Randy Gimblett

RBSim 2: Simulating the Complex Interactions between Human Movement and the Outdoor Recreation Environment

Abstract This paper describes advancements in recreation management using new technology that couples Geographic Information Systems (GIS) with Intelligent Agents to simulate recreation behaviour in real world settings. RBSim 2 (Recreation Behaviour Simulator) is a computer simulation program that enables recreation managers to explore the consequences of change to any one or more variables so that the goal of accommodating increasing visitor use is achieved while maintaining the quality of visitor experience. RBSim provides both a qualitative understanding of management scenarios by the use of map graphics from a GIS as well as a quantitative understanding of management consequences by generating statistics during the simulation. Managers are able to identify points of over crowding, bottlenecks in circulation systems, and conflicts between different user groups. RBSim 2 is a tool designed specifically for the purposes of simulating human recreation behaviour in outdoor environments. The software is designed to allow recreation researchers and managers to simulate any recreation environment where visitors are restricted to movement on a network (roads, trails, rivers, etc.). The software architecture is comprised of the following components: • GIS module to enter travel network, facilities, and elevation data • Agent module to specify tourist personality types, travel modes, and agent rules • Typical Trip planner to specify trips as an aggregation of entry/exit nodes, arrival curves, destinations and agents. • Scenario designer to specify combinations of travel networks, and typical trip plans. • Statistical module to specify outputs and summarise simulation results This paper describes the RBSim software architecture with specific reference to the trip planning algorithms used by the recreation agents. An application of the simulator at Port Campbell National Park, Victoria Australia is described.

The simulation and visualization of complex human–environment interactions

Abstract The purpose of this paper is to describe ongoing research work into the development of a simulation system that integrates statistical analysis, simulation and visualization with computer modeling for analyzing the complex human–environment interactions in dynamic settings. The simulation system allows individuals to explore many facets of human–environment interactions and resulting impacts and compare these results to field-tested methods of actual conditions. More specifically, the research explores procedures for representing the human decision-making process, behavior patterns and associated impacts within a dynamic environment. This tool provides opportunities to (1) develop methods to extract from human behavior and physical systems data, rules which define how individuals communicate, and interact with each other and their environment; (2) develop automated techniques for statistically comparing actual human/environment interactions and associated impacts with simulated outcomes; and (3) the use of visualization methods for evaluating simulation outcomes against actual human–environment interactions. This research builds on data collected on human use, associated resource impacts and monitoring of environmental conditions along the Colorado River through Grand Canyon National Park. Both ecological (beach morphology, river flow rates) and social (human and recreation use and impact) data have all been collected on the Colorado River ecosystem. This data will be used to test, evaluate and demonstrate the effectiveness of simulation and visualization techniques in solving complex problems in a dynamic, sensitive ecosystem. The Grand Canyon River Trip Simulation system is currently being tested to confirm the veracity of river trip projections provided by the model.

Autonomous rule generation and assessment for complex spatial modeling

Abstract Traditional geographic information systems (GISs) have provided a mechanismto allocate the individual weightings of physical variables for single spatial modeling applications. However, as the complexity of the decision-making process increases, as in conflicting resource allocation modeling, examination of many more complex resource and non-resource relationships becomes tedious and extremely difficult. What is needed is a technique to assist the modeler in effectively, and correctly, defining and evaluating large rule sets for complex spatial analysis. In this paper we will briefly review traditional techniques for defining rules used in combining large variable sets, outline a approach using genetic algorithms and neural network techniques for automated rule generation, and discuss the implementation of this approach in Hoosier National Forest. Results of this study clearly demonstrate the potential use of this approach for modeling complex resource problems.

Multiple use management: using a GIS model to understand conflicts between recreationists and sensitive wildlife

The management of natural resources involves the integration of physical, biological, and sociological information. Computerized geographical information systems (CIS) offer the capacity to combine this information. GIS has the potential to be a powerful wildlife management tool for integrating recreational and biological information. This study develops a recreational use model using data from a traditional recreational survey and a mountain sheep habitat model within a GIS database. This study develops a method that provides the resource manager with a toot to make predictions about the locations where recreational users may be encroaching on mountain sheep. Findings of this study illustrate that frequent recreational use along two trails occurred within preferred sheep habitat. More than 45% of recreationists surveyed travel extensively off the designated trails into the wilderness areas and thereby further encroach upon sheep habitat. This study demonstrates the use of a GIS to identify critical habit...

Perceptual values in the cultural landscape : a computer model for assessing and mapping perceived mystery in rural environments

Abstract Landscape perceptual theories developed within the last decade which have been founded on a cognitive structure have clearly provided a useful, yet generalizable basis for improving the mapability of scenic assessment models. With the increase usage of Geographic Information Systems (GIS) and subsequently the development of more sophisticated spatial models, the application of such scenic assessment models are more easily validated and refined. This study looks at the mystery component of the Kaplans “Information Processing Model” and develops a quantitative procedure for predicting and mapping mystery in the rural Indiana landscape. Ninety (90) color slides of rural landscape scenery were presented to 26 respondents who rated each photograph on a five point scale for mystery. Landscape composition classes were used to discover the interrelationships of the mystery dimensions and the physical landscape variables affecting their perception and were used to develop a model for mapping mystery. This research strongly supports the use of Kaplans Information Processing Model as a reliable, comprehensive theoretical foundation for improving landscape assessment procedures. In addition, this research demonstrates how the mystery concept can be validated using a representative sample of the public which can be subsequently used for developing measures that both identify and describe physical landscape variables for mapping perceptual values of mystery in the rural Indiana landscape.

