RBSim is a computer program that simulates the behavior of human recreators in high use natural environments. Specifically RBSim uses concepts from recreation research and Artificial Life and combines them with geographic information systems to produce an integrated system for exploring the interactions between different recreation user groups within real geographic space. RBSim joins two computer technologies:
RBSim is experimental at this stage, but demonstrates the potential of combining the two technologies to explore the complex interactions between humans and the environment. The implications of this technology should also be applicable to the study of wildlife populations and other systems where there are complex interactions in the environment.
An autonomous agent is a computer simulation which is based on concepts from Artificial Life research. Agent simulations are built using object oriented programming technology. The agents are autonomous because once they are programmed they can move about the landscape like software robots. The agents can gather data from their environment, make decisions from this information and change their behavior according to the situation they find themselves in. Each individual agent has it’s own physical mobility capabilities, sensory capabilities, and cognitive capabilities. This results in behavior that echo’s the behavior of real animals (in this case humans) in the environment.
The process of building an agent is iterative and combines knowledge derived from empirical data with the intuition of the programmer. By continuing to program knowledge and rules into the agent, watching the behavior resulting from these rules and comparing it to what is known about actual behavior, a rich and complex set of behaviors emerge. What is compelling about this type of simulation is that it is impossible to predict the behavior of any single agent in the simulation and by observing the interactions between agents it is possible to draw conclusions which are impossible using any other analytical process.
Until now, there has been no tool for recreation managers and researchers to systematically investigate different recreation management options. Much of the research is based on interviews or surveys, but this information fails to inform the manager/researcher how different management options might affect the overall experience of the user. For example if a new trail is introduced, we might expect that conflicts might be reduced, but to what extent? If we go to a system of scheduling use, what is the impact on the number and frequency of users. More importantly when you have different, conflicting recreation uses, how do different management options increase or decrease the potential conflicts?
None of these questions can be answered using conventional tools. These questions all pivot around issues such as time and space as well as more complex issues such as intervisibility between two locations on a map. By combining human agent simulations with geographic information systems it is possible to study all these issues simultaneously and with relative simplicity.
RBSim developed as a synthesis of work over a ten year period by researchers in the U.S. and Australia. Randy Gimblett, an Associate Professor of Landscape Architecture in the School of Renewable Natural Resources, The University of Arizona has been studying recreation behavior in forest land in the western U.S. In his PhD research Gimblett collected data on three recreation groups in Broken Arrow Canyon near Sedona, Arizona. The Canyon is popular for hikers, mountain bikers and people on commercial jeep tours because of the unique spectacular desert scenery of eroded red sandstone. Sedona is well know by New Age enthusiasts for its "Spiritual Vortex". This, combined with the close proximity to the Grand Canyon, Monument Valley and the Navaho and Hopi Indian Reservations, make it an important tourist destination by visitors around the world.
The upshot of this popularity is a problem common to many popular wildland recreation destinations. People are "loving the place to death" by overuse. This overuse not only has negative impacts on the landscape but also in the quality of the experience people have when they visit. Crowding, conflicts between hikers, mountain bike enthusiasts and jeep tours can create negative experiences in what should be a spectacular and memorable landscape setting.
The U.S. Forest Service is responsible for managing the resource. They must have guidance on what actions to take to protect the environment and provide the best possible recreation experience for visitors. Options include building new trails, limiting the number of visitors, or relocating existing trails. Any of these strategies or combinations of these strategies has complex consequences on the net experience of visitors. There have been no tools available to natural resource managers to study these complex interactions. RBSim has been developed to address these complex issues by using computer simulation technology. By simulating human behavior in the context of geographic space, it is possible to study the number and type of interactions visitors will have within each group and between groups.
Bob Itami, Director of Digital Land Systems Research has been a colleague of Randy Gimblett for over 12 years. He is the author of SAGE GIS and has worked in the area of recreation and natural resource planning for years. Bob has a keen interest in environmental simulation and when Randy decided to pursue his PhD research at the University of Melbourne it was an ideal opportunity to join heads to develop a system which combined object oriented simulation models with traditional GIS systems. The result is RBSim.
