Simulation of marine activities by coupling Geographical Information System and Agent Based Model: improvements and technical achievements Annalisa Minelli, Cyril Tissot, Mathias Rouan, Matthieu Le Tixerant
12 October 2016, Perugia (IT)
Outline: 1.
MAS & GIS integration
2.
The SIMARIS project: aims and description
3.
Methods: GAMA, GRASS and the Python
4.
Technical configuration of the integration
5.
Some results and conclusions
OGRS 2016, 12 October, Perugia (IT)
MAS & GIS integration: Why? ●
Simulation occurring in georeferenced environment (for MAS simulation)
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Time variable integration (for GIS modeling)
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Representation of “intelligent” spatio-temporal operators (for GIS modeling)
OGRS 2016, 12 October, Perugia (IT)
MAS & GIS integration: How? Tight coupling (*) ●
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Integration of geographical operations in MAS, but.. ○
Limited quantity
○
Quite limited performance
Integration of intelligent agents in GIS, but.. ○
Time variable issue
○
Few experiments
○
Lack of a dedicated AB infrastructure
(*) from Karadimas et al., 2006
OGRS 2016, 12 October, Perugia (IT)
MAS & GIS integration: How? Loose coupling(*) ●
Only data coming from GIS models or ABM simulation are integrated into the other system
Direct cooperative coupling(*) ●
Client-server architecture
(*) from Karadimas et al., 2006
OGRS 2016, 12 October, Perugia (IT)
MAS & GIS integration: How? Indirect cooperative coupling(*) ●
A third software structure recalls single functionalities of both simulation and geographical modeling
(*) from Karadimas et al., 2006
OGRS 2016, 12 October, Perugia (IT)
MAS & GIS integration: How? Indirect cooperative coupling(*) ●
A third software structure recalls single functionalities of both simulation and geographical modeling
SIMARIS: Simulation du déroulement d'activités marines
(*) from Karadimas et al., 2006
OGRS 2016, 12 October, Perugia (IT)
The SIMARIS model Simulation of human-environment interaction in the near sea. It is.. ●
Geographically based
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Multi-scale and multi-level
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Marine activities
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Highly automated
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Ant colony algorithm implemented OGRS 2016, 12 October, Perugia (IT)
The SIMARIS model Simulation of human-environment interaction in the near sea. It is..
It aims to.. ●
Represent simultaneously several
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Geographically based
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Multi-scale and multi-level
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Marine activities
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Highly automated
protected areas
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Ant colony algorithm implemented ●
Individuate possible conflict zones
activities ●
Evaluate impacts on marine
between activities OGRS 2016, 12 October, Perugia (IT)
The SIMARIS model
OGRS 2016, 12 October, Perugia (IT)
The SIMARIS model GAMA platform
OGRS 2016, 12 October, Perugia (IT)
The GAMA platform ●
GIS Agent-Based Modeling Architecture
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Spatially explicit and Open Source
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Models in .gaml, an high level and intuitive language
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Headless mode supported
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Multi-level and multi-scale
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Integration libraries with R, PostGres, SQLite etc.
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Many data type supported (es. OSM)
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Developed by the IRD/UPMC international research unit UMMISCO OGRS 2016, 12 October, Perugia (IT)
GRASS GIS ●
Geographic Resources Analysis Support System
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34 years of life
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Many development centres all around the world
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Many programming languages
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Over 350 modules for different purposes
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Managing, analysis, visualization, storage and creation of spatial data
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Big data support and topological 2D/3D engine
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Full temporal framework OGRS 2016, 12 October, Perugia (IT)
..all wrapped in Python
..all wrapped in Python 1.
Preprocessing of SIMARIS inputs by GRASS (no GUI): reshaping of input layers in relation to the chosen spatial extension
2.
Inputs are integrated to gama-headless within the code of the model: SIMARIS set the right spatio-temporal resolution, calibrate and runs the analysis
3.
Outputs are produced both during the simulation and at the end of the processing OGRS 2016, 12 October, Perugia (IT)
Set up the integration SIMARIS takes as input: ●
Bathymetry of the zone - required
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Fishing calendars - required
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Departing and unload ports (for each fishing activity) - required
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Fishing zones - optional
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Estimated fishing potential map - optional
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Tide levels - required OGRS 2016, 12 October, Perugia (IT)
Set up the integration SIMARIS gives in ouput: ●
Map of attendance (in terms of boat traffic)
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Map of attractiveness (using the ant colony algorithm)
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Charts of resource consumption
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Fishing balance in time
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Snapshots of maps at different timesteps
OGRS 2016, 12 October, Perugia (IT)
Set up the integration Geographical bbox of the zone
OGRS 2016, 12 October, Perugia (IT)
Set up the integration Inputs of SIMARIS model
OGRS 2016, 12 October, Perugia (IT)
Set up the integration
Path to GRASS database
OGRS 2016, 12 October, Perugia (IT)
Set up the integration
GAMA headless experiment configuration files
OGRS 2016, 12 October, Perugia (IT)
Set up the integration Geographical bbox of the zone Inputs of SIMARIS model
Path to GRASS database
Model output
GAMA headless experiment configuration files
OGRS 2016, 12 October, Perugia (IT)
Simulation description Elaboration details: ●
~40 km2 in the Brest bay (Brittany region, France) Spatial resolution = 20 mt
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9 boats for 3 types of fish (King Scallop, algae and clams)
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Simulation over four months (fishing season - sept/dec) Temporal resolution = 30’
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Shapefile of fishing zones given OGRS 2016, 12 October, Perugia (IT)
Simulation description Studied zone:
~40 km2 ●
Iroise Sea, Brest Bay (France)
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Marine Protected Area
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Many human activities in the near sea (naval military base, commerce, stakeholdes, plaisance etc.) OGRS 2016, 12 October, Perugia (IT)
Simulation results From snapshots: Animation showing boats operating in the fishing zones and tide evolution in time (timestep = 30’)
OGRS 2016, 12 October, Perugia (IT)
Simulation results From shapefiles: Animation of most frequented zones in time (capture rate = 1 week)
OGRS 2016, 12 October, Perugia (IT)
Simulation results Resource consumption and regeneration in time (king scallop example)
OGRS 2016, 12 October, Perugia (IT)
Simulation results Resource consumption and regeneration in time (king scallop example)
Growth rate decreases in the adult phase of King Scallop life
OGRS 2016, 12 October, Perugia (IT)
Simulation results Fishing balance
OGRS 2016, 12 October, Perugia (IT)
Conclusions Strength points: ●
Rapid execution of the analysis since no GUI is loaded
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Better performance in leaving geographical operations executed by GIS and simulation executed by MAS (time and quality of the output)
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Good automatisation level guaranteed by the Python script
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Easiness in configuration for a future WPS
OGRS 2016, 12 October, Perugia (IT)
Conclusions Points to improve: ●
Better connection with a specific library (GAMA to GRASS or viceversa)
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GAMA headless configuration is a little complicate (a lot of different configuration files)
Ongoing work: ●
Generalisation of the model (more maritime activities)
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Strengthening of the multi-level infrastructure OGRS 2016, 12 October, Perugia (IT)
Thanks to you all for the attention
[email protected] All the software is available at: https://github.com/annalisapg/maritimeSimulation
OGRS 2016, 12 October, Perugia (IT)