Deriving artificial models of visitors from dispersed patterns of use in the Sierra Nevada Wilderness, California

Summary Natural resource managers are faced with a complex problem of understanding human use patterns and associated impacts in dispersed recreation wilderness settings. While conventional approaches to modelling have limited use in acquiring and understanding such complex associations, spatial simulation models have been proposed as an alternative. The purpose of this paper is to describe a project whose focus is on a dispersed recreation context of backpacking trips and commercial packstock operations in the John Muir Wilderness in the Sierra Nevada Mountains in California. This paper will discuss the data collection and synthesis to derive agent profiles and rules as a precursor to the development of a dynamic, agent based model that represents the spatial distribution of visitation patterns.

Spatial dynamic emergent hierarchies simulation and assessment system

Abstract Over the last decade, concerns over the fragility of the earths resources resulting from centuries of human-related activities have, like never before, captured the attention of environmental policy and decision-makers. To simulate an ecosystem using computer modelling will require a design which tends to emulate a natural system in its construction. This paper describes the development of a modeling framework referred to as Spatial Dynamic Emergent Hierarchies Simulation and Assessment System (SDEHSAS), a tool for the evaluation and simulation of environmental and ecological processes. We discuss the four key technological areas of the assessment system: data base management systems (DBMS); DBMS integration pathway; intra-system communications; and self-adapting models currently being developed as methods for solving the spatial and temporal scale as well as multiple model interactions. SDEHSAS provides a road map for the development of advanced models that can be used with data bases representing different environmental conditions, running on distributed systems and having the capacity of tracking process interactions. SDEHSAS can provide modelers with a common protocol that will allow the development of complex spatial simulations to be addressed more easily and with less trouble.

Linking perception research, visual simulations and dynamic modeling within a GIS framework: the Ball State experience

Abstract The explosion of information technology (and especially in the areas of GIS and Remote Sensing development) is having a significant impact on the quality and effectiveness of natural resource analysis and planning. Incorporated into the information technology boom are new and exciting ways to realistically capture and manipulate digital data to enhance our visual understanding of various resource planning and design decisions. Work undertaken in university environments is assisting in the research and development aspects of applying GIS technology and image capture capabilities to the visualization and management of natural resources, and Ball State University is no exception. This paper provides a comprehensive summary of research work undertaken in Ball State Universitys Computer Graphics Laboratory that not only incorporates public values in the planning process but also develops innovative technologically based methods to assist planners in analyzing, assessing and simulating landscape scenery.

Linking Participatory, Bayesian, and Agent-Based Modeling Techniques to Simulate Coupled Natural-Human System: A Case Study with Ranchers in Sonora, Mexico

Conflicts for water between human use and ecological function have increased in recent years for semi-arid rivers. In the semi-arid Rio Sonora watershed, precipitation varies widely both annually and seasonally. In a harsh semi-arid climate with limited water resources, ranching remains one of the few remaining options to support livelihoods. Water resources are an important component to ranching operations, but are limited and costly. The method a rancher adopts to manage his herd is one of many decisions heavily influenced by both water and money. In an uncertain political and environmental climate, concern rises about the future of the rancher industry, rancher families, and their way of life. As a case study for the Rio Sonora Watershed, a series of workshops were held in the city of Rayon, Sonora, Mexico. The purpose of the workshops was to identify important decisions made by ranchers in the study area, and attempt to predict the likelihood of their choices under various environmental conditions. We applied a new methodology for characterizing decision-making, Bayesian cognitive mapping, which creates a probability of the likelihoods decisions will be made. The construction of a Bayesian cognitive map requires stakeholder involvement in two steps: development of an acyclic graph structure and data collection. The Bayesian cognitive map of Sonoran ranchers is then be applied to an agent-based model to incorporate temporal dynamics. Developing Bayesian cognitive maps of human decision-making is a time-intensive endeavor; however, the approach is holistic, easy to use, encourages stakeholder participation, and values individual variation. This chapter provides an introduction into how Bayesian cognitive maps can be created via participatory approaches and translated into an agent-based model.

Level of Sustainable Activity: A Framework for Integrating Stakeholders into the Simulation Modeling and Management of Mixed-Use Waterways

This chapter presents a framework called “Level of Sustainable Activity” (LSA) that integrates the boating community, business, and management into a process for analyzing and interpreting the outputs of agent-based vessel simulations to define waterway vessel capacities and management strategies. This chapter describes the LSA framework and explores its application in two very diverse contexts: an urban river (Yarra River) in Melbourne, Australia, and; a vast wilderness waterway (Prince William Sound) in Alaska.