Wenwu Tang

Spatial memory and animal movement

Memory is critical to understanding animal movement but has proven challenging to study. Advances in animal tracking technology, theoretical movement models and cognitive sciences have facilitated research in each of these fields, but also created a need for synthetic examination of the linkages between memory and animal movement. Here, we draw together research from several disciplines to understand the relationship between animal memory and movement processes. First, we frame the problem in terms of the characteristics, costs and benefits of memory as outlined in psychology and neuroscience. Next, we provide an overview of the theories and conceptual frameworks that have emerged from behavioural ecology and animal cognition. Third, we turn to movement ecology and summarise recent, rapid developments in the types and quantities of available movement data, and in the statistical measures applicable to such data. Fourth, we discuss the advantages and interrelationships of diverse modelling approaches that have been used to explore the memory-movement interface. Finally, we outline key research challenges for the memory and movement communities, focusing on data needs and mathematical and computational challenges. We conclude with a roadmap for future work in this area, outlining axes along which focused research should yield rapid progress.

Modelling adaptive, spatially aware, and mobile agents: Elk migration in Yellowstone

The potential utility of agent‐based models of adaptive, spatially aware, and mobile entities in geographic and ecological research is considerable. Developing this potential, however, presents significant challenges to geographic information science. Modelling the spatio‐temporal behaviour of individuals requires new representational forms that capture how organisms store and use spatial information. New procedures must be developed that simulate how individuals produce bounded knowledge of geographical space through experiential learning, adapt this knowledge to continually changing environments, and apply it to spatial decision‐making processes. In this paper, we present a framework for the representation of adaptive, spatially aware, and mobile agents. To provide context to this research, a multiagent model is constructed to simulate the migratory behaviour of elk (Cervus elaphus) on Yellowstones northern range. In this simulated environment, intelligent agents learn in ways that enable them to mimic real‐world behaviours and adapt to changing landscapes.

HPABM: A Hierarchical Parallel Simulation Framework for Spatially-explicit Agent-based Models

A Hierarchical Parallel simulation framework for spatially-explicit Agent-Based Models (HPABM) is developed to enable computationally intensive agent-based models for the investigation of large-scale geospatial problems. HPABM allows for the utilization of high-performance and parallel computing resources to address computational challenges in agent-based models. Within HPABM, an agent-based model is decomposed into a set of sub-models that function as computational units for parallel computing. Each sub-model is comprised of a sub-set of agents and their spatially-explicit environments. Sub-models are aggregated into a group of super-models that represent computing tasks. HPABM based on the design of super- and sub-models leads to the loose coupling of agent-based models and underlying parallel computing architectures. The utility of HPABM in enabling the development of parallel agent-based models was examined in a case study. Results of computational experiments indicate that HPABM is scalable for developing large-scale agent-based models and, thus, demonstrates efficient support for enhancing the capability of agent-based modeling for large-scale geospatial simulation.

Visualizing the impact of space-time uncertainties on dengue fever patterns

In this article, we evaluate the impact of positional and temporal inaccuracies on the mapping and detection of potential outbreaks of dengue fever in Cali, an urban environment of Colombia. Positional uncertainties in input data are determined by comparison between coordinates following an automated geocoding process and those extracted from on-field GPS measurements. Temporal uncertainties are modeled around the incubation period for dengue fever. To test the robustness of disease intensities in space and time when accounting for the potential space-time error, each dengue case is perturbed using Monte Carlo simulations. A space-time kernel density estimation (STKDE) is conducted on both perturbed and observed sets of dengue cases. To reduce the computational effort, we use a parallel spatial computing solution. The results are visualized in a 3D framework, which facilitates the discovery of new, significant space-time patterns and shapes of dengue outbreaks while enhancing our understanding of complex and uncertain dynamics of vector-borne diseases.

A parallel agent-based model of land use opinions

In this article we present a parallel agent-based model (ABM) to support large-scale simulations of land use change. ABMs are a commonly used simulation approach for the investigation of land use systems. The computationally intense nature of these models, however, often prohibits the development of models that fully capture the complex dynamics of land use systems when using typical desktop computing environments. The search for scientific understanding and solutions to real-world problems is, therefore, often limited by an inability to explore a wide range of scales or the impact of complex interactions. Parallel computing provides a potential solution for this limitation issue. Our ABM is designed using parallel computing to simulate the formation of large-scale land use opinions within spatially explicit environments. Agents, environments, and interactions among agents are distributed among processors through parallel computing strategies, including spatial domain decomposition, ghost zones, and synchronization. We examine the computational performance of the model within a supercomputing environment. It is demonstrated that by leveraging increasingly available high-performance parallel computing resources large-scale ABMs of land use systems can be developed and, ultimately, underlying processes that drive these systems better understood.

A communication-aware framework for parallel spatially explicit agent-based models

Parallel spatially explicit agent-based models (SE-ABM) exploit high-performance and parallel computing to simulate spatial dynamics of complex geographic systems. The integration of parallel SE-ABM with CyberGIS could facilitate straightforward access to massive computational resources and geographic information systems to support pre- and post-simulation analysis and visualization. However, to benefit from CyberGIS integration, parallel SE-ABM must overcome the challenge of communication management for orchestrating many processor cores in parallel computing environments. This paper examines and addresses this challenge by describing a generic framework for the management of inter-processor communication to enable parallel SE-ABM to scale to high-performance parallel computers. The framework synthesizes four interrelated components: agent grouping, rectilinear domain decomposition, a communication-aware load-balancing strategy, and entity proxies. The results of a series of computational experiments based on a template agent-based model demonstrate that parallel computational efficiency diminishes as inter-processor communication increases, particularly when scaling a fixed-size model to thousands of processor cores. Therefore, effective communication management is crucial. The communication framework is shown to efficiently scale up to 2048 cores, demonstrating its ability to effectively scale to thousands of processor cores to support the simulation of billions of agents. In a simulated scenario, the communication-aware load-balancer reduced both overall simulation time and communication percentage improving overall computational efficiency. By examining and addressing inter-processor communication challenges, this research enables parallel SE-ABM to efficiently use high-performance computing resources, which reduces the barriers for synergistic integration with CyberGIS.

