• Journal of Information Technology Services
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• v.17 no.4
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• pp.139-150
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• 2018

This paper investigated the human distribution in subway station areas in Seoul, using geotweets and subway ridership data. Eight stations were selected from the districts of Gangnam and Gangbuk. Geotweets located within a 600-meter radius of the central coordinates of each station were extracted, and distances between the center of station and each tweet location were calculated. Donut-shaped dimension and pie-shaped dimension were generated, using the Haversine distance formula and the Azimuth angle formula respectively. By combining the two dimensions, Dartboard-shaped space division is created. Popular places within the subway station areas identified from this research are almost the same as the current well-known popular places, and this is an important case showing that people send tweets from various places where they engage in daily activities. We expect this study can be a methodological guideline for social scientists who use spatio-temporal or GPS data for their research.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.169-180
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• 2020

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.

• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.376-387
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• 2017

This paper attempts to analyze the geographical characters of Twitter data and presents analysis potentials for social network analysis in geography. First, this paper suggests a methodology for a topic modeling-based approach in order to identify the geographical characteristics of tweets, including an analysis flow of Twitter data sets, tweet data collection and conversion, textural pre-processing and structural analysis, topic discovery, and interpretation of tweets' topics. GPS coordinates referencing tweets(geotweets) were extracted among sampled Twitter data sets because it contains the tweet place where it was created. This paper identifies a correlated relationship between some specific topics and local places in Jeju. This correlation is closely associated with some place names and local sites in Jeju Island. We assume it is the intention of tweeters to record their tweet places and to share and retweet with other tweeters in some cases. A surface density map shows the hotspots of tweets, detecting around some specific places and sites such as Jeju airport, sightseeing sites, and local places in Jeju Island. The hotspots show similar patterns of the floating population of Jeju, especially the thirty-year age group. In addition, a topic modeling algorithm is applied for the geographical topic discovery and comparison of the spatial patterns of tweets. Finally, this empirical analysis presents that Twitter data, as social network data, provide geographical significance, with topic modeling approach being useful in analyzing the textural features reflecting the geographical characteristics in large data sets of tweets.