GIS & AGENT BASED MODELLING
Agent based modelling (ABM) is a computer simulation process used extensively to understand complexity arising from autonomous agents interacting with multiple independent variables. Geospatial Agent based modelling is a growing field that integrates ‘real’ landscape spatial data as the basis for agent interaction The rules sets governing ABM behaviour can be relatively simple however through interaction with multiple spatial ecological data sets complex ecological processes can be simulated. Following are some examples of GIS-Agent Based Model integrations used for teaching and research.
Agent-based models of fire spread have been developed focusing on the fire management needs of northern Australia, these 'Incendiary' fire behaviour visualisation management models are not attempting to predict fire spread but rather provide a useful teaching and planning tool when thinking about fire management operations. The Incendiary models provide a virtual lab to explore fire management scenarios, to facilitate discussion between different land managers and across cultures and build common understandings related to key fire management issues in Northern Australia..
The Incendiary models aim to:
Facilitate the broader use of available fire-related data sets for understanding fire and ecosystems in Northern Australia
Allow this visualisation of fire behaviour in relation to a range of relevant landscape and fire weather variables. (ie fuel loads, fuel types, curing, wind speed, wind direction, temperature and humidity).
Support the communication of fire management objectives to a culturally diverse range of land managers.
Agent-based modelling allows for the simulation of complex systems integrating spatial data to explore problems of geography, the environment, planning and social systems. Two simulations designed to assist with modelling travel time to services have been developed and are shown here in videos below
The first uses a travel time output from the SAGA-GIS application and adds transport network agents (cars) and allows the interactive placement of patient agents simulating cases of patient travel and demonstrating the integration of raster and network forms of travel. The second incorporates the travel time calculation into the ABM and allows the rapid exploration of multiple travel time scenarios through a user-friendly interface. This model explores how Agent-based travel time modelling can assist local government planners to simulate impacts on access to care through enabling changes to continuous variables (such as rainfall or travel speed), and the addition of discrete factors (such as road breaks or new services). In both cases, the ABM format removes any need for GIS or modelling skills through creating a simple and accessible format for interactive scenario modelling.
This work uses Agent-based modelling for exploring the emergence of patterns from complex interactions through an investigation the adaptive evolution of toads on the move.
Three Agent-based models were created in Netlogo to explore changes in Cane Toad morphology at the colonising front as new populations move across Australia. Research has shown that individuals at the front generally have longer legs and thus travel faster than non-colonising individuals. There is also evidence suggesting inheritability of the direction of their movement. In all the models toads inherit movement speed (leg length) attributes.