# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
###### Abstract  
Living in the \"era of social networking\", we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this \"socially-generated knowledge\" (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely _location-based information_ included within geo-tagged photos that we collect from Flickr, along with plain _simple textual information_ from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into \"tile\"-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results. a 201710355 10.3390/a10010035 13 March 20171035 10.  
or in case of tourists to familiarize themselves with some of the major (or minor) landmarks of an area (e.g., monuments, archaeological sites, etc.). The users' whereabouts are typically tied with their photos and/or videos, usually for sharing this content within their social circles or even just for personal archiving [3]. This multimedia content is often loosely annotated by a set of descriptive (at least to satisfy its author's intentions) keywords, which are typically referred to as \"tags\". A special tag category, extremely popular during the last few years, consists of the \"geo-tags\", i.e., the geographical coordinates used to \"tie\" a given multimedia content item to a specific geographical location. Due to the aforementioned observations, research interest has shifted to unprecedented domains, such as those related to the acquisition of information and analysis of the online \"footsteps\" that correspond to the actual presence of users in certain places, which is often referred to as \"digital footprinting\" [4,5]. The latter is often processed, in order to extract semi-automatic knowledge about the users' whereabouts, interests or even to recommend them additional, semantically related information to cover their needs for tourism, entertainment, etc. Within this environment, users are acting as \"social sensors\", although this is not their main intention. The uploaded user-generated digital content accompanied by useful metadata information, like tags and/or geo-tags, is considered to be the ideal source of information among others for the discovery of meaningful, popular trends with respect to users' behavior. More specifically, location-based information mined from such geo-tagged images offers a great opportunity to analyze users' preferences in their daily lives and complement the knowledge of their social activities through the utilization of associated tags. One of the research areas that has been benefiting by this huge sensor network formed by hundreds of millions of people is the one of \"vernacular\" geography [6]. It is common sense that geography is a science that studies the earth and human interaction with it. ([[http://www.rgs.org/geography/what+is+geography.html](http://www.rgs.org/geography/what+is+geography.html)]([http://www.rgs.org/geography/what+is+geography.html](http://www.rgs.org/geography/what+is+geography.html))) The main aspect of geography is to define a set of boundaries that divide the earth into regions. The latter may be divided into three major categories: formal, functional and vernacular [7].