Spatial semantic network and agent-based framework for spatial information interoperation

2004 ◽  
Author(s):  
Luo Yingwei ◽  
Wang Xiaolin ◽  
Xu Zhuoqun
Keyword(s):  
Spatial Information ◽  
Semantic Network ◽  
Agent Based

The agent-based spatial information semantic grid

2006 ◽  
Author(s):  
Wei Cui ◽  
YaQiong Zhu ◽  
Yong Zhou ◽  
Deren Li
Keyword(s):  
Spatial Information ◽  
Agent Based ◽  
Semantic Grid

scholarly journals Generating Agent-Based Multiscale Multicellular Spatiotemporal Models from Ordinary Differential Equations of Biological Systems, with Applications in Viral Infection

2021 ◽  
Author(s):  
T.J. Sego ◽  
Josua O. Aponte-Serrano ◽  
Juliano F. Gianlupi ◽  
James A. Glazier
Keyword(s):  
Differential Equations ◽  
Viral Infection ◽  
Spatial Information ◽  
Biological Systems ◽  
Spatial Models ◽  
Model Parameters ◽  
Ode Model ◽  
Agent Based ◽  
Ode Models ◽  
Computationally Expensive

AbstractThe biophysics of an organism span scales from subcellular to organismal and include spatial processes like diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent spatial and stochastic features of a biological system, limiting their insights and applications. Spatial models describe biological systems with spatial information but are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them. In this work we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice-versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters, investigate objects and processes implicitly represented by ODE model terms and parameters, and improve the reproducibility of spatial, stochastic models. We hope to employ our method to generate new ODE model terms from spatiotemporal, multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.Statement of SignificanceOrdinary differential equations (ODEs) are widely used to model and efficiently simulate multicellular systems without explicit spatial information, while spatial models permit explicit spatiotemporal modeling but are mathematically complicated and computationally expensive. In this work we develop a method to generate stochastic, agent-based, multiscale models of multicellular systems with spatial resolution at the cellular level according to non-spatial ODE models. We demonstrate how to directly translate model terms and parameters between ODE and spatial models and apply non-spatial model terms to boundary conditions using examples of viral infection modeling, and show how spatial models can interrogate implicitly represented biophysical mechanisms in non-spatial models. We discuss strategies for co-developing spatial and non-spatial models and reconciling disagreements between them.

scholarly journals INTEGRATION OF BUILDING INFORMATION MODELING AND AGENT-BASED MODELING FOR EVACUATION SIMULATION

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 109-112
Author(s):  
C. Beyaz ◽  
E. D. Özgener ◽  
Y. G. Bağcı ◽  
Ö. Akın ◽  
H. Demirel
Keyword(s):  
Information Technologies ◽  
Information Science ◽  
Spatial Information ◽  
Emergency Evacuation ◽  
Indoor Navigation ◽  
Information Modeling ◽  
Accurate Information ◽  
Agent Based ◽  
Fire Evacuation ◽  
Building Information

Abstract. Building Information Modelling (BIM) is a highly advanced spatial modeling method that is fully incorporated in the building lifecycle. With the support of Information Technologies, the use of BIM has become common in building management such as energy efficiency, indoor navigation and emergency evacuation simulations. This study focuses on emergency evacuation simulations since, integrating BIM and Spatial Information Science, could mitigate casualties in emergencies. Traditional evacuation management methods are generally inadequate since they are based on 2D evacuation plans, they are static and do not consider the characteristics/interactions of the people in the building. This study aims to integrate BIM and Agent-Based Modelling (ABM) for emergency evacuation simulations, where characteristics of the building and the users are incorporated. Istanbul Technical University Faculty of Civil Engineering was selected as study area and the BIM model was created by using the CAD drawings of the floor plans. The users of the Faculty building such as students, academicians, administrative staff and visitors are considered for simulations. The BIM model was transferred to the ABM environment, and the routes used during the fire evacuation were generated. Fire evacuation simulations were performed, where agents having different characteristics evacuate the building according to the rules predefined. Three different scenarios were tested. Major conclusion of this study is that, via integrating BIM and ABM, it is possible to model people’s behavior within a three-dimensional digital environment, where decision-makers could be performing simulations such as fire evacuation supported by dynamic, realistic and accurate information.

