# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 2 Related Work
### AOI Extraction  
During the last few years, many research efforts have focused towards the extraction of AOIs. Earlier approaches exploited available geolocation services and user-generated tags, while more recent ones have focused on the socially-generated geo-tagged metadata from popular social networking websites. Amongst the most characteristic approaches of the first category is the one of [PERSON] et al. [16], who reviewed efforts towards the extraction of knowledge on vernacular places before the rising of geo-tagging. Current methods included mining the web, using geo-references from business directories, from social websites or from user created information sources. Then, based on those, they proposed a heuristic approach to find the extent of a currently developing area, relying mainly on knowledge deriving from sources from all the aforementioned categories. [PERSON] and [PERSON] [17] proposed an automated statistical method for the generation of footprints, i.e., spatial representations of places. Due to the limited amount of geo-tagged photos available, they also relied on user-generated, location-related metadata. They evaluated their approach on both precise (i.e., with well-defined boundaries) and imprecise regions with their results being satisfactory, according to an informal assessment produced by users. [PERSON] et al. [18] exploited check-ins and geo-tagged photos in an effort to unveil people's preferences in their daily lives, focusing on both travellers and local residents. They first extracted AOIs and then ranked them by using both transitions between places and relations of users to places. They demonstrated that information extracted from check-ins and geo-tagged photos are mutually complementary. [PERSON] et al. [19] aimed to define boundaries for vernacular areas. To this goal, given the name of such an area as a query to three services, all geo-tagged resources that have been tagged with this name are returned. Based on their popularity, they create point clouds which are then clustered. They applied their method in a city area, a large-scale landmark and a set of routes towards a specific place. [PERSON] et al. [20] worked on the problem of learning thematic maps, i.e., maps that show the spatial distribution of a given feature. They worked using zip code data and applied well-known classification methods. Empirically, they assessed that even for this case where regions are relatively small, a small number of data per cluster is required. [PERSON] and [PERSON] [21] proposed a spatial demographic approach using Self-Organizing Maps [22] for clustering. Their goal was to describe communities based on certain characteristics of their residents. They showed that these types of communities are not always continuous but may spread within the whole urban area. On the other hand, [PERSON] et al. [23] used check-in data from Foursquare, ([[http://www.foursquare.com](http://www.foursquare.com)]([http://www.foursquare.com](http://www.foursquare.com))) collected from Twitter. ([[http://www.twitter.com](http://www.twitter.com)]([http://www.twitter.com](http://www.twitter.com))) Their problem was twofold: to provide a means of similarity between two areas of the same city and also between different cities. In order to model activity patterns within a given city, they applied a clustering algorithm, on small square areas (tiles), using Foursquare categories as features. This way they were able to model areas based on the activities of people visiting them. [PERSON] and [PERSON] [24] used data derived from Flickr and performed a study whose goals were to assess the reliability of user-generated tags, to describe city centers with these tags and to extract knowledge from them, so as to describe vernacular areas, in terms of their location. [PERSON] et al. [9] aimed to extract and understand urban AOIs in terms of their spatial, temporal and thematic features. They used geo-tagged data derived from Flickr. In order to identify their boundaries, they used the chi-shape algorithm [25].