Geosimulation
• Geosimulation models are developed to represent phenomena that occur in urban systems in highly realistic manner
• In particular, Cellular Automata and Multiagent based geosimulations operate with:– entities representing human individuals and
infrastructures (e.g. households, homes, urban services, streets etc.);
– Properties and behaviour of spatial entities;– Spatial relationships between entities;– External influences.
Geosimulation– The modeled entities are associated to
georeferenced objects;– Properties and phenomena at higher level of
geographic representation emerge as the product of the dynamic interaction between spatial entities that form the building blocks of the simulation model
MAGIMultiAgent Geosimulation Infrastructure
• Software environment for the design, development and execution of Geosimulation applications
• Developed by LAMP – Laboratory of Analysis and Models for Planning – University of Sassari (Italy)
Main features of MAGI• Enables for the development of a wide range of
geosimulation models• The resulting models can be integrated with different
computational approaches (both on-line and off-line coupling are allowed)
• Offers an effective graphical user interface
Simulation monitoring Simulation steeringModel development support
Main features of MAGI
• Development phase:
• Simulation phase:
Different models
(on-line and/or off-line coupling with MAGI)
Outcomes for post-processing
MAGI main ingredients• Portable C++ libraries:
– GEOS (Geometry Engine Open Source)– Leaf-prior Update R-Tree (spatial indexing for moving
objects)– OpenTreads (Threads management)
• Windows Development and Simulation Environment:– MinGW (Minimalist GNU for Windows – C++ open
source development tool)– GIS Import/Export libraries (some raster and vector
formats are currently supported: GeoTiff, MID/MIF, Shapefile)
GEOS• GEOS is an API of 2D spatial predicates and
functions
• It has the following design goals: – It conforms to the Simple Features Specification for
SQL published by the Open GIS Consortium – It provides a complete, consistent, robust
implementation of fundamental 2D spatial algorithms
– It is fast enough for production use – It is written in C++ – It is open source
GEOS & MAGI• Each agent is associated to a geometry
Point LineString LinearRing Polygon
• In addition, each agent has:– Properties (i.e. C++ objects, representing integer, real
numbers, vectors, list, graphs, etc., which define the agent’s state)
– Behavioral rules (e.g. for updating the agent’s properties or geometry)
– Neighbor lists, which can be dynamically updated (lists play a role like neighborhoods in Cellular Automata models)
MAGI Agents
• Agents can represent static geographic objects…
• …or cells – this enables the development
of Cellular Automata models
MAGI Agents
• Agents can move in and interact with the environment
Layers• Agents are organized in layers
Layer parameters are variables accounting for relevant properties which can influence the agents’ behaviour
Layer functions can be defined and scheduled for execution during simulations (e.g. layer functions can update layer parameters)
Global parameter and functions are also allowed (e.g. a parameter may affect more than one layers)
Neighbor lists• Each agent can hold one or more neighbor list (i.e. the lists
of objects of interest for the agent)– each list has a target layer which may be different from the layer to
which the agent belongs
• MAGI allows:
Standard CA neighbourhoods
(static)
Query-based
neighbor lists
(dynamic)
Custom neighbourhoods
(static)
On regular tessellations
Query-based neighbor list
• MAGI class library includes a spatial indexing specific for moving objects (i.e. the Leaf-prior Update R-Tree) which makes efficient both the execution of spatial queries and the index updating operations (required when an agent change its position in space)
– Nevertheless, spatial queries are inherently computationally expensive when involve the evaluation of spatial relationships!
• A neighbor list can be defined by a spatial query which is iterated during the simulation with a specified frequency
Binary Predicates• MAGI supports, through GEOS, a complete
set of geometric binary predicates.
• Binary predicate methods take two geometries as arguments and return a boolean indicating whether the geometries have the named spatial relationship.
• The relationships supported are: equals, disjoint, intersects, touches, crosses, within, contains, overlaps
• Binary predicates can be used by Agent’s behavioral rules and spatial queries for neighbor lists’ updating, in order to simulate spatial perception and reasoning
B
A
Agent behaviour• The agent behaviour is defined by actions• Actions can update the state of the agent itself, the state
of the environment or the state of other agents– a model may include ‘degenerate’ agents without behavioral
rules (e.g. as part of the agents’ environment)
Scheduling• The simulation proceeds in time-steps• Two main types of scheduling:
– Simultaneous updating: agents are assumed to change simultaneously (like cells’ states in Cellular Automata)
• conflicts can arise when agents compete over limited resources
– Sequential updating: agents’ states change in sequence (each agent observes the reality left by the previous one)
• conflicts between agents are resolved but the order of updating may influence results.
• Sequential random updating is also available;
What kind of agents are they?
• Basically, hybrid reactive - deliberative agents:
– When agent A 'receive' a message from agent B, it can only react immediately with a standard response (which is known by B)
– On the other hand, agents can perceive and modify the environment, they can have an internal state (MAGI library allows any kind of C++ object as agent state) and they can execute complex decision algorithms before acting.
• This kind of agent proved to be suitable for the vast majority of geosimulation applications where many ‘simple’ agents determine the emergence of a complex behaviour
MAGI GUI
Model structure editing
Editing of behavioral rules and queries
Model building Model execution
MAGI GUIXML model structure editing
MAGI GUI• MAGI provides interfaces for exchanging raster
and vector spatial data with GIS
Samples are mapped into agent properties
MAGI GUIParameter monitoring and editing
A model step-by-step
– Number and type of global parameters
– Global functions– Layers
Step 1: definition of model structure
– For each layer:• Name• Type of agent
activation • Type of boundary (e.g.
toroidal space ?)• Type of entities (e.g.
cells ?)• Number and type of
layer parameters• Layer functions
A model step-by-stepStep 2: definition of agents
– Type of geometry– Structure of agent’s state (number
and type of properties)– Actions defining agent’s behaviour
(C++ source code)– Number and type of Neighbor lists
• spatial queries in case of query-based lists
A model step-by-stepStep 3: model plug-in generation
3.1 model source code generation
3.2 compile and linking
Step 4: model execution
4.1 setting-up an initial scenario
4.2 run the model
Under development:
• Multi-threading based concurrent computation– for exploiting the multi-core processor architectures (the current
trends in processor technology indicate that the number of processor cores in one chip will continue to increase)
• Model debugging capabilities– At the moment advanced debugging can be done in an external
environment (the open source IDE CodeBlocks)
• OpenGL visualization capabilities• Tests, documentation and examples• Archive of pre-built models• Utilities for “dummy” users