A Spatial Agent-Based Model of Malaria

2011 ◽  
Vol 3 (3) ◽  
pp. 17-34 ◽  
Author(s):  
S. M. Niaz Arifin ◽  
Gregory J. Davis ◽  
Ying Zhou
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Model ◽  
Spatial Information ◽  
Spatial Models ◽  
Model Verification ◽  
System Capacity ◽  
Agent Based Model ◽  
Agent Based ◽  
Mosquito Abundance ◽  
Adult Female Mosquito

In agent-based modeling (ABM), an explicit spatial representation may be required for certain aspects of the system to be modeled realistically. A spatial ABM includes landscapes in which agents seek resources necessary for their survival. The spatial heterogeneity of the underlying landscape plays a crucial role in the resource-seeking process. This study describes a previous agent-based model of malaria, and the modeling of its spatial extension. In both models, all mosquito agents are represented individually. In the new spatial model, the agents also possess explicit spatial information. Within a landscape, adult female mosquito agents search for two types of resources: aquatic habitats (AHs) and bloodmeal locations (BMLs). These resources are specified within different spatial patterns, or landscapes. Model verification between the non-spatial and spatial models by means of docking is examined. Using different landscapes, the authors show that mosquito abundance remains unchanged. With the same overall system capacity, varying the density of resources in a landscape does not affect abundance. When the density of resources is constant, the overall capacity drives the system. For the spatial model, using landscapes with different resource densities of both resource-types, the authors show that spatial heterogeneity influences the mosquito population.

Agent-Based Semantic Interoperability of Geo-Services

2010 ◽  
pp. 110-127
Author(s):  
Iftikhar U. Sikder ◽  
Santosh K. Misra
Keyword(s):  
Spatial Information ◽  
Description Logics ◽  
Land Use Changes ◽  
Agent Based ◽  
Collaborative Modeling ◽  
Multiple Data ◽  
Agent Interaction ◽  
Agent Collaboration ◽  
One Stop ◽  
Multi Agent

This chapter proposes a multi-agent based framework that allows multiple data sources and models to be semantically integrated for spatial modeling in business processing. The authros introduce a multiagent system (OSIRIS – Ontology-based Spatial Information and Resource Integration Services) to semantically interoperate complex spatial services and integrate them in a meaningful composition. The advantage of using multi-agent collaboration in OSIRIS is that it obviates the need for end-user analysts to be able to decompose a problem domain to subproblems or to map different models according to what they actually mean. The authors also illustrate a multi-agent interaction scenario for collaborative modeling of spatial applications using the proposed custom feature of OSIRIS using Description Logics. The system illustrates an application of domain ontology of urban environmental hydrology and evaluation of decision maker’s consequences of land use changes. In e-government context, the proposed OSIRIS framework works as semantic layer for one stop geospatial portal.

scholarly journals Introducing Spatial Information into Predictive NF-κB Modelling – An Agent-Based Approach

PLoS ONE ◽  
2008 ◽  
Vol 3 (6) ◽  
pp. e2367 ◽  
Author(s):  
Mark Pogson ◽  
Mike Holcombe ◽  
Rod Smallwood ◽  
Eva Qwarnstrom
Keyword(s):  
Spatial Information ◽  
Agent Based

scholarly journals Computational population biology: linking the inner and outer worlds of organisms

2013 ◽  
Vol 59 (1) ◽  
pp. 2-16 ◽  
Author(s):  
Wayne M. Getz
Keyword(s):  
Population Biology ◽  
Dynamic Models ◽  
Spatial Information ◽  
Cultural Shift ◽  
Agent Based ◽  
Share Model ◽  
Advance Research ◽  
Inner World ◽  
Systems Models ◽  
Model Components

Computationally complex systems models are needed to advance research and implement policy in theoretical and applied population biology. Difference and differential equations used to build lumped dynamic models (LDMs) may have the advantage of clarity, but are limited in their inability to include fine-scale spatial information and individual-specific physical, physiological, immunological, neural and behavioral states. Current formulations of agent-based models (ABMs) are too idiosyncratic and freewheeling to provide a general, coherent framework for dynamically linking the inner and outer worlds of organisms. Here I propose principles for a general, modular, hierarchically scalable framework for building computational population models (CPMs) designed to treat the inner world of individual agents as complex dynamical systems that take information from their spatially detailed outer worlds to drive the dynamic inner worlds of these agents and simulate their ecology and the evolutionary pathways of their progeny. All the modeling elements are in place, although improvements in software technology will be helpful; but most of all we need a cultural shift in the way in which population biologists communicate and share model components and the models themselves and fit, test, refute and refine models, to make the progress needed to meet the ecosystems management challenges posed by global change biology.

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