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INTEGRATION OF AGENT-BASED MODELIING AND

GIS FOR URBAN SIMULATION

Majeed Pooyandeh a, Saadi Mesgari b, Abbas Alimohammadi c

a,b,c Faculty of Geodesy and Geomatics K.N. Toosi Univ. of Technology 1346 Vali Asr Avenue

Post code 19967-15433, Tehran, Iran.

Phone: +98-21-8878 62 12 Fax: +98-21-8878 62 13

E-mail: a: majeed_pooyandeh@yahoo.com b: mesgari@kntu.ac.ir c: alimoh_abb@yahoo.com

ABSTRACT

Urban simulation has remained an interesting research topic for many years. Issues like

urban growth, congestion and segregation demand advanced modeling approaches. Numerous

approaches and modeling techniques have been applied and they all have their own advantages

and deficiencies. While aggregative modeling techniques have been criticized by researchers for

their serious lacks for such a modeling, individual-based modeling techniques have received

special attention lately. Among these models, agent-based modeling is considered to have

outstanding superiorities. Agent-based modeling provides a better understanding of the structure

and processes of urban systems. Integration of this modeling technique with GIS will enhance

their potential for urban simulation purposes dramatically. This paper reviews the applications of

agent-based modeling in urban simulation, and then depicts the necessity of integrating this

modeling approach with GIS and reports current state and prospect of this integration.

Keywords: Agent-based modeling, GIS, integration, urban simulation

1. INTRODUCTION

Urban systems are dynamic in nature; this means that change is an importantissue for all urban phenomena. Due to lack of computational and modeling abilities,

many of these dynamic systems were considered static in their applications. With the

development of earth sciences and improvement of computer technology, dynamic

aspects of urban systems are under special attention nowadays. Considering cities andurban systems as complex systems, has opened up a new horizon for urban modelers.

Availability of micro-level data and advanced processing abilities of computers have

resulted in a new simulation approach which is called Micro-simulation. In this form ofsimulation every active agent in an urban environment should be involved. Agent-based

modeling is the poster child of complexity modeling, since it can properly model

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qualities like self-organization and emergence which are essential characteristics ofcomplex systems. Recently it has been mentioned by some researches that an appropriate

integration of agent-based modeling and GIS can enhance both systems for urbansimulation purposes (Brown et al., 2005; Parker, 2004; Torrens and Benenson, 2005).This paper focuses on this integration; first of all it describes agent-based simulation,

then concentrates on urban agent-based modeling and indicates the applications of agent-

based modeling in urban domain. Then the attitude of GIS towards dynamic phenomena

is depicted, and lastly the integration of them is discussed in three parts namelynecessity, current state and future works.

2. AGENT-BASED SIMULATION

Traditional urban models have an aggregative view of the problem. Torrens

(2001) identified following weaknesses for these models: their centralized approach, apoor treatment of dynamics, weak attention to detail, shortcomings in usability, reducedflexibility, and a lack of realism. It has also been mentioned that they dont address the

concerns of current planning and policy analysis, which are the issues like regeneration,

segregation, polarization, economic development, and environmental quality (Batty,2003). Agent-based simulation is a new modeling technique which acts in an individual-

based manner. In this approach agents which are the actors of the seen are determined

and their attributes and behaviors are defined. Up to this point agent-based simulationand object-oriented modeling have common characteristics, but there are a number of

differences between these techniques. Stating these differences can lead to a better

understanding of agent-based simulation. First of all agents are autonomous, this means

that they can independently make decisions (Jennings, 2000). Also they are active, i.e.they dont need to receive messages from other objects to become active (figure 1).

Figure 1- Canonical View of an Agent-based System (Jennings, 2000)

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Castle and Crooks (2006) identified three main advantages for agent-basedmodels. First they are able to model emergent phenomena; moreover they provide a

natural environment for modeling, i.e. their basic units (agents) are more compatible withour conceptual perception of real world phenomena. Moreover they have the uniquequality of flexibility. These flexibility can be interpreted according to agents two other

characteristics. Agents are adaptive (Holland, 1995) and they can learn from their

environments. This means that they can change their actions according to their living

environments. But like any other models they certainly have some limitations. To fullymodel a complex system, many attributes and behaviors should be considered for an

agent; to overcome this problem multiple runs and varying initial conditions are required

(Axtell, 2000), this brings about computational problems and makes the system morecomplex. On the other hand they are very sensitive to initial conditions and to small

variations in interaction rules (Couclelis, 2002). Lastly agents are weak in modeling

systems that demonstrate subjective and extremely complex and unpredictable behaviorslike that of human beings in specific environments; such models may result in even more

complex structures than their corresponding simulated systems.