The simulation interface provides a mechanism for the user to control input variables, while the simulation engine keeps track of timing and all global parameter. The agent object model is the heart of the simulator which serves to characterize agents and define subclasses for each agent. The trail object model is a member of the agent object class communicates directly with recreator objects developed specifically for this application from the survey data. In a typical run, agents are created from input parameters randomly, respond to a specific set of rules. Depending on the agent they varying in speed, energy levels and objective or goals. Landscape agents are basically solitude seeking, They traverse the canyon minimizing encounters with other agents. These agents will pass other agents along the way, stop at scenic lookouts only when there are less that four other recreators present. These behaviors coincide with the desired benefits extracted earlier in the survey. Seeking physical exercise, avoiding crowds and enjoying nature are strongly correlated benefits of this group of recreators.
All agents keep track of visual and physical encounters along the way. A hiker’s visual encounter for example is from along the hiking trail. Hikers in front of the evaluating agent are summarized for each cell that agent is currently in. At the same time each agent is evaluating biker and jeep agent using other trails in the database. All agents detect explicit locations of other agents and determine whether they are visible or not. Each agent then has a record of where and how many visual encounters it has with other agents as it is moving through the landscape over time.
The summary statistics for each cell are recorded for each agent in the simulation. Agent type, age, personality, column, row, energy, energy expended, velocity, physical encounters with other agents, visual counters with other agents, time viewing landscape, time resting is stored for each agent are all stored. This information is subsequently used to determine spatially represented physical and visual encounters and movement patterns along the trails. This information is used to graph the number of encounters overtime for each of the agents being studied as illustrated in figure 1 and as a three-dimensional image figure2. Maps of conflict areas or concentrations of encounters along the trails can be directly obtained from this software and translated in to any GIS format.
Figure 1 - Output of a hikers visual and physical encounters with other agents during a simulation run.
Figure 2 - Three dimensional view of encounters along the trail from encounters in figure 1.
For more information contract:
Dr. Randy Gimblett
Bob Itami
Deadman, P., E Schlager & H. R. Gimblett Simulating Common Pool Resource Management Experiments with Adaptive Agents Employing Alternate Communication Routines. Journal of Artificial Societies and Social Simulation vol. 3, no. 2. 2000
Gimblett, H.R., T. Daniel & M. J. Meitner. 2000. An Individual-based Modeling Approach Simulating Recreation Use in Wilderness Settings. In: Cole, David N.; McCool, Stephen F. 2000. Proceedings: Wilderness Science in a Time of Change. Proc. RMRS-P-000. Ogden. UT; U.S. Department of Agriculture, Forest Science, Rocky Mountain Research Station.
Gimblett, H.R., T.C. Daniel & C. Roberts. Grand Canyon River Management: Simulating Rafting the Colorado River through Grand Canyon National Park Using Spatially Explicit Intelligent Agents. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4):Problems, Prospects and Research Needs, Banff, Alberta, Canada, September 2-8, 2000.
Itami, R. M., G. S. MacLaren, K. M. Hirst, R. J. Raulings & H. R. Gimblett. RBSIM 2: Simulating human behavior in National Parks in Australia: Integrating GIS and Intelligent Agents to predict recreation conflicts in high use natural environments. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4):Problems, Prospects and Research Needs, Banff, Alberta, Canada, September 2-8, 2000.
Gimblett, H.R., R.M. Itami & M. Richards. Simulating Wildland Recreation Use and Conflicting Spatial Interactions using Rule-Driven Intelligent Agents. In H. R Gimblett, editor. Integrating GIS and Agent based modeling techniques for Understanding Social and Ecological Processes. Oxford University Press. 2000.
Daniel, T. & R. Gimblett. Autonomous Agent Model to Support River Trip Management Decisions in Grand Canyon National Park. International Journal of Wilderness Special Issue on Wild Rivers. 2000
Gimblett, H. R., M.T. Richards & R. M. Itami. RBSim: Geographic Simulation of Wilderness Recreation Behavior. Journal of Forestry. (Forthcoming).
