Multi-Agent and Population-Based Geo

Multi-Agent and Population-Based
Geo-Simulation for Decision Support:
Past Works and Prospects
Dr. Bernard Moulin
Laval University
Computer Science and Software Engineering Department
Pouliot Building, Quebec G1V 0A6, Canada
Tel. (1) 418 656 5580 Fax (1) 418 656 2324
[email protected]
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Outline
Multi-Agent Geo-Simulation (MAGS)
Multi-Actor Dynamic Spatial Situations (MADSS) and Decision Making
Retropective of our Geoide Projects on MAGS
From MAGS to MUSCAMAGS to CrowdMAGS to the IVGE Project
The COLMAS Project and Nsim Technology’s GeoSDK Platform
Retropective of our Geoide Projects on Population-Based Geosimulation
The VNO-MAGS Project : Geosimulation of the West Nile Virus Spread
Needs and Challenges for the Geo-Simulation of Zoonose Spread
Main Ideas of the ZoonosisMAGS Project
ZoonosisMAGS (our software suite) and the Simulation of Lyme Disease
Potential for Future Works
Conclusion and Acknowledgments
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Multi-Agent Geo-Simulation (MAGS)
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‘Geosimulation deals with the construction of different kinds of
spatial models in order to study spatial-temporal phenomena while
developing software to support ‘actor-based simulations’
Multi-Agent Geo-Simulation (MAGS) takes advantage of the
coupling of Multi-Agent-Based Simulation (MABS) and
Geographic Information Systems (GIS)
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Multi-agent geo-simulation (MAGS) can be effectively used to
simulate complex systems in virtual geo-referenced environments
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MAGS tools/approaches used to support decision makers for:
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the creation of action plans and the assessment of these plans and of their
impacts on actors’ mobility and decision making
the comparison of intervention scenarios
to help visualize the outcomes of action plans
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Multi-Actor Dynamic Spatial Situations

A Multi-Actor Dynamic Spatial Situations (MADSS) involves
a large number of actors of different types (human, animal,…) acting in
geographic spaces of various extents
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MADSSs need to be monitored to insure :
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human security and equipment preservation in case of natural or manprovoked hazards (flood, earthquake, wildfire, oil slicks),
the respect of public order (population evacuation, crowd monitoring and
control, peace-keeping, etc.)
the adequate use of infrastructures (monitoring of people and households
transportation and shopping habits in a urban area to better plan transportation
infrastructures, location of services’ retailers, etc.)
Impact of emergency response plans
Decision makers need an overall understanding of the
situation to monitor its evolution, to develop strategies to
adequately intervene, to develop and compare alternative
intervention scenarios and to anticipate the consequences of
these interventions
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Decision Making and MADSS
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Most critical MADSSs involve large populations and/or
important infrastructures/equipments that are usually spread out
on large geographic spaces
Natural hazards or man-provoked hazards can trigger MADSSs
that may endanger human lifes, threaten the equilibrium of ecosystems and/or the preservation of equipment & infrastructures
Decision makers need tools to simulate such MADSSs in order
to anticipate how they may evolve and to possibly assess the
impacts of their decisions
Systems based on Multi-Agent Geo-Simulation (MAGS) may
provide some support to decision makers, either by training them
before the occurrence of such situations or by providing them
with simulations during the evolving MADSS
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Retrospective of our Projects on Multi-Agent
Geosimulation for Decision Support
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The MAGS and MUSCAMAGS Projects
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We developed the MAGS Platform (2002 to 2005) and used it in
several multi-agent geosimulation projects (crowd simulation,
consumer spatial behaviours in malls, Fire control, etc.)
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To MallMAGS (2005) and SOLAP Tools and ...