2.1. URBAN SIMULATION USING AGENT-BASED MODELING TECHNIQUES

Urban systems are believed to be complex systems, with dynamic non-linearinteractions and numerous actors. In fact there are some debates about this assumption in

urban research society. Some researchers have stated that urban systems are totally

different from living organisms and qualities like self-organization and emergence arenot applicable for this domain. But anyway adopting this kind of modeling technique

seems to be more useful than ignoring it, since simulations that are currently performedusing this approach can not be done using other modeling techniques. Thus agent-based

approach is considered to be an appropriate technique for modeling complex phenomena.

Decision-making agents can represent stakeholders at multiple scales, from individualparcel managers to village households to local planning boards. Interaction environments

can include social networks, markets, and political institutions (Parker, 2004).

Exploiting agent-based models in geographic applications is a relatively newnotion (Brown et al., 2005; Parker, 2004). Cellular automata techniques have been the

dominant simulation tool for some years and were used in numerous applications. But it

has become clear that CA techniques have some limitations in modeling agent likebehaviors of urban components. This led to a number of studies which used agent-based

modeling for urban simulation. These studies can be mentioned as: simulation of

residential dynamics in the city (Benenson, 1998, Torrens, in press); the application of

agent-based models in studying the dynamics of pedestrian behavior in streets (Batty,2001; Castle, 2006; Kerridge et al., 2001; Schelhorn et al., 1999) and modeling the

discrete dynamics of spatial events for mobility in carnivals and street parades (Batty et

al., 2003). Also Torrens (2001) integrated CA and multi-agent models to support the

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exploration of what-if scenarios for urban planning and management and Semboloni etal., (2004) presented CityDev, an interactive multi-agent simulation model on the web.

3. GIS AND DYNAMIC PHENOMENA

Modeling dynamic phenomena has been challenging GIS research community for

many years. So spatio-temporal modeling is regarded to be a long-term research topicespecially in urban domain. But in spite of this attractiveness, time has not yet been fully

integrated with GIS. Numerous approaches and modeling techniques have been proposed

by researchers, but these theoretical models have mainly remained in conceptual level.The implementations of temporal GIS models have mainly resulted in building spatio-

temporal databases which are more or less capable of handling spatial and temporal

queries. These systems are not appropriate for simulation purposes, especially when the

system under consideration is regarded to be a complex system with non-linearinteractions between its components and characteristics like self-organization and

emergence. Thus the concept of self-organization and the potential for simulating

behavior in space and time can be contrasted with new TGIS approaches in which thereal world is observed, modeled and represented from a static observer point of view.

4. INTEGRATION OF AGENT-BASED MODELS AND GIS

4.1. NECESSITY OF INTEGRATION

In Agent-based urban modeling and simulation, real-world spatial data are to be

used, so some form of coupling with GIS data and functionality seems to be necessary inorder to create models that effectively represent both complex spatial structures and rich

dynamical processes (Brown et al., 2005). Integration of agent-based modeling

techniques with GIS yields a number of outstanding advantages. First of all using GISvector data, there would be no need to impose spatial limitations to the simulation.

Comparing this approach with cellular automata in which modelers encounter problems

like regular lattice and neighborhoods or one agent per cell, clarifies these advantages.This integration gives great spatial modeling abilities to agent-based modelers. On the

other hand since Geographical information systems are currently static; this integration

enhances GIS analysis potential for modeling dynamic phenomena (Anderson, 1997;

Box, 2002). This implies that spatial data models can be integrated with spatio-temporal

processes, and this is of great significance in urban systems where both form andfunction are to be considered. In fact GIS tools are limited in process knowledge and give

little description of processes; on the other hand agent-based modeling tools are limitedin spatial knowledge. Also it should be added that for simulation of complex phenomena,

gathering and maintaining accurate spatial data is vital. GIS provides an appropriate

environment for gathering such data and maintains the accuracy and integrity of data.

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Brown et al.(2005) have identified four key relationships that affect how GIS and agent-based moles can interact. They can be named as identity, causal, temporal and

topological relationships. From the identity relationships perspective, it can be stated thatmapping agents to spatial features can be one-to-one or many-to-many, and agents canexist without spatial features and vice versa. There are also a number of causal

relationships between agents and spatial features. Agents can change a spatial feature,

also they can move a spatial feature and lastly they can change attributes on a field.

According to temporal aspect, agent-based modeling has a rich notion of time, on theother hand current GIS have mostly a static design. In contrast with the temporal aspect,

from the topological aspect, GIS has a rich spatial expression and should be in control of

topological dynamics. GIS can help defining topological rules and spatial associationsbetween spatial features. So in this integration, each part shares its strength points with

the other.

