# Indicators as Mediators for Environmental Decision Making: The Case Study of Alessandria
## 2 Materials and Methods
### Methods
#### 2.2.2 Mapping
In addition, referring to some experiments in the urban analysis [51; 52] that work on Location-Based Social Media (LBSM) data to investigate the perception and use of public spaces of both local citizens and temporary communities, Instagram, Foursquare, and Flickr were included as sources. The Foursquare API was helpful for the research in finding existing Foursquare places in the municipal territory, which includes most of the commercial activities and public services. Thus, this layer enriches the socio-economic information, providing a quantitative parameter (number of check-ins) to measure popularity, in relation to the community of Foursquare users [53]. Flickr and Instagram are social media which are mainly used for sharing visual content (images). Nevertheless, they serve different targets (and use different geolocation criteria): the first has a smaller community of users but it offers the possibility of sharing photos in different formats, with a high percentage of professional photographers or tourists using their trip albums. In this case, if the shooting device has the geolocation enabled, the image maintains, in its metadata, the coordinates. Thus, after querying the API, the points in the city where a Flickr photo was taken and shared were downloaded, along with other metadata (such as the timestamp, hashtags, the description, etc.). Instagram connects a broader and younger community: it counts around a billion active users, among which 70% are under 35 years old. Thus, it permits more precise and updated information on trends concerning popular places in the city or even temporary Points of Interest (POI). In addition, Instagram geolocation works on the geotagging system: the images do not have coordinates, but the user can tag the shared photo in an Instagram place. Thus, from an urban analysis perspective, the distribution of Instagram places in the city, the number of pictures per place, and the visual characteristics of the posts become significant information. Even if Instagram has stricter API regulations, the use of scraping techniques is widely accepted in the literature. In this sense, a Python algorithm has collected a dataset with the existing Instagram places in Alessandria, with the quantitative attribute of the number of geotagged photos [54] (Figure 4). Figure 4: (**a**) The map shows the distribution of social network activity in the historical center of Alessandria: in magenta, the Instagram places, sized by the number of photos, in yellow, the different concentrations of Flickr photos, in green, the different concentrations of Foursquare places; (**b**) the map shows the distribution of digital activity related to touristic attraction; in particular, in orange, the restaurants, sized by the number of Tripadvisor reviews, in red the attractions, sized by the number of reviews, in green, the hotels, sized by the number of reviews, whereas the lines link the restaurants/hotels to the closest attraction [Sources: Authors, OpenStreetMap, Municipality of Alessandria]. This corpus of knowledge has been organized in an SQLite relational geodatabase (DB): it is a format selected for its portability, which permits one to have an SQL database in one file, its interoperability, its ability to be manageable with both opensource and proprietary GIS software, and its ability to perform spatial analysis and to share information among the different stakeholders. The DB is organized in 23 tables (Table 2), where the data from the sources mentioned above have been stored, thus harmonizing their cartographic projection (WGS84-UTM33). Table 2 contains data related to the whole municipal territory of Alessandria. Nevertheless, to link this information to our object of study (the network of 13 squares in the historical center), further geoprocessing operations have been performed.