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This paper considers the most suitable market segment(s) from an environmental and local economic development perspective in the specific context of visits to natural environments. More specifically, the paper explores the distinctions and differences between tourists (non-residents) and residents with regard to visit behavior at natural attractions. By using the CHAID algorithm, a decision tree is constructed for means of transportation which serves as a key factor in the segmentation process. However, such a tree for visitors' resident or non-resident status cannot be built as a first explicative variable, unless it is statistically forced. Once it is forced, the tree opens in several sub-segments, for non-residents and residents alike. Finally, it allows understanding of the means of transportation used by visitors according to their geographical origin as well as a set of added independent variables: accommodation establishment, length of stay, season, and other demographic variables (educational level, gender, and age). Also, more importantly, we have obtained segments with no overlap configured according to all the aforementioned variables. This is a very strong result from a methodological point of view and for policy makers in destination settings.

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The analysis of seasonality and domestic tourism from the perspective of the accommodation sector has, to date, been unsatisfactorily studied in the domain of visitors to national parks. In light of the scale of accommodation development and its crucial role in tourism, most notably with regard to environmental impact, in-depth knowledge about accommodation market segments and their specific characteristics and patterns of behavior are integral to the development of tourism policy. In the context of domestic tourism, underpinned by an understanding of the theory of planned behavior and push-and-pull motivations, this study examines seasonality and accommodation type used by visitors to national parks in the small islands of the Canaries, Spain with the objective being to design the most appropriate environmental tourism policy. By adopting a Multinomial logistic regression model with 1671 surveys carried out, the study concludes that domestic visitors demonstrate a higher propensity to select environmental-friendly accommodation types during the high season. At the same time, residents who visit the parks in high season assess the lowest value to the preservation level of the parks’ natural resources.

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