Mining geosocial data from Flickr to explore tourism patterns: The case study of Athens
(2021) In Master Thesis in Geographical Information Science GISM01 20211Dept of Physical Geography and Ecosystem Science
- Abstract
- Kanari Domna
Mining geosocial data from Flickr to explore tourism patterns: The case study of Athens
Social media are providing a new type of geo-tagged data that by processing them, new types of knowledge can be generated and used by decision-makers in different areas including tourism. It includes e.g., identifying touristic areas that are not prioritized or advertised by touristic authorities. This study aims to investigate a methodology to detect Areas of Interest (AOIs) and temporal distributions of visitors geotagged photos in Athens, by mining and analyzing geosocial data from the Flickr social media platform, from 2009 to 2019. The methodology of this research, divided into 5 stages of procedures: the geosocial data mining,... (More) - Kanari Domna
Mining geosocial data from Flickr to explore tourism patterns: The case study of Athens
Social media are providing a new type of geo-tagged data that by processing them, new types of knowledge can be generated and used by decision-makers in different areas including tourism. It includes e.g., identifying touristic areas that are not prioritized or advertised by touristic authorities. This study aims to investigate a methodology to detect Areas of Interest (AOIs) and temporal distributions of visitors geotagged photos in Athens, by mining and analyzing geosocial data from the Flickr social media platform, from 2009 to 2019. The methodology of this research, divided into 5 stages of procedures: the geosocial data mining, the cleaning process of the data, the spatial clustering analysis, the construction of the database, and the visualization of the results through a Web-GIS application. The total amount of geosocial data harvested from the Flickr social media platform for this research was 157,314 and after the cleaning process was 77,659. To identify the most desired AOIs and the temporal distribution tendencies of the visitors, the HDBSCAN clustering algorithm was applied to the dataset. The algorithm produced 20 spatial clusters in popular areas of Athens and the results of the clustering analysis were stored in a database. To validate the results, 21 of the most famous Points of Interest (POIs) of Athens were gathered, mapped and the correlation between them and the produced AOIs was explored. Finally, the produced AOIs and the collected POIs were presented through a prototype Web-GIS platform among with temporal distribution statistics for each AOI. The results of this study showed that the HDBSCAN algorithm produced 8 new AOIs that were not suggested or advertised by tourism authorities. Also, the findings of this research demonstrate that the study area presents in general medium to high levels of seasonality with small exceptions and that the visitors are mostly from Europe, North America, and Asia. Despite some reliability issues geosocial data present, tourism agencies/authorities, urban planners, and policymakers must seriously consider exploiting such kind of data and understand the power of the tools they can create using location intelligence.
Keywords: Geography, Geographical Information Systems, GIS, Spatial Analysis, Spatial Clustering, Density-Based Clustering, HDBSCAN, Web-GIS, Tourism footprints, Social Media data, Geosocial data
Advisor: Ali Mansourian
Master degree project 30 credits in Geographical Information Sciences, 2021
Department of Physical Geography and Ecosystem Science, Lund University
Thesis nr 134 (Less) - Popular Abstract
- Kanari Domna
Mining geosocial data from Flickr to explore tourism patterns: The case study of Athens
Indirect participatory geography resulted from the utilization of a large volume of population data with spatial reference mainly from social networks. Innovatively, combining heterogeneous data sources and formulating new hypotheses a new research era has been arisen. In the already developed social network applications the participants publicize daily several different types of data, which are part of different topics based on individuals’ interests. Those data are a source of valuable information that can be used to analyze people's traces and footprints in urban areas. In recent years, the development of technology and the... (More) - Kanari Domna
Mining geosocial data from Flickr to explore tourism patterns: The case study of Athens
Indirect participatory geography resulted from the utilization of a large volume of population data with spatial reference mainly from social networks. Innovatively, combining heterogeneous data sources and formulating new hypotheses a new research era has been arisen. In the already developed social network applications the participants publicize daily several different types of data, which are part of different topics based on individuals’ interests. Those data are a source of valuable information that can be used to analyze people's traces and footprints in urban areas. In recent years, the development of technology and the availability of the data through the internet and specifically through social media in combination with spatial analysis can bring information to “smart city” strategies.
