Spatial process and data models: Toward integration of agent-based models and GIS

Brown, Daniel G.; Riolo, Rick L.; Robinson, Derek T.; North, Michael; Rand, William

doi:10.1007/s10109-005-0148-5

Cited by 251 publications

(157 citation statements)

References 37 publications

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“…Therefore, the integration of GIS and ABM will enhance the capability of urban s imulation techniques (Brown et al 2005;Parker 2005 Integration of GIS and ABM in the urban dynamics domain has been studied before in several aspects. Gorn;alves et al (2004) for example, recommended a conceptual environment for coupling GIS and ABM s imulation tools.…”

Section: Gis and Abm Integrationmentioning

confidence: 99%

Firm Dynamic Analysis for Urban Land Use and Economic Growth Modelling

Man¹,

Sabri²,

Hosni³

et al. 2013

PMJ

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In urban growth processes, urbanisation is highly influenced by economic growth which triggers the dynamics of economic agents and land uses. This is consisted of complex subsystems which need sophisticated methods like agent-based modelling and simulation to understand the pattern, behaviour and scale of multiple actors. The objective of this paper is to identify the behaviour, pattern and the scale of impact of firms on market in the region in order to foster an accurate agent-based modelling. The Geographic Information System is utilized to geocode the entrance and exit of firms to the market in Greater Kuala Lumpur region. This study has also carried out a temporal analysis considering 18 years performances of the firms from 1990 to 2007. The findings in this paper show sector 9 (i.e. Financing) has highest percentage of establishment with 35. 1 %. In addition, Sector 3 (i.e. Mining) and Sector 5 (i.e. Electricity) have the lowest percentage of establishment with 0.3 %. The result of this study will be a foundation to facilitate developing an agent-based modelling and simulation which helps town planners and decision makers to understand the relationship and interaction between economic growth and dynamic land use patterns in their region.

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Section: Gis and Abm Integrationmentioning

confidence: 99%

Firm Dynamic Analysis for Urban Land Use and Economic Growth Modelling

Man¹,

Sabri²,

Hosni³

et al. 2013

PMJ

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“…Some aspects of agent technology have already been used with GIS [7] [8]. Two key developments have resulted in GIS tools: RePast for ArcGIS and in Oracle Spatial.…”

Section: A Quantitative Comparison Of the Systemmentioning

confidence: 99%

“…This is important since GIS tools are made increasingly available through the Internet, and are being ported to mobile networks for LBS applications [6]. Consequently the issue of interoperability urgent needs to be addressed [7] [8].…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based System to Support Geo-Information Analysis

Hattori

Klein

Ito

2007

2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07)

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-Geo-Information Systems (GIS) are software for the handling and analysis of spatial data and are at the heart of GeoInformation Science (GISc). Most GIS functions require interaction with human experts. This is considered problematic since it causes extra complexity and increases the amount of resources required. Agent Technology has the potential to assist in reducing this problem. However the current application of software agents to the GIS domain is very limited and fails to take into account the full functionality and advantages of Agent Technology. In this paper we discuss the application of agents in GIS and argue for the need to produce an agent-based framework for GIS. By way of context we define a novel agent-based system for one important aspect of GIS, the construction of a variogram. We quantitatively compare our architecture with two the existing agent-based tools: RePast for ArcGIS and Oracle Agents.

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“…It enables researchers to study emergent system-level patterns and outcomes from generalized interactions at the individual agent level [4][5][6]. ABMs have been widely applied in many fields, including ecology, biology, social science, economic science, computer science, and the geospatial sciences [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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Spatial process and data models: Toward integration of agent-based models and GIS

Cited by 251 publications

References 37 publications

Firm Dynamic Analysis for Urban Land Use and Economic Growth Modelling

Firm Dynamic Analysis for Urban Land Use and Economic Growth Modelling

An Agent-Based System to Support Geo-Information Analysis

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

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