Lifemapper 2.0

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Lifemapper 2.0

• Using and Creating Geospatial Data and Open
  Source Tools for the Biological Community

Aimee Stewart, CJ Grady, Dave Vieglais, Jim
Beach Natural History Museum and Biodiversity
Institute University of Kansas
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Overview

• Overall Goals
• History
• Current version
• Implementation
• Future

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Niche Modeling

• Yeah yeah yeah
• Data
• Environmental
• Occurrence
• Computationallimitations

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Lifemapper 1.0

• NSF funded
• Experimental app.
• Successful DC project
• Enthusiastic users
• Limited by
• Data quality
• Architectural decisions

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Lifemapper 2.0

• Demo pipeline processing specimen data from GBIF
  cache
• Funded by NSF/EPSCoR
• Simpler, controlled architecture
• Goals
• On demand computation
• Model archive
• Data and analysis service

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Components

• Cluster
• Spatial datalibrary (SDL)
• Workflow controller
• Open-source
• Python

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Operation

• Client
• retrieves point data
• constructs request
• sends job to cluster by REST
• Cluster front end receives /schedules job
• Cluster nodes
• retrieve environmental data
• dispatch job to OM
• Client
• polls for status
• retrieves and stores model/projection

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Data

• Environmental data
• URL in job, retrieved via WCS by node
• Caches on nodes for efficiency
• Point data
• Could be REST or WFS URL
• Result data
• Model (ruleset) stored on file system
• Projection (raster map) registered in SDL

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Cluster

• 64 node, 128 processors
• 2 TB storage
• NPACI Rocks
• Sun Grid Engine scheduler
• HTTP REST service
• Run openModeller (GARP or other algorithm)
• Get status
• Get result data

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Spatial Data Library

• Mapserver with custom python WS
• Layer metadata in PostGIS
• Independent service - so could
• be standalone
• be one of multiple SDLs servicing pipeline
• Will have
• search/query webservice
• browsable web interface

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Workflow Controller

• Could simply generate jobs so easy to
  integrate
• Currently
• Harvests from GBIF
• Generates jobs per species
• Reproduce LM1 but with refined data and scalable
  system

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Overall system

• Standalone system
• Only outside connection is REST service
• Easily moved to smaller/larger system
• Or multiple systems for failover for high demand
• Interface - easier than existing SOAP
• Designed to provide high throughput, not rapid
  evaluation of single model
• Open Source Software

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Implementation Status

• Core components operational
• harvest data
• generate jobs
• output projections
• store back to SDL
• No user interface yet
• WS so existing viz solutions easy

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What does the future hold?

• Fine-tune
• Taxonomicresolution
• Data cleaning
• Multiple
• algorithms
• projection scenarios
• Analysis services

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Acknowledgements

• Funding
• NSF Award (EPSCoR 0553722)
• Kansas Technology Enterprise Corporation
• openModeller
• CRIA and more
• Original GARP
• David Stockwell, SDSC
• Environmental data
• Climate Research Unit
• Int. Panel on Climate change
• Normalization BDWorld, Tim Sutton, Pete Brewer
• GBIF and contributing collections
• Lifemapper1 Team, especially Ricardo Pereira

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Questions?