Regarding this study, it is conducted for the city of Athens, Greece, and is based on crowdsourcing and how the mining and the processing of geographical data through social media platforms can contribute to tourism and reveal spatial patterns. The goal of this study is to create a WEB-GIS platform which will provide to the community the results from the data mining and their spatial analysis. It provides information about which areas are more preferable by the public and which season the city has the most visitors. Through the construction and operation of the platform, many features such as maps, Areas of Interest (AOIs) and statistics are published electronically and a user-friendly interface has been created, where people can design their tourism-routes. Concerning the structure of methodology, it is organized in different parts. The methodology of this research, is divided into 5 stages of procedures: the geosocial data mining, the cleaning process of the data, the spatial clustering analysis, the construction of the database, and the visualization of the results through a Web-GIS application
The clustering analysis produced 20 popular AOIs and among them, 8 new AOIs were generated, which were not suggested or advertised by tourism authorities. Concerning the temporal distributions of the visitors, the study area presents in general medium to high levels of seasonality with small exceptions. Regarding the visitors nationalities distribution, it seems that the majority of visitors are mostly from Europe, North America, and Asia. Those findings are expected to help the tourism authorities, the municipal and the urban planners to develop, plan and manage more efficient the tourism strategies of the city and of course the visitors of the city either they are foreigners or locals to explore which areas are worth to visit using new technologies.
Keywords: Geography, Geographical Information Systems, GIS, Spatial Analysis, Spatial Clustering, Density-Based Clustering, HDBSCAN, Web-GIS, Tourism footprints, Social Media data, Geosocial data
Advisor: Ali Mansourian
Master degree project 30 credits in Geographical Information Sciences, 2021
Department of Physical Geography and Ecosystem Science, Lund University
Thesis nr 134 (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9061406
- author
- Kanari, Domna LU
- supervisor
- organization
- course
- GISM01 20211
- year
- 2021
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Geography, Geographical Information Systems, GIS, Spatial Analysis, Spatial Clustering, Density-Based Clustering, HDBSCAN, Web-GIS, Tourism footprints, Social Media data, Geosocial data
- publication/series
- Master Thesis in Geographical Information Science
- report number
- 134
- language
- English
- id
- 9061406
- date added to LUP
- 2021-07-09 13:52:39
- date last changed
- 2021-07-09 13:52:39
@misc{9061406,
abstract = {{Kanari Domna
Mining geosocial data from Flickr to explore tourism patterns: The case study of Athens
Social media are providing a new type of geo-tagged data that by processing them, new types of knowledge can be generated and used by decision-makers in different areas including tourism. It includes e.g., identifying touristic areas that are not prioritized or advertised by touristic authorities. This study aims to investigate a methodology to detect Areas of Interest (AOIs) and temporal distributions of visitors geotagged photos in Athens, by mining and analyzing geosocial data from the Flickr social media platform, from 2009 to 2019. The methodology of this research, divided into 5 stages of procedures: the geosocial data mining, the cleaning process of the data, the spatial clustering analysis, the construction of the database, and the visualization of the results through a Web-GIS application. The total amount of geosocial data harvested from the Flickr social media platform for this research was 157,314 and after the cleaning process was 77,659. To identify the most desired AOIs and the temporal distribution tendencies of the visitors, the HDBSCAN clustering algorithm was applied to the dataset. The algorithm produced 20 spatial clusters in popular areas of Athens and the results of the clustering analysis were stored in a database. To validate the results, 21 of the most famous Points of Interest (POIs) of Athens were gathered, mapped and the correlation between them and the produced AOIs was explored. Finally, the produced AOIs and the collected POIs were presented through a prototype Web-GIS platform among with temporal distribution statistics for each AOI. The results of this study showed that the HDBSCAN algorithm produced 8 new AOIs that were not suggested or advertised by tourism authorities. Also, the findings of this research demonstrate that the study area presents in general medium to high levels of seasonality with small exceptions and that the visitors are mostly from Europe, North America, and Asia. Despite some reliability issues geosocial data present, tourism agencies/authorities, urban planners, and policymakers must seriously consider exploiting such kind of data and understand the power of the tools they can create using location intelligence.
Keywords: Geography, Geographical Information Systems, GIS, Spatial Analysis, Spatial Clustering, Density-Based Clustering, HDBSCAN, Web-GIS, Tourism footprints, Social Media data, Geosocial data
Advisor: Ali Mansourian
Master degree project 30 credits in Geographical Information Sciences, 2021
Department of Physical Geography and Ecosystem Science, Lund University
Thesis nr 134}},
author = {{Kanari, Domna}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master Thesis in Geographical Information Science}},
title = {{Mining geosocial data from Flickr to explore tourism patterns: The case study of Athens}},
year = {{2021}},
}