Agent-Based Modelling and Simulation using Repast: A Gallery of GIS
Applications
Christian J. E. Castle, Andrew T. Crooks, Paul A. Longley, Mike Batty
Centre for Advanced Spatial Analysis.
University College London, 1-19 Torrington Place, London, WC1E 7HB, England
Tel. +44(0)207 679 4260 Fax +44(0)207 813 2843
c.castle@ucl.ac.uk, andrew.crooks@ucl.ac.uk, plongley@geog.ucl.ac.uk,
http://www.casa.ucl.ac.uk
KEYWORDS: Agent Based Models, Simulation, Toolkits, Repast, CASA
1. Introduction
Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling
systems comprising of autonomous, interacting actors (i.e. agents) by computer representation.
For example, in a pedestrian evacuation context, agents can include different evacuees with
different physical and behavioural characteristics (e.g. adult, child, mobility impaired person,
knowledge of building layout, member of staff, visitor or tourist, etc), all of whom are required to
make decisions or take actions that might affect the evacuation process. By simulating individual
actions of diverse agents, and measuring the resulting system behaviour and outcomes over time,
ABMS can be a useful tool for studying the effects on processes that operate at multiple scales
and organisational levels, and their effects (Brown, in press).
2. Agent-Based Modelling and Simulation
The development of models can be greatly facilitated by the utilisation of libraries of reusable
model components. The use of pre-defined ABMS toolkits can reduce the burden modellers face,
programming parts of a simulation that are not content-specific e.g. simulation control or inputoutput procedures (Tobias and Hofmann, 2004). It also increases the reliability and efficiency of
the programs, as most of the complex parts of the program have been created and optimised by
professional developers, as standardised simulation tools. Finally, modellers profit from their
ease of use, most are ready made, widely distributed, provide detailed documentation and a user
interface that simplifies the user’s task of creating a model. Unsurprisingly, there are limitations.
For example, a substantial amount of effort is required to understand another programmer’s code,
a toolkit may be written in its ‘own’ programming language (e.g. NetLogo), and the
desired/required modelling functionality may not all be present (yet!). However, these can be
offset against additional tools offered by the user community.
Object-Orientated Programming (OOP) is central to ABMS. However, it is not the intention of
this paper to outline its concepts here. The subsequent section of this paper evaluates some of
the ABMS toolkits currently available, highlighting some of the key questions that should be
considered whilst determining an appropriate toolkit for a users needs.
2.1 Toolkits
Numerous ABMS toolkits have emerged, although their level of development varies considerably.
Some of the more established toolkits, and therefore mainstream include: AgentSheets; AnyLogic;
Ascape; Breve; Cormas; deX; ECHO; JADE; Madkit; MAGSY; MASON; MIMOSE; NetLogo;
Ps-I; Quicksilver (renamed Omonia); Repast; SimAgent; SimPAck; StarLogo; Sugarscape;
Swarm; TeamBots; and VSEit. Of course, comprehensive practical experience must be gained to
obtain a full understanding of a modelling or simulation toolkit’s functionality in relation to a
users needs. Unfortunately, this is not usually a feasible objective. For this reason several
authors (Najlis et al., 2001; Tobias and Hofmann, 2004; and Roberson, 2005) discuss criteria that
can, or should be considered before selecting a toolkit. These include, but are not limited to: ease
of programming and/or using the package; size of the community using the platform; size of the
programming community familiar with the language in which the package is implemented; the
ability to represent space (object vs. field view) and topological relationships; mechanism for
scheduling and sequencing events; and the ability to represent multiple organisational /
hierarchical levels, or scales.
Depending on the model specification and the users programming expertise, these criteria will be
weighted differently. For example, the ease of programming the model, which is affected by the
programming language used by the platform, can have a considerable bearing on the platform
used. According to Wood (2002) in comparison to C++, for example, Java is considerably easier
to use, especially for a beginner. Although Java also incorporates some of the more modern and
useful developments in OOP languages (e.g. applets, which allow models to be shared over the
internet), the most compelling argument for using Java is its platform independence.
The remainder of this paper is dedicated to Repast models that have, or are being developed at the
Centre for Advanced Spatial Analysis, University College London. Repast has/is been used by a
number of researchers for several reasons, which are a balance of the criteria listed above. Repast
provides the option of programming a model in Java, which is advantageous for the reasons
outlined above. Currently there is a large and very active community using Repast, which has
generated an invaluable library of example models and source code. Help is also available via the
community mailing list, where responses are usually prompt (allowing for the time lag in to
America) and helpful. Additionally, the list of toolkits listed above can be split into proprietary,
freeware, and open source. The distinction being, unlike freeware, proprietary toolkits require a
licence, but both are essentially black box. This limits the possibility to extend the toolkit when
required functionality is unavailable, and the user is not entirely sure about the inner workings of
their model. Conversely, open source toolkits (which includes Repast) are not only free, but they
provide a modeller with the flexibility to alter or extend core functions if they do not exist, and
understand how core functions are programmed and work. This provides the user with a more
extensible and robust toolkit. A more detailed overview of Repast, including an outline of
Repast’s functionality is provided in the subsequent section.
3. Repast
Repast provides a set of core classes for creating, running, displaying and collecting data from
agent based simulations (Figure 1), that allow for complex models to be developed in a relatively
short period of time (Macal and North, 2005). It seeks to support the development of extremely
flexible models of agents with an emphasis of social interactions. Users can build simulations by
incorporating Repast library components into their own programs depending on what functions
are required. The use of these core features means that less time is given to GUI development
and more time for model development.
