Simulation of outdoor recreation: the importance of empirical data

Managers of nature areas need adequate and reliable data about visitor use in order to make effective decisions on both nature protection and recreational use. The last decades several recreation simulation models have been developed as tool to understand and predict how distributions of recreational use are likely to change in response to management actions (e.g. development of new trails, installation of new parking lots). Besides measuring visitor use levels and testing alternative management practices, simulation models can also help to improve communication of ideas between managers and the public. Without doubt the models should generate reliable results that represent real visitor behaviour. Scientists agree that the design of simulation models should be grounded in reality, i.e. the behaviour of agents should be based on empirical data. However, in practice it is often too costly to conduct an empirical study to gather input for simulation models. The requirement of initial data – e.g. spatial data, data specific to various user groups, and ecological data – frequently leads to the rejection of the modelling approach, simply due to the lack of resources. An often applied solution is working with qualitative input data from stakeholder interviews on recreational behaviour. Nevertheless, availability of high quality data is important, since any computer simulation is only ever as good as the data on which it is based. This paper explains how the calibration of MASOOR (Multi Agent Simulation Of Outdoor Recreation) with the help of theoretical insights and empirical data contribute to its improvement in terms of reliability. The calibration refers not only to the settings of behavioural rules, but also to additional input data on visitor numbers, and their characteristics in time and space. The calibration consists of three steps. The first model is based on secondary data and management information and simulates behaviour of a general visitor. The second model is elaborated with empirical data and also simulates a general visitor. Finally, the third model distinguishes four visitor types. These visitor types are based on the meanings they ascribe to the environment they visit. The outcomes of the simulation models are correlated to actual visitor behaviour that is recorded by means of GPS. The results show the importance of empirical data and discusses the findings related to the notion that the strength of a model lies not only in its validity, but also in its simplicity.