Itami R., R. Gimblett, R. Raulings , D. Zanon , G. MacLaren , K. Hirst , B. Durnota. RBSim: Using GIS-Agent simulations of recreation behavior to evaluate management scenarios. Aurisa 99 – The Spatial Information Association. Australasian & Regional Information Systems Conference. November 22-16, 1999.
Gimblett, H.R. & R.M. Itami. Modelling the Spatial Dynamic and Social Interaction of Human Recreators Using GIS and Intelligent Agents. International Congress on Modelling and Simulation. Hobart, Tasmania. December 8-11, 1997.
Roberts, C. & R. Gimblett. Computer Simulation for Rafting Traffic on the Colorado River Fifth Biennial Conference of Research on the Colorado Plateau. October 25-29th, 1999. Flagstaff, Arizona.
Gimblett, H. R., B. Durnota & R.M. Itami. Spatially-Explicit Autonomous Agents for Modelling Recreation Use in Wilderness. Complex International Journal. Vol. 3 1997.
Gimblett, H. R., R. M. Itami & D. Durnota. Some Practical Issues in Designing and Calibrating Artificial Human-Recreator Agents in GIS-based Simulated Worlds. Workshop on Comparing Reactive (ALife-ish) and Intentional Agents. Complex International Journal. Vol. 3 1997.
Gimblett, H.R. Simulating Recreation Behaviour in Complex Wilderness Landscapes Using Spatially-Explicit Autonomous Agents. Unpublished dissertation. University of Melbournre, Parkville, Australia. 1997.
Deadman, P., & Gimblett, H.R. The Role of Goal-Oriented Autonomous Agents in Modeling People-Environment Interactions in Forest Recreation. Mathematical and Computer Modelling. Volume 20, Number 8, October, 1994.
Gimblett, H.R. Virtual Ecosystems. AI Applications in Natural Resources, Agriculture, and Environmental Science Journal. Volume 8, No. 1. Winter, 1994. Pgs. 77-81.
Saarenmaa, H. & Gimblett, H. R. Preface to the Special Issue on Object-Oriented Modelling of Natural and Artificial Agents in Ecosystem and Natural Resource Management. Mathematical and Computer Modelling. Volume 19, Number 9, November, 1994.
Gimblett, H.R., Ball, G. L. & Guisse, A.W. Autonomous Rule Generation and Assessment for Complex Spatial Modeling. Landscape and Urban Planning Journal. 30 (1994) 13-26.
Gimblett, H.R. & Ball, G. L. An Exploration and Application of Neural Network Architectures for Monitoring and Simulating Changes in Forest Resource Management. AI Applications. Natural Resources, Agriculture, and Environmental Science. Vol. 9. No. 1, 1995.
Harris, L. K., H.R. Gimblett, H.R. & W. W. Shaw. Multiple Use Management: Using a GIS Model to Understand Conflicts Between Recreationists and Sensitive Wildlife. Society and Natural Resources, Volume 8, pp. 559-572. 1995.
Ball, G. L. and Gimblett, H.R. Spatial Dynamic Emergent Hierarchies Simulation and Assessment System. Ecological Modelling, 62 (1992). Pgs. 107-121.
Deadman, P., Brown, R. D. Gimblett, H.R. Modelling Rural Residential Settlement Patterns with Cellular Automata. Journal of Environmental Management, (1993) 37. Pgs. 147-160.
Loading RBSim
After you have acquired the RBSim_code.zip, unzip the code and then run the setup file to install RBSim.
Professor
School of Natural Resources
University of Arizona
Tucson, Arizona 85721
(520) 621-6360
gimblett@ag.arizona.edu
http://www.srnr.arizona.edu/~gimblett
Geodimensions Pty Ltd
16 Tullyvallin Cres
Sorrento, Victoria 3943
AUSTRALIA
email: bob.itami@geodimensions.com.au
phone: (03) 5984 3409
International: +61 3 5984 3409
Mobile: 042 787 6100
References
Itami, R. M., H. R. Gimblett, Intelligent Recreation Agents in a Virtual GIS World.In Proceedings of Complex Systems 2000 Conference. November 19-22, 2000. University of Otago, Dunedin, New Zealand.