At present Repast 3.1 offers three different programming languages in which to implement and
develop Repast models: RepastPy (based on Python scripting language), RepastJ (Java based),
Repast.Net (implemented in C# but can use any .Net language). All have the same core services,
which allow ABMs to be developed.
One of Repast’s attractive features is its ability to integrate GIS data (either raster or vector)
directly into the simulation with relative ease. GIS have been available for quite a while, but until
recently has not been easily incorporated into programmes of social systems. Repast allows a
user to build a ‘world’ populated by mobile entities (agents) that have their own rule sets for
establishing their behaviour. Agents live in ‘spaces’ within these worlds, which are the
environments they move around and interact within. Typically, the space agents occupy are
simple grids (i.e. cellular or raster), or graphs with edges and nodes. However, with the
integration of GIS, agents can move around ‘real space’. For example, the use of RepastJ or
RepastPy provides a set of classes that allow Shapefiles to be displayed either through OpenMap
or ArcGIS (via Agent Analysis).
Charting
Parameter Settings
Display Behaviour
Control Simulations
Figure 1: Typical Repast features.
4. Applications
Repast is currently being used in a wide variety of applications from the study of land use change
(Deadman, et al., 2004; Brown et al., 2005; and Xi et al., 2005), watershed modelling (Reitsma
and Albrecht 2005), infectious disease modelling (Yang and Atkinson 2005), regime change
(Cederman 2003), economics and business studies (North et al., 2002; Robertson 2003; and
Padgett et al., 2003), to name but a few. CASA researchers have used or are using Repast
(specifically RepastJ) to study a range of applications: pedestrian modelling, urban dynamics,
segregation, residential and business location and evacuation modelling (Figure 2). The models
use the core Repast functionality, extending it further to meet their own specific needs.
For example the CAPABLE project (Children’s Activities, Perceptions and Behaviour in the
Local Environment) is based on the increasing concern about allowing children out
unaccompanied by adults and at the same time increasing recognition of children's rights and
autonomy. The purpose of this research is to explore how children use the local environment and
what kind of environmental factors affect children’s spatial behaviour. Part of the project
objective is to develop a multi-agent-based model of children’s spatial movement, as well using
animation software for visualizing GPS traces. This software is written in Java using Repast and
OpenMap libraries. The Ordnance Survey (OS) MasterMap™ is used as the base map, upon
which GPS points are overlaid, thus allowing the creation of a time-series animation.
Barros (in press) has used Repast to study urban dynamics in Latin American cities. This
research produced a series of exploratory agent-based simulation models exploring urban growth
in relation to poverty and spontaneous settlements in Latin American cities where there is poor
control of policies upon the development process. The models explore how these processes occur
in both space and time, and how this is shaped by individual decisions.
The CAPABLE Project
Urban Dynamics in Latin American Cities
Segregation Modelling.
Pedestrian Modelling
Figure 2: Screen shots of some applications that are being developed at CASA.
Another research project at CASA involves an agent-based approach to simulating pedestrian
movement and the factors that control it. Part of the model simulates rent rate, land value and
land use changes in informal shopping centres. Another part of model looks at the affect of
pedestrians on micro-scale land use change due to decisions that are likely to have an impact on
trips’ demand and/or offer. Issues being explored include how pedestrians affect rent rates,
pedestrian flows and then rent rates based on retailing theory (Zachariadis, 2005).
5. Conclusion
ABMS is a relatively new and growing approach to modelling systems comprising of
autonomous, interacting agents by computer representation. Modelling can be facilitated by the
use of model components that are predefined within an ABMS toolkit. This reduces the burden
of programming non content-specific parts of a simulation, and increases the reliability and
efficiency of the program by allowing complex parts to be created and optimised by professional
developers. A non-exhaustive list of mainstream toolkits was identified, providing the reader
with criteria they should consider before choosing a toolkit for their modelling requirements.
Specific details of the Repast toolkit were discussed, outlining benefits in relation other toolkits.
In particular, Repast is a robust and extensible toolkit that allows user to develop models tailored
to their specific needs. It also has the added advantage of being open source and having a healthy
support and user community. Finally, an overview of models developed at CASA using Repast
was provided focusing on GIS applications.
Acknowledgments
We gratefully acknowledge the financial support of the Economic and Social Research Council
(ESRC, Collaborative Award in Science and Engineering (CASE) award PTA-033-2004-00034:
Castle, and PTA-033-2003-00008: Crooks; http://www.esrc.ac.uk); Camden Primary Care Trust
(PCT http://www.camdenpct.nhs.uk); and the Greater London Authority Economic unit (GLA
http://www.london.gov.uk/mayor/economic_unit).
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Biography
Christian Castle is a second year Ph.D. student at the Centre for Advanced Spatial Analysis
(CASA; University College London) under the supervision of Professor Paul Longley He is
currently undertaking an ESRC CASE studentship with Camden Primary Care Trust (PCT). The
project is entitled 'A GIS-Based Spatial Decision Support System (SDSS) for Emergency Services:
London’s King’s Cross Redevelopment'. His main research interests focus on pedestrian
modelling; emergency evacuation management; and Spatial Decision Support Systems (SDSS).
Andrew Crooks is a third year Ph.D. student at CASA under the supervision of Professor Mike
Batty. He holds an ESRC CASE award for researching 'Economic Sketch Planning Using
Complexity Theory’ supported by GLA Economics. His research explores the modelling of urban
areas using ideas from complexity theory and visualisation techniques from sketch planning.
Paul Longley is Professor of Geographic Information Science at UCL and serves as Deputy
Director of CASA.