The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people’s traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people’s traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people’s traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers’ traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims’ hotspots are more sporadic than elderly drivers’ hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future.

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In this study, we visualize and analyze global positioning system (GPS) data to identify the spatiotemporal characteristics of moving and staying patterns. As a case study, we collect and process GPS data generated by students participating in inquiry-based fieldwork. Space-time path (STP) analysis is applied to visualize movement, while density-based spatial clustering of applications with noise (DBSCAN) is used to identify spatial clusters or staying places (sites where people spend time, such as homes and workplaces). We find that some clusters derived by DBSCAN are not actual clusters, and the times spent in some clusters are overestimated when we investigate the time spent in each cluster. To resolve this, 3D DBSCAN is used to find precise clusters. The results show that the 3D DBSCAN method is effective in finding clusters of spatiotemporal data. The 3D DBSCAN methodology proposed in this study can be applied effectively in movement data analysis, such as tourist travel patterns through SNS, trajectories of cars, vessels, or wildlife, and the movement of visitors in parks.

In this study we aim to analyze the urban image of Seoul that tourists feel through the photos uploaded on Flickr, which is one of Social Network Service (SNS) platforms that people can share Geo-tagged photos. We first categorize the photos uploaded on the site by tourists and then performed the image mining by utilizing Convolutional Neural Network (CNN), which is one of the artificial neural networks with deep learning capability. In this study we are able to find out that tourists are interested in old palaces, historical monuments, stores, food, etc. in which are considered to be the signatured sightseeing elements in Seoul. Those key elements are differentiated from the major sightseeing attractions within Seoul. The purpose of this study is two folds: First, we analyze the image of Seoul by applying the technology of image mining with the photos uploaded on Flickr by tourists. Second, we draw some significant sightseeing factors by region of attraction where tourists prefer to visit within Seoul.