A grid-based spatial data model for the simulation and analysis of individual behaviours in micro-spatial environments
Introduction
Individual spatiotemporal activity in micro-spatial environments is closely related to the management of unconventional emergencies, traffic control and dispersion, urban planning and other applications [1], [2], [3], [4], [5], [6], [7]. By constructing a real or virtual geographical environment, crowd simulation can dynamically simulate individual spatiotemporal activities and predict the evolution of crowd behaviour, which has scientific and practical value for efficient, scientific and well-organised decisions in people–place relationships and in improving urban and emergency management. Despite the wide-spread use of crowd simulations, an appropriate spatial data model that supports such applications is currently not available.
Geographic Information System (GIS) is an important tool for studying people–place relationships and can be used to in support of geographical process modelling, expression and analysis. The advantages of combining geographic environment expression and analysis using GIS and with crowd simulations include the realisation of an integrated analysis of the geographic environment and individual behaviours and an improvement in the fidelity and efficiency of individual behaviour modelling. However, GIS is mainly focused on geographic entities, phenomena, procession and spatial environments at the macro-scale and lacks the ability to model the behaviours and related incidents of individuals and groups in society and economic activities. GIS is primarily used for place-based information regarding people–place relationships in earth’s systems and cannot sufficiently address people-based geo-processes [8], [9], [10], [11]. A closer integration of GIS and the personal behaviours has been proposed recently. Miller [8] holds that traditional GIS, which is a place-based system, cannot completely describe people–place relationships, and people-based GIScience (Geographic Information Science) must be evaluated in greater detail. The use of Virtual Geographic Environments (VGE), which originated from the integration of VR and geography, is an important research field in GIScience. From the perspective of VGE, Gong and Lin also proposed the concept of human-oriented GIS [10], [11]. However, research methods and spatial data models for people-based GIScience or human-oriented GIS have received little attention.
Spatial data models form the theoretical basis of GIS spatiotemporal expression and the simulation, prediction and analysis of geo-processes. Multiple spatial data models for modelling individual behaviours have recently been proposed. For example, activity is a common method used to model individual spatiotemporal behaviour [12], [13], [14]. According to Axhausen [15], an activity is defined as the main activity carried out at a location, including the waiting time before or after the activity. Activity-based spatial data model focuses on modelling individual travel behaviours, and tracking analysis is a key application, the method of modelling spatial environment is not the key in the model. Crowd simulation is the focus in computer science [2], [16]. Cell-based model is the common way of modelling the spatial environment. The cell used for crowd simulation is most commonly expressed as a two-dimensional (2D) square grid similar to the GIS raster cell but smaller. For example, the cell size in EXODUS (a crowd simulation software package) is 0.5 m × 0.5 m [17]. In most cases, a person will occupy one cell at any given time. The spatial data model used for crowd simulation lacks the complexity required to describe the multiple types of semantic information associated with geographic entities. When the model applied is to large-scale geographic environments, the attribute storage space is consistently too large, and it is time-consuming to search for the individual by location. In addition, the spatial relationships among geographic entities are not expressed, which complicates the spatial analysis of individual behaviours. Many researchers have realised the advantages of GIS spatial data models and have started to apply GIS in crowd simulation. For example, Liu and Chen [18], and Lee and Kwan [19] modelled the spatial environment of a crowd simulation based on the ArcGIS Geodatabase vector data model and arc-node model respectively. However, both methods are ineffective when describing the geometric characteristics of micro-spatial environments (e.g., the irregular shapes of indoor rooms or outdoor squares cannot be given a fine level of definition) [20]. Based on an GIS arc-node model, Tang and Ren [21] constructed a network using cells and then applied the network to the simulation and analysis of individual behaviours. However, the basic unit of the spatial data model remains the cell. As the number of cells increases, the required computational cost also increases. Therefore, with this method, it is difficult to rapidly calculate individual behaviours, which does not meet the demands of crowd simulation in a complex or large-scale spatial environment.
The objective of this article is to develop a new spatial data model for crowd simulation in micro-spatial environment to remedy the defects of the existing data model. The specific objectives are as follows: (1) analyse the requirements of modelling people and place in micro-spatial environments, then defining the basic unit of the people–place spatial data connection and building a spatial data model for individual behaviour simulation and analysis; (2) study the methods of modelling spatial environments and simulating individual behaviours with the data model we advanced; and (3) implement a crowd simulation for a specific study area using the data model and analyse the usability of the model.
Section snippets
Modelling people and place in micro-spatial environments
Although people and place can be modelling from various perspectives in macro-spatial environments, there are still many differences and some special requirements of modelling them in micro-spatial environments.
