Detection of Points of Interest within the public space

The analysis aims to identify the most appreciated and visited places and locals by the Instagram users within the study area. The identification of POIs in the area is carried out by integrating the previously identified clusters and then performing a complementary SMGI datasets extraction. As a matter of fact, the clusters emerging from the analysis conducted by means of the DB-SCAN algorithm enable to detect the areas attracting the major interest of users, but in order to gain further insights about the reasons behind the users preferences, it is necessary to perform further analyses at the local scale. Therefore, the clusters are enlarged from their centroid through the ArcGIS ‘buffer tool’ selecting a 25 meters radius and then investigated by coupling information obtained through SMGI datasets extracted from the Instagram Places and the Foursquare social media platforms, which may be considered as LBSNs that provide georeferenced information about POIs, as well as information about the number of user presences. A comparison between the two extracted complementary SMGI datasets is performed in order to assess and confirm the POI existence, while the attractiveness of each confirmed POI is evaluated though the metadata provided by Foursquare, relying upon the “number of check-in” and the “number of users that visited the place”, so

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 143 detecting the most attractive place within each cluster. Indeed, a specific POI is assigned to each cluster in terms of attractiveness, if it is embedded within the buffered cluster area, or by means of both attractiveness and spatial proximity, if a cluster does not contain any POI.

Moreover, the analysis concerns the investigation of places typology in order to provide the characterization of the study area relying on the most visited POIs. Afterwards, the POIs are examined in more detail at the local scale, identifying their names and evaluating how many clusters belong to a specific POI. As a matter of fact, several overlapping clusters may belong to the same POI.

The complementary SMGI extraction from Instagram Places and Foursquare is performed by means of SPATEXT tools, which allow the collection of POIs by specifying a spatial query. The extractions result in two dataset consisting of 771 POIs for Instagram and 177 POIs for Foursquare. Despite the notable difference in data volume, a comparison between the two datasets expose that the Instagram Places SMGI dataset may contain the same POI for multiple times due to the opportunity of users to label the places where they took photos. This capability leads toward the creation of the same POIs with different names. On the other hand, the dataset extracted from Foursquare is automatically and periodically checked and assessed by the social media platform itself, which deletes duplicates and assess the quality of contained POIs. In addition, the Foursquare dataset provides useful information regarding the typology of POIs and the number of users who visited and made a check-in in the considered locals, allowing the identification of most attractive POIs and enabling the study area characterization. The results of the complementary SMGI extraction are provided in Figure 62, exposing the spatial distribution of Instagram Places and Foursquare SMGI datasets.

Figure 62. POIs obtained from the Instagram Places SMGI dataset and the Foursquare SMGI dataset.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 144 The spatial analysis results are shown in Figure 63, highlighting the complementary SMGI datasets, the buffered clusters and the SMGI dataset of users’ contribution who led to the clusters definition.

Figure 63. POIs identification: clusters, Instagram Places and Foursquare POIs and SMGI dataset.

The analysis of the most visited POIs for typology exploits the predefined categories provided by the Foursquare social network. Indeed, this LBSN enables users to georeference their position every time they visit (check-in) a specific place or locations and then automatically assigns each locations to a general and a specific category. Therefore, the different typologies of POIs within the study area, the most visited typologies of POIs according to users preferences, as obtained from clustering, and finally their identification at the local scale, are provided in Table 36, 37, and 38, respectively.

POIs Typology POIs number POIs Typology POIs number

Beach 28 Café 15

Italian Restaurant 9 Restaurant 8

Bed & Breakfast 7 Bar 6

Other Nightlife 6 Nightclub 5

Surf Spot 5 Hotel 4

Cocktail Bar 3 Fast Food Restaurant 3

Food 3 Hospital 3

Pizza Place 3 Playground 3

Sandwich Place 3 Snack Place 3

Table 36. Main POIs categories within the study area as obtained from the Foursquare SMGI dataset.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 145

POIs Typology POIs number POIs Typology POIs number

Beach 38 Café 35

Italian Restaurant 16 Hospital 11

Restaurant 8 Bar 7

Table 37. Most visited POIs typologies according to users’ preferences within the study area.

POIs Name POIs Typology POIs Name qnt. POIs Typology

Calamosca 6 Beach Il Lido 6 Beach

La Sella del Diavolo 6 Cafè Ospedale Marino 6 Hospital

Centro Donna Binaghi 5 Hospital La Pirata 5 Italian restaurant

Le Terrazze Calamosca 5 Cocktail Bar Bobocono Beach 4 Ice Cream Shop

Calafighera 4 Beach Capolinea 4 Beach

Emerson Cafè 4 Cafè Kaìros 4 Cafè

La Lanterna Rossa 4 Cafè La Marinella 4 Italian restaurant

La Rotondina 4 Cafeteria Sa Sesta @ Poetto 4 Beach

Spinnaker 4 Food Court Twist 4 Cafè

Il fico d’india 3 Cafè La Paillote 3 Cocktail Bar

Table 38. Preferred POIs by users visits within the study area.

The obtained results demonstrate the potentialities of SMGI to elicit information related to the geography of places, fostering the POIs identification within the study area, enabling the characterization of the public place. As a matter of fact, the results show that the area is mainly visited for the presence of both the beach and a number of leisure places, namely cafè, restaurant and bar. However, the results stress also the presence of the hospital ‘Ospedale Marino’, which is located within the study area.