A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics
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 t...
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doaj-7385b39f18664f71a64fc1e6821c43d72020-11-24T21:06:14ZengMDPI AGAlgorithms1999-48932017-03-011013510.3390/a10010035a10010035A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying SemanticsEvaggelos Spyrou0Michalis Korakakis1Vasileios Charalampidis2Apostolos Psallas3Phivos Mylonas4Institute of Informatics and Telecommunications, National Center for Scientific Research—“Demokritos”, 153 41 Athens, GreeceDepartment of Informatics, Ionian University, 49 100 Corfu, GreeceDepartment of Computer Engineering, Technological Educational Institute of Central Greece, 351 00 Lamia, GreeceDepartment of Computer Engineering, Technological Educational Institute of Central Greece, 351 00 Lamia, GreeceDepartment of Informatics, Ionian University, 49 100 Corfu, GreeceLiving 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.http://www.mdpi.com/1999-4893/10/1/35areas of interestsemanticsgeo-clusteringFlickr |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Evaggelos Spyrou Michalis Korakakis Vasileios Charalampidis Apostolos Psallas Phivos Mylonas |
spellingShingle |
Evaggelos Spyrou Michalis Korakakis Vasileios Charalampidis Apostolos Psallas Phivos Mylonas A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics Algorithms areas of interest semantics geo-clustering Flickr |
author_facet |
Evaggelos Spyrou Michalis Korakakis Vasileios Charalampidis Apostolos Psallas Phivos Mylonas |
author_sort |
Evaggelos Spyrou |
title |
A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics |
title_short |
A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics |
title_full |
A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics |
title_fullStr |
A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics |
title_full_unstemmed |
A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics |
title_sort |
geo-clustering approach for the detection of areas-of-interest and their underlying semantics |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2017-03-01 |
description |
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. |
topic |
areas of interest semantics geo-clustering Flickr |
url |
http://www.mdpi.com/1999-4893/10/1/35 |
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