# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
###### Abstract
The first are typically administrative regions, i.e., spanning from municipalities and cities to countries and continents; their boundaries are formally and strictly defined. In some cases formal regions may be defined based on any feature, e.g., deserts or mountains. Functional regions are those that are formed by patterns of interaction among different locations. More specifically functional regions are typically organized around a certain point with which they may interact at any sense, e.g., small towns around a major city are together forming a metropolitan area, or several areas forming a power network. In general, one could simply describe a functional region as a set of regions acting as a cohesive unit. On the other hand, the vernacular regions (sometimes referred to as \"perceptual\") are neither formal nor functional and are often regarded as \"naive\" geography [8]. Boundaries are not strict but vague. They rely either on the common sense, perception, feelings and attitudes of their residents (e.g., in urban areas) or of people familiar to the broader area (e.g., in rural areas). Each vernacular region exists due to emotional attachments of people (and especially its residents) to the specific place. A vernacular region is somehow similar to the notion of an \"area-of-interest\" (AOI). According to [PERSON] et al. [9], an AOI is an \"area within an urban environment which attracts people's attention\". When the application domain is tourism, an AOI is generally considered to contain landmarks, museums, places of worship, art galleries, i.e., places that typically attract a tourist during her/his visit at an unknown city. These places are typically referred to as \"places-of-interest\" (POIs). By contrast, the intuition of a local resident to the notion of an AOI may significantly differ. In that case AOIs may include retail stores, restaurants, bars, parks, i.e., POIs where she/he could spend her/his free time. Similarly to vernacular regions, AOIs in general are vague areas, with uncertain boundaries. We may also argue that a given area is considered to consist an AOI, based mainly on subjective criteria, that stem from people's interests. Thus, these criteria may significantly differ due to age, nationality, culture etc. For these reasons it is quite difficult to create an \"accurate\" list of AOIs for a given city, while it may probably be impossible to define generally accepted boundaries, since they reflect a \"sense of place\", rather than confining to strict administrative boundaries. Urban AOIs are able to reveal useful information that can be exploited by tasks such as city planning, transportation analysis and location-based social recommendations [9] and may also provide input to tasks such as further AOI analysis and understanding. To the aforementioned goals, we first divide the given urban area into small square regions, which shall be referred to as \"tiles\". From each tile we collect all geo-tagged photos from the popular Flickr ([[http://www.flickr.com](http://www.flickr.com)]([http://www.flickr.com](http://www.flickr.com))) social network. We use the tags that have been manually added by the users within the process of sharing their photos to create a histogram description for each tile. We then propose a novel algorithm so as to merge similar (in terms of their tag-based description) tiles and extract a set of AOIs. From each, we process all contained tags and upon a \"term frequency--inverse document frequency\" (TF-IDF) approach, we extract what we consider to be the most descriptive ones, we try to propose the name of the AOI and moreover we calculate a set of heuristic-based metrics, so as to assess the quality of the geo-clustering results. We should emphasize herein that the presented work introduces the use of the underlying semantics in the process of geo-clustering, an attribute typically ignored by current state-of-the-art approaches. Thus, the latter are prone to provide insufficient interpretations.