Abstract
From the beginning of the 21st century, following major European and global initiatives such as the Millennium Ecosystem Assessment (2005) [1, 2] and The Economics of Ecosystem and Biodiversity [3], the approach of Ecosystem Services could be considered an effective way to rebuild the traditional approach oriented to identify the impacts of territorial transformations in decision making processes. This research is oriented to contribute to the wider methodological framework of the Millennium Ecosystem Assessment [1]. Starting from this, the present work contributes to build interpretative models for the evaluation of a relevant part of the fourth class of ecosystem services: the territorial touristic attractiveness. The INVEST model, an open source toolkit, has been applied to assess the attractiveness of the Basilicata Region considering both natural and cultural heritage in order to highlight strengths and weaknesses of the investigated methodology, compared with Strategic development perspectives (also defined Smart Specialization Strategy).
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Notes
- 1.
INVEST- related documentation is available online at http://data.naturalcapitalproject.org/nightly-build/invest-users-guide/html/index.html.
- 2.
Based on these variables, the software processes a linear regression model. The regression equation used is the following:
$$ {\text{y}}\,{ = }\,\upbeta_{0} \, + \,\upbeta_{ 1} {\text{x}}1\, + \,\upbeta_{ 2} {\text{x}}2\, + \, \ldots \, + \,\upbeta_{\text{i}} {\text{xi}}\,{ + }\,{\text{e}}_{\text{i}} \,\,\,\,\,\,{\text{i = 1,}} \ldots , {\text{N}} $$(1)where: βi are the linear regression coefficients; xi are the territorial components considered as predictive variables to input into the software; y matches with the expected value of the model, which in the specific case is the Basilicata region tourism specialization level.
Linear regression analysis is a technique that allows to analyse the linear relationship between a dependent variable (or response variable) and one or more independent variables (or predictors). It is an asymmetric methodology that is based on the hypothesis of the existence of a cause-effect relationship between several variables. The equation shown here contributes to the formation of a global index: the regional tourism attraction index (in this work). The template was applied on each domain and on proper combinations of domains to identify the most significant variable combination. The determination coefficient, better known as R2, is used as a measure of the good adaptation of the multiple linear regression model. This is a value between 0 and 1 and expresses the relationship between the variance explained by the model and the total variance. If the result is close to 1, it means that the predictors (input variables) are a good interpreter of the dependent variable value in the sample; if it is close to 0, they don’t.
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Scorza, F., Pilogallo, A., Las Casas, G. (2019). Investigating Tourism Attractiveness in Inland Areas: Ecosystem Services, Open Data and Smart Specializations. In: Calabrò, F., Della Spina, L., Bevilacqua, C. (eds) New Metropolitan Perspectives. ISHT 2018. Smart Innovation, Systems and Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-92099-3_4
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