Integration agent-based models and GIS as a virtual urban dynamic laboratory

Peng Chen; Miaolong Liu

doi:10.1117/12.764945

7 August 2007 Integration agent-based models and GIS as a virtual urban dynamic laboratory

Peng Chen, Miaolong Liu

Proceedings Volume 6754, Geoinformatics 2007: Geospatial Information Technology and Applications; 675423 (2007) https://doi.org/10.1117/12.764945
Event: Geoinformatics 2007, 2007, Nanjing, China

Abstract

Based on the Agent-based Model and spatial data model, a tight-coupling integrating method of GIS and Agent-based Model (ABM) is to be discussed in this paper. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena such as urban dynamic. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, the agent-based model and spatial data model are discussed, and then the relationships affecting spatial data model and agent-based process models interaction. After that, a realistic crowd flow simulation experiment is presented. Using some tools provided by general GIS systems and a few specific programming languages, a new software system integrating GIS and MAS as a virtual laboratory applicable for simulating pedestrian flows in a crowd activity centre has been developed successfully. Under the environment supported by the software system, as an applicable case, a dynamic evolution process of the pedestrian's flows (dispersed process for the spectators) in a crowds' activity center - The Shanghai Stadium has been simulated successfully. At the end of the paper, some new research problems have been pointed out for the future.

Citation Download Citation

Peng Chen and Miaolong Liu "Integration agent-based models and GIS as a virtual urban dynamic laboratory", Proc. SPIE 6754, Geoinformatics 2007: Geospatial Information Technology and Applications, 675423 (7 August 2007); https://doi.org/10.1117/12.764945

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KEYWORDS

Data modeling

Geographic information systems

Process modeling

Databases

Systems modeling

Computer programming

Computer programming languages

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