A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics†
AbstractLiving 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Spyrou, E.; Korakakis, M.; Charalampidis, V.; Psallas, A.; Mylonas, P. A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics. Algorithms 2017, 10, 35.
Spyrou E, Korakakis M, Charalampidis V, Psallas A, Mylonas P. A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics. Algorithms. 2017; 10(1):35.Chicago/Turabian Style
Spyrou, Evaggelos; Korakakis, Michalis; Charalampidis, Vasileios; Psallas, Apostolos; Mylonas, Phivos. 2017. "A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics." Algorithms 10, no. 1: 35.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.