4.2. CURRENT STATE

Integration GIS and gent-based modeling is a new concept and there are fewapplications that effectively implement this integration (Itami, 2002; Jiang & Gimblett,

2002). A number of agent-based modeling (ABM) and GIS integration approaches have

been proposed which range from loosely to tightly coupling of ABM-GIS. Due to several

reasons tight coupling is much more favorable than loose coupling. Using loose couplinginvolves passing interchange files between the model and the database (Brown et al.,

2005). This eliminates direct use of database functions within the models (Gimblett

2002). Also due to the file interchange the model becomes computationally inefficient.So tight coupling as an alternative is more desirable. In this kind of integration three

main kinds of approaches can be identified. The first one is ABM centric, in which GISabilities are embedded in an agent-based modeling environment. This model works well

in cases where we dont have complicated spatial analysis or repetitive spatial operations.

In other circumstances, functions that are readily available within the GIS frameworkneed to be written, debugged, tested, and documented within the ABM framework and it

should be considered that developing ABM systems is not as straightforward asGeographic information systems, (Brown et al.,2005) because agent-based systems are

not designed for spatial functionality, and certainly GIS environments are much more

suitable for such purposes. As an example we can mention GeoTools Java library, whichhas embedded GIS management and visualization functionality in the RePast ABM

development platform.As an alternative GIS-centric approaches can be proposed, in which ABM

functions are implemented in a GIS environment. So in this approach we have a GIS

interface in which agent-based simulation functionalities are embedded. The GIS uses theABM libraries and analyses are performed through GIS interface. Agent Analyst can

be mentioned as an example of this approach. Agent Analyst is an extension of ArcGIS

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software package which is designed recently using RePast ABM development platform.This open source and free extension is easily added to the GIS analysis environment and

provides agent-based modeling capabilities. In contrast with the Agent-centric approachthis approach provides extensive spatial functionality. On the other hand GIS do notusually provide means of keeping and coordinating time which is an important part of

agent-based simulation (Brown et al.,2005).

Also it should be mentioned that another approach has also been proposed that is

neither agent centric nor GIS centric. In contrast to building a completely new systemfrom the ground up, an intermediate middleware alternative builds on existing

platforms and involves the development of software to handle the identity and causal

relationships between the agents within an ABM environment and spatial features withina GIS environment, as well as the temporal and topological relationship issues that arise

in the model (Brown et al.,2005). In this approach the whole functionality of one part is

not adopted, rather for each task, it is determined that what kinds of functions are neededand which part can present these functionalities. This decreases the development

procedure since only the two systems should be linked and there is no need to build new

systems from the beginning.

4.3. FUTURE WORKS

An important issue in integrating GIS and agent-based modeling is making theunification in the level of data model. We need to explore and develop a common

temporal-spatialdata model to integrate ABMs temporal and causal analysis and GISs

locational and topological analysis. Parker (2004) has mentioned three issues thatresearchers wish to achieve in ABM-GIS integration. First of all there is a need to

involve advanced mathematical functionality. This functionality is extremely useful forconstructing agent decision-making algorithms, especially those based on optimizing

behavior, for model calibration, and for coupling ABMs with environmental process

models (Parker, 2004).Secondly there should be some tools for verification of simulation results. Any

modeling and simulation approach should be verified accurately. Also generation of good

quality temporal and spatial output graphics and the ability to save and export animationsin standard formats is essential for these models. There should be the ability to analyze

generated output using statistical models and also conduct on the fly sensitivity

analysis by changing model parameters. Also there is a need for a wide range of built-in

functions including transparent and well-documented algorithms for agent behavior,spatial processes, calibration, verification, and validation; process-based models (parker,

2004)

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5. CONCLUSIONS AND RECOMMENDATIONS

Integration of agent models and GIS seems to be very promising for urbansimulation. In fact in such integration each part covers the lack and limitations of theother part. GIS has extensive abilities in spatial modeling but is not typically capable of

handling dynamic phenomena, on the other hand agent-based modeling is dynamic in

nature but lacks powerful spatial tools, so integrating these two modeling tools enhancesboth of them dramatically. A number of approaches have been proposed by researchers.

From the loosest to the tightest in the spectrum of ABM-GIS tightness, five

integrating approaches, including data exchange approach, ABM centered approach, GIScentered approach, middleware approach and whole integrated agent-based GIS approach

have been proposed (Brown et al.,2005; Benenson & Torrens, 2004; Gimblett, 2002).

Three of these approaches were discussed and evaluated in this paper. Certainly it can

not be stated that an approach is the best for all circumstances, rather each applicationdemands its own integration approach.

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