Simulation of Dynamic
Environments (FireMAGS
2006)
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The COLMAS Project
and Nsim Technology’s GeoSDK Platform
(Nsim Tech: Perron, Hogan; RDDC: Berger, Bélanger)
Nsim Technology, a start-up company created by the two Jimmys (Perron, Hogan)
in 2004 and benefiting from a technological transfer of the MAGS project
The COLMAS Project is an example of a joint project: Use of Multi-Agent
Geosimulation and Machine Learning for Distributed Continuous Planning in
the Case of Territory Surveillance using Unmanned Autonomous Vehicules
Various solutions found from the best one to the
worst one (SDKsim –COLMAS)
UAVs’ patterns for scenario
with obstacles
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Nsim Technology
NSim Technology offers products and services
in the fields of geographic system management
simulation and intelligent spatio-temporal analysis, artificial
intelligence and 2D/3D visualization
NSim provides services and a development environment (GeoSDK) to
its clients, especially in the defense and security sectors
NSim recently developped an innovative technology, Nsim Contour,
offering features improving collaborative client-server geosimulation
and 2D/3D geographic management systems
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From MAGS (2004) to CrowdMAGS (2009)
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The IVGE Project:
Informed Virtual Geographic Environment
Method
Vector layers selection
GIS
Elevation map
Spatial decomposition
Elevation
map
Merged
semantics map
(2.5D)
(2D)
Maps unification
Elevated merged semantics map
IVGE
Topological graph usage
Different layers from GIS data (with
different semantics)
3D Map with merged semantics
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Applications of the IVGE for path planning in urban and
natural environments, deployment of sensors, etc.
Mehdi Mekni won the William L. Garrisson Price for the Best 2011 PhD Thesis in
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Computational Geography (American Association of Geographers AAG)
Retrospective of our Projects on PopulationBased Geosimulation for Decision Support
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Population-Based Geosimulation Projects
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VNO-MAGS Project to simulate the propagation of West Nile Virus
(2006-2010) financed by INSPQ and part of the Geoide-supported
MUSCAMAGS Project
The LymeMAGS Project (2009-13 - Financed by INSPQ and Ministère de
la santé et des services sociaux du Québec), part of the Geoide-supported
CODIGEOSIM Project (PIV 05, J. Wu Project Leader)
Simulation of the propagation of Lyme disease (ticks - rodents – Deer
interactions) for decision support in Public Health
The ZoonosisMAGS Platform: a generic platform to create populationbased geo-simulation of zoonoses (2010-13)
The SénartMAGS Project (agent-based geosimulation) of the risk of Lyme
Disease in peri-urban parks (Collaboration with Godard’s team of Paris 8
University) (2009-2012)
Note on Zoonoses : infectious diseases transmitted by insects to animals and
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to humans. There are numerous zoonoses such as Malaria, West Nile
Virus, Lyme disease, etc.
The VNO-MAGS Project: Geosimulation of
the West Nile Virus Spread
We simulate the dynamics of
the populations of Culex
mosquitoes and of crows and
their interactions which favor
the West Nile Virus infection
spread
Helping decision makers explore
different intervention scenarios
under various weather conditions
(temperature, rain falls)
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Needs and Challenges
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There is a need for flexible tools to understand the ‘dynamics’
of disease propagation in GIS … which raises challenges:
How to model huge populations and their interactions taking
into account geographic characteristics (i.e. land-cover in
relation to habitat suitability)?
How to efficiently simulate these interactions as well as species
evolution (especially for insects such as mosquitoes and ticks),
and animals’ displacements?
How to calibrate these models (data availability)?
How to develop friendly tools to help decision makers
understand the phenomena (visualization) and to assess different
scenarios (climate, interventions)
How to couple spatial and statistical analysis tools to support 17
these assessments?
Main Ideas of the ZoonosisMAGS Project (1/2)
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Create an virtual geographic environment (VGE) whose
cells correspond to habitat patches influencing species’
biology (suitability)
Associate with each cell the populations of the involved
species at different stages (i.e. evolution, infection)
Model the evolution and interactions of these populations
using a dynamic compartment model taking into account
environmental parameters (i.e. temperature, geographic
characteristics)
Enhance the VGE with information related to species’
mobility behaviors (migration, emigration, dispersal)
 Informed VGE
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Main Ideas of the ZoonosisMAGS Project (2/2)
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Enhance the compartment model with ‘transfer
compartments’ and mobility transitions
Implement this model as efficient state-transition
mechanisms in the geo-simulator exploiting the IVGE
Develop a prototyping tool (Matlab) for model
exploration and testing and a C++ geo-simulator for fullscale geo-simulations
The objective is to enable Public Health Officers to :
1) Analyze the spatial-temporal characteristics of the spread of a
zoonose
2) Specify different climate and/or intervention scenarios and
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compare their outcomes
Scenario
Specification
User
P2
Specify
Scenarios and
initialize data
A04
IVGE
A04.1
Cell hierarchies
data, cell’s
neighboors
A04.2
Population
simulation data
A04.3
Other data(eg.
