## 3 A Geographic Information-Based Methodological Framework for Tourism Analysis  
The methodological approach builds on a preliminary heuristic analysis concerning social networks' contents related to the Sardinian region, in order to identify the most popular destinations, the relationships between the quality of tourist services and their locations, and the spatial distribution of tourists' preferences at the regional and local levels. We use geographic information data, in particular both AuGI and geographic information generated by social media's users. Combined AuGI and geographic information generated by social media's users are used to describe location. The analysis framework is implemented, at the regional and local levels, by considering two dimensions (the quality of tourist services and their locations), using both AuGI and geographic information generated by social media's users. First of all, analysis at the regional scale is implemented to describe spatial patterns of tourists' preferences and to identify locations of interest. Afterwards, at the local level, that is, with reference to a particular cluster or spot of interest, further analyses were developed aiming at understanding the possible reasons which caused the detected patterns and singularities, that may help in explaining success- or failure-related factors concerning destinations' and services' features. We use a methodology based on spatial statistical and spatio-temporal textual analysis; that is, the analysis of texts which takes account of spatial and time-related constraints (indicated as \"STTx\" from now on) [19]. Operationally, the study is implemented through the following steps:  
1. construction of a geocoded database whose records are retrieved from Booking.com and TripAdvisor.com;
2. implementation of a spatial analysis of opinions and attitudes of the local communities based on spatial statistics and STTx, and identification of clusters of TLSs which show concentration of preferred TLSs (hot spots as opposed to cold spots);
3. analysis of preferences expressed at the local level; data are integrated with AuGI from the Sardinian regional Spatial data infrastructure (SDI) and other official open data sources, in order to find explanatory hints on the tourists' preferences dynamics and to get insights on the relationships among the preferences, the local geography-related features and the quality of the tourism-related services at selected destinations;
4. estimate of a spatial regression to investigate spatial patterns within a particular tourist destination. By doing so, we describe the spatial distribution of the sign, magnitude, and significance of the influence of each explanatory (location-related) variable on the dependent variable, that is the level of satisfaction with the tourist destination. The first step consists of the construction of a database based on data extracted from TripAdvisor.com and Booking.com, which we collected for the period May 2012-May 2013. The focus of the two portals is to filter contents based on rankings, which are drawn from tourists' ratings. Rankings can be classified into several categories, such as value/price, rooms, location, cleanliness, and sleep quality. Since information concerning services' locations is available, it can be considered geographic information generated by social media's users [19]. The main issue is to manage this enormous amount of information. Thus, the study required the adoption of a mixed methodological approach, where quantitative information, concerning the scores of tourists' evaluation criteria, and qualitative information, including tourists' textual descriptive reviews, were collected in a database. TripAdvisor.com's ratings range from 1 (\"terrible\") to 5 (\"excellent\"), Booking.com from 1 to 10. Moreover, in both platforms, a text box allows tourists to express their opinions through text.