## 3 A Geographic Information-Based Methodological Framework for Tourism Analysis
Despite the lack of application programming interfaces (APIs) to access the services' information, the data extraction is performed by an _ad hoc_-implemented Javascript code that allows the Web content extraction. Then geocoding is performed on the extracted addresses. This makes it available information on the location of tourist facilities within the Sardinian region. Coordinates of the facilities were automatically detected in approximately 80% of the records, while approximately 20% of the geocoded locations were manually re-edited. A unified database of 992 records was finally defined. The records provide tourism lodging services' (TLSs) names, categories, locations, and related quantitative scores. After the preliminary descriptive analysis of the preferences dataset, the second step of the methodology is the application of spatial analysis of tourism preferences to explore spatial patterns of positive tourists' judgments at the regional level. The application of spatial analytical techniques aims at exploring the spatial patterns of tourists' perception and their relationships with different variables. Visualizing this type of information on a map and interpreting derived results are difficult and, at times, illegible. This issue of representation has been overcome by using hot spot analysis (tool of ArcGIS 10.1 version; ESRI: Redlands, CA, United States). The hot spot analysis allows us to calculate the Getis-Ord Gi* statistic for each feature in a dataset by looking at each feature in the same neighborhood [32]. The resultant \(z\)-scores and \(p\)-values give some clues of where features with either high or low values cluster spatially. In the third step, the methodology adopted for this study shifted to the local scale for further analysis, aimed at finding explanatory answers for the phenomena under observation. The shift from the regional to the local scale was also conducted relying on spatial analysis and spatial statistics techniques on an integrated geographic information generated by social media's users/AuGI database. The aim is two-fold. On the one hand, the study is performed to discover why tourists prefer some destinations rather than others at the regional scale (qualitative analysis); on the other hand, we implement a quantitative assessment related to the location of tourists' preferences and to factors that contribute to high TTPI rates. We consider the tourist destination of Cagliari as an important reference point for our analysis since it shows a highly-successful tourist performance. The geographic information-based analysis assesses the success factors related to this destination; that is, the determinants (explanatory variables) of the high TTPI rates (dependent variable). Moreover, the aim is to understand not only where people go but also what people think. This assessment is implemented by analyzing the review contents. The reasons implied by the tourists' preferences for certain destinations are detected through the STTx related to their reviews. The analysis is performed by means of the database of the tourists' comments, which collects data concerning the tourists' origin, language, and textual opinions. Textual analysis, which utilizes a tag cloud, makes it possible to detect information contained in the textual comments. Tag cloud is a visual representation of keywords (tags) contained in the textual strings of these comments. In the final step, a spatial regression is used for both modeling the preferences phenomenon and testing the reliability of the hypothesis derived from textual analysis, in order to make appropriate decisions in terms of policy. The aim of the spatial regression is to discover what factors contribute to the TPPI rate. The model is applied to a sample of 150 TLSs spatially distributed over 100 of the 1359 Cagliari's census tracts. The dependent variable is the score of the TPPI, normalized as fraction of the comments that are favorable to a location belonging to a census tract.