geografie luoghi spazi città

Fig.1 – Tourist clusters in Italy, 2011.
Source: Capone (2016, p. 68).
Since the clustering of tourism activities has already been linked to the growth of tourist destinations
in the literature, we investigated the determinants of clustering of tourist firms and then applied
different weights to these determinants.
3. ANALYSING THE REASONS FOR THE CLUSTERING OF TOURIST FIRMS IN TOURIST DESTINATIONS.
3.1 Variables. — To investigate the forces that most influence the process of clustering and the
major factors behind competitive advantage for locations, Porter (1998) proposes four conditions: factor
conditions, rivalry and structure of the local industry, demand conditions, and related and supporting
industry. This model has been applied to the tourism industry several times and to the competitiveness
of destinations (Nordin, 2003; Jackson, Murphy, 2006).
The factor conditions identify the local resources of the cluster, such as raw materials, human
capital and infrastructure. In this study, we analyse the resources of tourist destinations, such as the
presence of artistic, cultural and environmental heritage through a number of variables.
To make clear any positive connections, the rivalry and structure of the local industry emphasises
structure and competition in the destination and is analysed by the level of concentration of firms
(LQ), the industrial structure of the various sectors, and the level of local related variety. Finally, to investigate
any connections outside the main industry, related and supporting industry are analysed indirectly
through the construction of an enlarged tourism filière and through the indices of variety and
unrelated variety.
All explanatory variables are expressed and calculated for 2001 (ISTAT, 2001), in order to analyse
the causal relationship between those variables and the concentration of tourism activities in the area
until 2011. The dependent variable is the LQ of tourism activities for the year 2011, and was drawn up
using data from the 2011 Census (ISTAT, 2011).
In order to identify the role of commons in the clustering of tourist firms we added dummy variables
for artistic, cultural and environmental heritage were also included as these seem to be crucial for
tourist destinations and the clustering of firms. These variables were built according to the integration
of multiple data sources. In the first instance, we tried to assess the presence of different levels of artis-
– 469 –