Temperature,
etc.)
P1
A01
Simulation
engine
Scenarios
P4
P3
IVGE generator
A02
A03
Simulation
Output
Calibrated
Data
P5
P6
Analyse Data
Calibrate
Models
Using formalism, specifiy
populations’ evolutions and
interactions
Data Base
A06
A07
A08
Other data
Input(eg. Weather
data, Land cover)
Population data
input
Environment
Input
Zoonosis-Mags Data Base
A09
P7
Analysed
Data
Simulation
Results
Analysis
Model
Specification
A05
Mathematical Model
Data & Other
Zoonosis Data
IVGE
Prepare other Data
A010
Monitoring
Data
P8
Prepare Environment
Data
A011
A012
Other Data
GIS Data
Monitoring
System(s)
Analyst/
Designer
Multiple Data
Sources
Geographic
Information System
Other Data Base
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E.g : SIDVS-WNV
E.g : Population data;
Weather data;
Parameters of the used
Models.
Enhanced Compartment Models
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2
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Overview of our Software Suite
Geobase
Data
Matlab
Simulator
Other data
Sources
Our Matlab
Simulator
Mlb Simul.
Outputs
(Excel)
Outputs
Analysis
(Excel/Mlb)
IVGE
Creator
User
Matlab
Interpreter
IVGE
IVGE Mlb
Loader
IVGE Data (Cells, Neighbors,
Suitability, Orientation, Transfer att.,
Distance-to-cross, etc.)
C++
Simulator
Model Specif.
(C++)
C++ Simulator
Interface
GIS data
Population
data
Other data
User
Scenario
(C++)
Z-MAGS
Database
(PostgreSQL)
Model Creator
(XML)
C++ Simul.
Outputs
ZoonosisMAGS
C++ Simulator
Database
Creator
(C++)
User
Model Specif.
(XML )
Mlb Simul.
Interface
Model Graphical
Interface (Visio)
C++ Outputs
Analysis
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User
The simulation of Lyme Disease
Scenarios data source
Data output, Log & Timeline
Models & Phenomena
Results
23
IVGE displayed in the C++ geosimulator
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Released soon
in 2014
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Conclusion
So much work !! …. So much fun ! … So many results! ….
So many students trained in various innovative areas !
Many thanks to Geoide that offered us all these
opportunities
Many thanks to our partners for supporting us in so many
ways …
and ....
Nothing of this would have happen without CRG .... being
the leader who created this excellent NCE: GEOIDE
Happy 25th Anniversary !!!!
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Acknowledgements
A big THANK-YOU to CRG and Geoide and to all the partners who
supported our projects during the past 15 years : Alberta Sustainable
Resource Development, Center for Spatial Analysis at McMaster
University, DRDC Valcartier, Institut National de Santé Publique du
Québec, Joint Program in Transportation (University of Toronto),
Ministère de la santé et des services sociaux du Québec, Ministère des
ressources naturelles et de la faune du Québec, Ministère des
transports du Québec, NSim Technology, PROCESSUS Research
Network, SOPFEU, Sûreté du Québec, Time Use Research Program at
St Mary’s University (Halifax), Service de police de la Ville de Québec
I warmly thank all the people who were involved in these 12 years of
research: Postdoc fellows and PhD students: A. Ali, M. Bouden, W.
Chaker, T. Garneau, E.F. Gbei, H. Haddad, M. Mekni, S. Paris, N Sahli;
MSc students: F. Bellafkir, M. Bouden, W. Chaker, C. Drolet, J. Gancet,
A, Kabli, B. Larochelle, F. Legault, F. Manirakiza, B. Mehdi, D. Navarro,
J. Perron, P. Pelletier, F. Rioux, O. Rouleau, S. Sedrati; Research
Professionals: H. Hogan, W. Chaker, M. Bouden, D. Marcotte.
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Acknowledgements
Another Topic on e-Governement
(New Book released May 2014)
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