaa039,

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