DOI QR코드

DOI QR Code

A Study on the Movement Characteristics of Geotweet Users: A Comparative Study on Domestic and International Movements

지오트윗 사용자의 이동 특성 분석에 관한 연구: 국내 이동과 해외 이동 비교 연구

  • Baik, Eui-Young (Spatio-temporal Data Analysis Lab, Kwangwoon University) ;
  • Cho, Jae-Hee (Information Convergence College, Kwangwoon University)
  • 백의영 (광운대학교 시공간데이터분석연구실) ;
  • 조재희 (광운대학교 정보융합학부)
  • Received : 2020.03.16
  • Accepted : 2020.07.20
  • Published : 2020.07.28

Abstract

The purpose of this study was to find the characteristics of the foreign and domestic travels and to seek out the significance of the study, by grouping the geotweets users who moved abroad, according to the average and the standard deviation of moving distances. Geotweets which caused foreign and domestic travels occurred divided, after building a data mart and the moving distances of users were measured by using the Haversine formula. It has moved more often among groups of foreign travelers in countries that use the same language and have similar lifestyles. There has been a lot of movement in developed countries with well-established infrastructure in a group of domestic travelers. This study tried to draw common features, by calculating the travel distances by each user and grouped users according to the characteristics of user's moving distances. There are significant differences in national economic power, age, jobs, etc. among users from a total of 21 countries analyzed by this study, so a more precise analysis would be able to be conducted, only if the whole conditions are considered. A future study should additionally consider real factors.

본 연구는 국가 간 이동이 발생한 지오트윗 사용자를 이동거리평균과 이동거리표준편차에 따라 그룹화하여, 국가 간 이동과 자국 내 이동에서 나타나는 특징을 발견하고 연구의 의의를 찾고자 하였다. 데이터마트를 구축 후 국가 간 이동과 자국 내 이동이 발생한 지오트윗을 분리하였고, 해버사인공식을 이용해 사용자의 이동거리를 측정하였다. 국가 간 이동 집단에서는 동일한 언어를 사용하며 생활방식이 비슷한 국가 사이에서 많이 이동하였고, 자국 내 이동에서는 인프라가 잘 구축된 선진국 위주의 국가에서 많은 이동이 발생하였다. 본 연구는 사용자별 이동거리를 계산하여 공통된 특징을 도출하고자 하였으며, 사용자의 이동거리 특성에 따라 그룹화하였다. 본 연구에서 분석한 21개국은 국가별 경제력이나 나이, 직업 등에서 차이가 커 많은 제반 사항이 고려되어야 정밀한 분석이 가능할 것이다. 향후에는 현실적인 사항을 추가한 연구가 진행되어야 할 것이다.

Keywords

References

  1. S. D. Kim, T. Y. Seong & M. H. Lee. (2015). Impacts of Inauguration of Sejong Metropolitan Autonomous City on Population Migration Network in Neighboring Areas : Focused on Population Migration in Chungcheong Region. Journal of the Korean Regional Development Association, 27(5), 283-302.
  2. D. S. Kim, J. H. Jang & D. H. Lee. (2009). Analysis of Population Movement by Region, Journal of the Korea Development Economics, 15(1), 133-152.
  3. J. Kulshrestha, F. Kooti, A. Nikravesh & P. K. Gummadi. (2012). Geographic Dissection of the Twitter Network. ICWSM 2012, 202-209.
  4. M. Lenormand, B. Goncalves, A. Tugores & J. J. Ramasco. (2015). Human diffusion and city influence. Journal of The Royal Society Interface, 12(109), 20150473. DOI : 10.1098/rsif.2015.0473
  5. I. Y. Hong. (2015). Spatial Distribution of Korean Geotweets. Journal of the Korean Cartographic Association, 15(2), 93-101. https://doi.org/10.16879/jkca.2015.15.2.093
  6. Y. K. Cha. (2018). Spatial Characteristics of High-density Location-based Social Network Service Data: The Case of Tweet Data in Seoul. The Geographical Journal of Korea, 52(2), 257-267.
  7. Blanford, J. I., Huang, Z., Savelyev, A. & MacEachren, A. M. (2015). Geo-located tweets. Enhancing mobility maps and capturing cross-border movement. PloS one, 10(6). DOI : 10.1371/journal.pone.0129202
  8. Zagheni, E., Garimella, V. R. K., Weber, I. & State, B. (2014). Inferring international and internal migration patterns from twitter data. In Proceedings of the 23rd International Conference on World Wide Web, 439-444.
  9. Khan, S. F., Bergmann, N., Jurdak, R., Kusy, B. & Cameron, M. (2017). Mobility in cities: Comparative analysis of mobility models using Geo-tagged tweets in Australia. In 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 816-822. DOI : 10.1109/ICBDA.2017.8078751.
  10. Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P. & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. https://doi.org/10.1080/15230406.2014.890072
  11. Huang, Y., Li, Y. & Shan, J. (2018). Spatial-temporal event detection from geo-tagged tweets. ISPRS International Journal of Geo-Information, 7(4), 150. DOI : 10.3390/ijgi7040150.
  12. Cvetojevic, S. & Hochmair, H. H. (2018). Analyzing the spread of tweets in response to Paris attacks. Computers, Environment and Urban Systems, 71, 14-26. DOI : 10.1016/j.compenvurbsys.2018.03.010.
  13. J. H. Cho & I. J. Seo. (2016). Comparing the Spatial Mobility of Residents and Tourists by using Geotagged Tweets. Journal of Information Technology Services, 15(3), 211-221. DOI : 10.9716/KITS.2016.15.3.211
  14. Lenormand, M., Goncalves, B., Tugores, A. & Ramasco, J. J. (2015). Human diffusion and city influence. Journal of The Royal Society Interface, 12(109), 473. DOI : 10.1098/rsif.2015.0473.
  15. Soliman, A., Yin, J., Soltani, K., Padmanabhan, A. & Wang, S. (2015). Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter users, In Proceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics. ACM, 55-58. DOI : 10.1145/2835022.2835032.
  16. Leetaru, K., Wang, S., Cao, G., Padmanabhan, A. & Shook, E. (2013). Mapping the global Twitter heartbeat: The geography of Twitter. First Monday, 18(5).
  17. C. Y. Ku. (2018). Spatial Characteristics of High-density Location-based Social Network Service Data: The Case of Tweet Data in Seoul. The Geographical Journal of Korea, 52(2), 257-267.
  18. Li, L., Goodchild, M. F. & Xu, B. (2013). Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and geographic information science, 40(2), 61-77. DOI : 10.1080/15230406.2013.777139.
  19. BTS. (Accessed 13 June 2019) Air Passenger Travel Arrivals in the United States from Selected Foreign Countries. https://www.bts.gov/content/air-passenger-travel-arrivals-united-states-selected-foreign-countries-thousands-passengers
  20. J. H. Oh. (2010). A Comparative Study on the Aerospace Body Inspection System in the United States, Europe and Korea. Conference of Aerospace Medical, 45-46.
  21. J. H. Cho & I. J. Seo. (2017). Investigation of Twitter Users' Activity Radius and Home Region in the City: The Case of Las Vegas. Journal of Korean Institute of Communications and Information Sciences, 42(2), 505-513. DOI : 10.7840/kics.2017.42.2.505
  22. M. G. Kim. (2016). A Study on Twitter User's Residential Location Inference for Twitter Mining, Ph.D. Thesis. University of Seoul.