Agimass, FitalewLundhede, Thomas HedemarkPanduro, Toke EmilJacobsen, Jette Bredahl2018-09-112018-06Agimass, F., Lundhede, T., Panduro, T.E. et al. 2018, 'The choice of forest site for recreation: A revealed preference analysis using spatial data', Ecosystem Services, vol. 31, part C, pp. 445-454.2212-041610.1016/j.ecoser.2017.11.016http://hdl.handle.net/2263/66523In this paper, we investigate the factors that can influence the site choice of forest recreation. Relevant attributes are identified by using spatial data analysis from a questionnaire asking people to indicate their most recent forest visits by pinpointing on a map. The main objectives of the study are (1) to examine the preferences of visitors for different forest attributes using data from actual visits and (2) to illustrate how many alternative sites need to be considered for estimation in case of a large number of potential recreational sites. Estimation is performed using a conditional logit as well as a random parameter logit model. The variables that are found to affect the choice of forest site to a visit for recreation include: forest area, tree species composition, forest density, availability of historical sites, terrain difference, state ownership, and distance. Regarding the second research objective, we empirically show the possibility of getting consistent parameter estimates through random selection of alternatives. We find that increasing the number of alternatives increases consistency of parameter estimates.en© 2018 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Ecosystem Services. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Ecosystem Services, vol. 31, part C, pp. 445-454, 2018. doi : 10.1016/j.ecoser.2017.11.016.Discrete choicesForest recreationRevealed preferenceSpatial dataSampling of alternativesBehaviorHeterogeneityValuationManagementDemand modelsStructural attributesPublic preferencesRandom utility modelPostprint Article