• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.505-513
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• 2017

In this study, we collected 200,578,703 geo-tweets and removed the twitter bots. Using the concept of activity radius, Twitter users are classified. Users are also divided first into domestic and overseas, and again domestic ones are divided into locals and non-locals. Statistical characteristics of activity strength and active area of Twitter users are described according to activity radius and home region, and the geographical distribution is presented visually. Through a case study of Las Vegas, we have identified the difference in activity strength and active area by the user's home residence. We expect to derive theories about human mobility by analyzing various cities with the method proposed in this study.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.23-29
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• 2015

In this study, the spatial relevance between the regional housing price data and the spatial distribution of the location-based social media data is explored. The spatial analysis with rasterization was applied to this study, because the both data have a different form to analyze. The geo-tagged Twitter data had been collected for a month and the regional housing price index about sales and lease were used. The spatial range of both data includes Seoul and the some parts of the metropolitan area. 2,000m grid was constructed to consider the different spatial measure between two data, and they were combined into the constructed grids. The Hotspot Analysis was operated using the combined dataset to see the comparison of spatial distribution, and the bivariate spatial correlation coefficients between two data were measured for the quantitative analysis. The result of this study shows that Seocho-gu area is detected as a common hotspot of tweet and housing sales price index data. though the spatial relevance is not detected between tweet and housing lease price index data.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.211-221
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• 2016

The human spatial mobility information is in high demand in various businesses; however, there are only few studies on human mobility because spatio-temporal data is insufficient and difficult to collect. Now with the spread of smartphones and the advent of social networking services, the spatio-temporal data began to occur on a large scale, and the data is available to the public. In this work, we compared the movement behavior of residents and tourists by using geo-tagged tweets which contain location information. We chose Seoul to be the target area for analysis. Various creative concepts and analytical methods are used: grid map concept, cells visited concept, reverse geocoding concept, average activity index, spatial mobility index, and determination of residents and visitors based on the number of days in residence. Conducting a series of analysis, we found significant differences of the movement behavior between local residents and tourists. We also discovered differences in visiting activity according to residential countries and used applications. We expect that findings of this research can provide useful information on tourist development and urban development.