Towards provenance-aware geographic information systems

GIS (Geographic Information Systems) play an important role to acquire and communicate geospatial knowledge based on spatial data and the use of spatial analysis, modeling, and visualization. The assurance of the validity and quality of spatial data handling and analysis remains a great challenge, in part, because of sophisticated procedures are often required for collaborative geospatial problem-solving and decision making. These procedures, when specified as knowledge derivation workflows, require carefully configured parameters and spatiotemporal specifications guided by specific contexts and purposes. The information of spatial data lineage and related analysis workflow is defined as spatial provenance in this research. Such information is often not well recorded or managed during spatial data handling and related analysis. This paper presents a provenance-aware GIS architecture that incorporates spatial provenance to address this shortcoming and facilitate the assurance of validity and quality of spatial data handling and analysis. Spatial provenance in this architecture is generated and managed to allow queries on data lineage and workflow information to support geospatial problem-solving. Basic elements of spatial provenance are captured using a spatial provenance model. The illustration of the provenance-aware GIS architecture and its proof-of-concept implementation reveals the similarity and difference in the use of spatial provenance in GIS applications. Overall, the architecture and implementation described in the paper demonstrates the necessity and feasibility of introducing provenance into GIS.

Agent-based modeling within a cyberinfrastructure environment: a service-oriented computing approach

Agent-based models (ABM) allow for the bottom-up simulation of dynamics in complex adaptive spatial systems through the explicit representation of pattern–process interactions. This bottom-up simulation, however, has been identified as both data- and computing-intensive. While cyberinfrastrucutre provides such support for intensive computation, the appropriate management and use of cyberinfrastructure (CI)-enabled computing resources for ABM raise a challenging and intriguing issue. To gain insight into this issue, in this article we present a service-oriented simulation framework that supports spatially explicit agent-based modeling within a CI environment. This framework is designed at three levels: intermodel, intrasimulation, and individual. Functionalities at these levels are encapsulated into services, each of which is an assembly of new or existing services. Services at the intermodel and intrasimulation levels are suitable for generic ABM; individual-level services are designed specifically for modeling intelligent agents. The service-oriented simulation framework enables the integration of domain-specific functionalities for ABM and allows access to high-performance and distributed computing resources to perform simulation tasks that are often computationally intensive. We used a case study to investigate the utility of the framework in enabling agent-based modeling within a CI environment. We conducted experiments using supercomputing resources on the TeraGrid – a key element of the US CI. It is indicated that the service-oriented framework facilitates the leverage of CI-enabled resources for computationally intensive agent-based modeling.

Toward an understanding of provenance in complex land use dynamics

Landscapes are complex adaptive spatial systems driven by biophysical and socioeconomic processes that are shaped by the cumulative behavior of interacting, but independent decision-makers. Agent-based modeling has been identified as an effective paradigm for the study of such systems because of its ability to represent behavior and interaction at the individual level. These interactions are inherently associated with cause–effect relations in the transition of land use systems through time. Elucidating specific cause and effect relations from model results, however, poses a significant challenge to the research community because of complicated model structures and the complex processes that drive system dynamics. In this article, we call for provenance-based agent-based models of land use dynamics to support the capture and tracking of complex cause–effect relations. We illustrate the importance of explicitly considering provenance in agent-based modeling through the development of a spatially explicit agent-based land use simulation framework. This framework is designed to simulate complex land use dynamics in southwest Montana, USA. Land managers are modeled as geospatial agents who interact with their spatially explicit environments. These agents exchange opinions about land use controls through social networks in an attempt to form consensus on land use policies. The heterogeneous opinion behavior of landowner agents and complicated social structural relationships produce complex dynamics. We conduct experiments to demonstrate the need to explicitly consider provenance in agent-based models of land use systems and its ability to promote our understanding of complex adaptive land use systems.

Simulating Complex Adaptive Geographic Systems: A Geographically Aware Intelligent Agent Approach

The objective of this paper is to present a spatially explicit agent-based simulation framework with a supporting software package to explore complex adaptive geographic systems. This framework is particularly suitable for modeling entities that are contextually aware, knowledge driven, and adaptive because it represents them as geographically aware intelligent agents. Fundamental advances in the explicit representation of contextual information, knowledge structures, and learning processes are needed for modeling intelligent agents situated within geographic systems. The representation of these agents requires the integration of agent-based models, machine learning, and GIS. Existing software packages for agent-based modeling, however, often provide insufficient support for this integration. The agent-based simulation package presented here is specifically designed to achieve such integration by assisting the development of agent-based models from the simulation framework. Object-oriented modeling techniques were used to implement this simulation package, which includes four modules: simulation, visualization, learning, and geoprocessing. In particular, the learning and geoprocessing modules facilitate the representation of adaptive behavior in agents within spatially explicit environments. The utility of the agent-based simulation package is illustrated using two simulation models: one of adaptive elk behavior and another of pedestrian movement. The successful design of the simulation models suggests that the modeling framework with the supporting software package is well suited to the resolution of complex adaptive geographic problems.