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Citation |
Senes G, Rovelli R, Bertoni D, Arata L, Fumagalli N, Toccolini A. J. Transp. Geogr. 2017; 65: 175-187. |
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Copyright |
(Copyright © 2017, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
The aim of this research is to assess the relationships between the number of users detected along some Italian greenways and the characteristics of the territory crossed in order to define a model capable of estimating the number of potential users of a greenway before it is realized. We have gathered monthly users data of 7 Italian greenways. For each greenway, we also analyzed the variables influencing its use (characteristics of the greenways, population, landscape, climate and season). Using the most significant variables, we have developed three different linear regression models (I: all 13 counters; II: sub sample of 10 mountain counters; III: sub sample of 9 homogeneous mountain counters) having as the dependent variable the monthly users number, in order to assess the combination more able to describe the studied phenomenon. The three models have a significant χ2, meaning that the regressors are jointly significantly different from zero, thus the set of our explanatory variables plays a role in estimating greenways monthly potential users. The use of a greenway is influenced mainly by the population level of education, the tourists number, the richness of historical and architectural resources, the degree of accessibility of the trail and by the month of the year. The population aged under 15 and over 64, the degree of urbanization influence negatively the use. Although additional researches are needed, the model defined may have potential application in the forecasting studies to estimate existing or proposed greenways use. However, because different regions have different climates, socio-demographic characteristics and landscapes, further research are needed to extend, test and validate the model. Language: en |
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Keywords |
Regression analysis; Greenways; Demographic and landscape variables; OLS ordinary least squares; Potential users estimation |