首页 | 本学科首页   官方微博 | 高级检索  
     


A grid-based spatial data model for the simulation and analysis of individual behaviours in micro-spatial environments
Affiliation:1. College of Urban and Environmental Science, Tianjin Normal University, Tianjin 300387, China;2. State Key Lab of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;3. School of Surveying Engineering, Henan University of Urban Construction, Pingdingshan 467036, China;1. Faculty of Information Technology, Monash University, Melbourne, Australia;2. School of Computing, Telkom University, Bandung, Indonesia;1. Department of Earth Sciences, Environment and Resources, University of Naples Federico II, Largo San Marcellino, 10, 80138 Naples, Italy;2. Department of Environmental Sciences, Informatics and Statistics, University Ca’Foscari of Venice, Via Torino 155, 30172 Mestre, Venice, Italy;1. Helmholtz Centre for Environmental Research – UFZ, Department of Ecological Modelling, 04318 Leipzig, Germany;2. University of Potsdam, Institute for Biochemistry and Biology, Maulbeerallee, 214469 Potsdam, Germany
Abstract:As crowd simulation in micro-spatial environment is more widely applied in urban planning and management, the construction of an appropriate spatial data model that supports such applications becomes essential. To address the requirements necessary to building a model of crowd simulation and people–place relationship analysis in micro-spatial environments, the concept of the grid as a basic unit of people–place data association is presented in this article. Subsequently, a grid-based spatial data model is developed for modelling spatial data using Geographic Information System (GIS). The application of the model for crowd simulations in indoor and outdoor spatial environments is described. There are four advantages of this model: first, both the geometrical characteristics of geographic entities and behaviour characteristics of individuals within micro-spatial environments are involved; second, the object-oriented model and spatial topological relationships are fused; third, the integrated expression of indoor and outdoor environments can be realised; and fourth, crowd simulation models, such as Multi-agent System (MAS) and Cellular Automata (CA), can be further fused for intelligent simulation and the analysis of individual behaviours. Lastly, this article presents an experimental implementation of the data model, individual behaviours are simulated and analysed to illustrate the potential of the proposed model.
Keywords:Spatial data model  Crowd simulation  Grid  Micro-spatial environments  Individual
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号