Spatial expression and crowd simulation using the grid-based spatial data model
Although the methods of modelling people and places in micro-spatial environments are different, the way of modelling spatial location is essential in both cases. Spatial location is the basis of the people–place data connection. The shapes of geographic entities in micro-spatial environments are irregular, and the occupied area of an individual cannot be ignored. Based on these two basic requirements in crowd simulation, the concept of the grid will be used as a basic spatial unit for
Spatial data acquisition and processing
The Beijing Exhibition Center, an important public space in Beijing, was chosen as the study area. The study area consists of two main parts, the indoor and outdoor spaces (Fig. 5). The indoor space contains exhibition halls, corridors, staircases, exit, pillar and other objects. The outdoor space is relatively simple and mainly consists of a square that is adjacent to the exhibition centre.
The 3D model of the study area was built in 3DsMax, and we used the WGS84 coordinate system with a
Discussion and conclusions
The organisation and expression of spatial data is consistently an essential component of people–place relationship research. Although many spatial data models have been proposed from the perspective of visible expression or semantic analysis, the majority of GIS spatial data models emphasise the macro-scale environment, and people-oriented spatial data models in micro-spatial environments are rare. The concept of the grid is presented in this article to meet the requirements of the spatial
Acknowledgements
This research is supported by National Natural Science Foundation of China (41201375); Key Knowledge Innovative Project of Chinese Academy of Sciences (KZCX2-EW-318); the Plans of the National High-tech R&D Program of China, 863 Program (2012AA12A204); National Natural Science Foundation of China (40871181 and 41101363); The Planned Science and Technology Project of Henan Province (122102310302); the Science Foundation for Doctorate Research from Tianjin Normal University (52XB1109); Supported
Reference (35)
- et al.
Emergency response after 9/11: the potential of real-time 3D GIS for quick emergency response in micro-spatial environments
Computers, Environment and Urban Systems
(2005) - et al.
Computer simulations vs. building guidance to enhance evacuation performance of buildings during emergency events
Simulation Modelling Practice and Theory
(2011) - et al.
Crowd evacuation simulation for bioterrorism in micro-spatial environments based on virtual geographic environments
Safety Science
(2013) - et al.
Autonomous pedestrians
Graphical Models
(2007) - et al.
A grid graph-based model for the analysis of 2D indoor spaces
Computers, Environment and Urban Systems
(2010) - et al.
Simulation for pedestrian dynamics by real-coded cellular automata (RCA)
Physica A: Statistical Mechanics and its Applications
(2007) - et al.
Crowd simulation for emergency response using BDI agents based on immersive virtual reality
Simulation Modelling Practice and Theory
(2008) - et al.
Jamming transition in pedestrian counter flow
Physica A: Statistical and Theoretical Physics
(1999) - et al.
Spatio-temporal location modeling in a 3D indoor environment: the case of AEDs as emergency medical devices
International Journal of Geographical Information Science
(2011) - et al.
Crowd Simulation
(2007)
MAGS project: multi-agent geosimulation and crowd simulation, spatial information theory
Foundations of Geographic Information Science
Simulating the effects of social networks on a population’s hurricane evacuation participation
Journal of geographical systems
Place-based versus people-based geographic information science
Geography Compass
What about people in geographic information science?
Virtual Geographic Environment: A Geographic Perspective on Online Virtual Reality
Exploring human-oriented GIS
Geomatics and Information Science of Wuhan University
Handling disaggregate spatiotemporal travel data in GIS
GeoInformatica
Cited by (14)
Spatio-temporal hazard assessment of a monogenetic volcanic field, near México City
2019, Journal of Volcanology and Geothermal ResearchAlgorithms for automated generation of navigation models from building information models to support indoor map-matching
2016, Automation in ConstructionCitation Excerpt :This approach can be extrapolated to 3D by specifying whether each grid is surmountable or insurmountable by a human for navigation purposes [29]. Bandi and Thalman [45], Yuan and Schneider [29], Li et al. [49] and Song et al. [54], performed geometrical analysis on the generated grid-based navigation models to determine the topology of the physical environment for which grid-based navigation model is being generated to find out if a user can navigate from one grid to the other. When the grids are diagonal neighbors, the process of topology identification based on geometrical reasoning can introduce topological errors (depicted by grids enclosed in the circle in Fig. 3) within which geometrical reasoning can classify the two grids to be connected although they are not connected in the physical environment.
Intelligent System Concept of Integrated Education History in Single Identity Number Using Grid-Based Model (GBM)
2023, 2023 IEEE International Conference of Computer Science and Information Technology: The Role of Artificial Intelligence Technology in Human and Computer Interactions in the Industrial Era 5.0, ICOSNIKOM 2023A Non-Rigid Hierarchical Discrete Grid Structure and Its Application to UAVs Conflict Detection and Path Planning
2022, IEEE Transactions on Aerospace and Electronic SystemsFire hazard assessment with indoor spaces for evacuation route selection in building fire scenarios
2022, Indoor and Built Environment