Accessibility of emergency medical care is one of the crucial factors in evaluating national primary medical care systems. While many studies have focused on this issue, there was a fundamental limit to the measurement of accessibility of emergency rooms, because the commonly used census-based population data are difficult to provide realistic information in terms of time and space. In this study, we evaluated the geographical accessibility of emergency rooms in South Korea by using dynamic population counts from mobile phone data. Such population counts were more accurate and up-to-date because they are obtained by aggregating the number of mobile phone users in a 50-by-50 m grid of a locational field, weighted by stay time. Considering both supply and demand of emergency rooms, the 2-step floating catchment analysis was implemented. As a result, urban areas, including the capital city Seoul, showed lower accessibility to emergency rooms, whereas rural areas recorded higher accessibility. This result was contrary to the results analyzed by us based on census-based population data: higher accessibility in urban areas and lower in rural. This implies that using solely census data for accessibility analysis could lead to certain errors, and adopting mobile-based population data would represent the real-world situations for solving problems of social inequity in primary medical care.
Recently from October, 2016 until April, 2017 in Seoul, South Korea, a series of protests have occurred on almost every Saturdays, where crowds estimated from 50,000 to 2.3 million have gathered in streets around palatial Gwanghwamun Square due to a political scandal of former president Park Geun-hye. In total, nearly 16.5 million crowds have attended to these events by holding candles aloft with wishes for an immediate resignation of the former president. With respect to these consecutive events in South Korea, we present a research with a goal of finding spatiotemporally the most influential factors for the characterization of event size (i.e. the number of participants) by using Seoul taxi trajectory data. For this objective, an analysis was conducted that finds the optimal combination of variables which maximizes dissimilarity measures among event size categories by utilizing Dunn's Index (DI) as an evaluation function, for which Genetic Algorithm (GA) was used due to combinatorial computation complexity of the optimization problem. As a result, the most governing regions and time bins with respect to three taxi statuses: drop off, pick up, passing with and without passenger were found for event size categories. Based on the analysis, we could come up with a reverse engineering approach which can find a list of influential factors of taxi trajectory data for characterizing event size. The influential factors could be used for traffic control and transit service plans for city administrators. The proposed approach can be applied to any other set of given clusters having many variables that requires huge amount of combinations to pick governing factors for such characterization problems.
Higher Education is facing disruptive innovation that requires, among other things, provision of a more effective and customized education service to individual students. In the field of Learning Analytics (LA), there has been much effort, in the form of the collection and thorough analysis of a variety of student-related datasets, to optimize learning performance and environments by means of personalization. The datasets include traditional questionnaire surveys, learning management system (LMS) log data of learner activities, and, more recently in the wake of the big-data-analytics trend, unstructured datasets such as SNS activities, text data, and other transactional data. Spatial data, however, is rarely considered as a key dataset, despite its high potential for characterization of students and prediction of their performances and conditions. In this context, the authors propose a new, spatial-data-driven student-characterization research framework. This vision paper describes spatial computing in its three, descriptive, predictive, and prescriptive modeling stages as well as its three challenges: (1) technical spatial data acquisition issues; (2) legal and administrative issues; (3) expansion of the application domains in which spatial data can contribute to improved modeling quality. With respect to each challenge, the on-going efforts are briefly introduced in order to substantiate the feasibility of the proposed research framework.
Service area analysis is crucial for determining the accessibility of public facilities in smart cities. However, the acquisition of service areas using conventional approaches has been limited. First, investigating traffic flow is difficult, as this factor varies significantly over time and space. Second, obtaining service areas of mobile facilities/targets has remained a challenge owing to a lack of data and methods. To address these problems, this study proposes an efficient big-data-driven approach that utilizes large-scale taxi GPS location data collected over two years within Seoul City and distributed computation to obtain the average travel time values on fine-grained grid cells of 100 m × 100 m resolution. On-the-fly visualization methods were then established with an ability to construct isochrone maps of service areas in near-real-time. This enabled performing accurate service area analysis of mobile facilities/targets dynamically. The proposed solution can be effectively used in various applications, such as optimizing the ride-sharing services or the routes of autonomous electric vehicles in future smart cities, as demonstrated in this study.
Analyzing students' characteristic can provide much information for campus planning, education design and student management. This study built students' sequential trajectories based on student smart card transactions and calculate similarity scores for finding relationship between students' trajectories and academic performance. The data used in this study are student smart card transaction data and attendance information of Yonsei university Songdo campus students. Based on this, the trajectory of each student is created into daily context sequence and connected in semester unit. In order to calculate the similarity of one semester trajectory between two students, Needleman-Wunsch Algorithm, which is mainly used for comparison of the DNA nucleotide sequences of two different species, was applied. The similarity score of trajectory sequences for student pair were calculated for 685 students in spring semester. For finding relation with academic performance, authors divided students into two groups; one group with high similarity score for both students in the pair and the other with pair of students with low similarity score. 2-sample T-test was conducted afterward in to determine whether the GPA of these groups were different form the overall distribution of student GPA. As a result, the mean value of GPA of the students with low similarity scores were statistically significantly lower than the overall mean value of GPA. This means that the trajectory sequence of students with lower GPA is less similar than the other students. The results of this study indicate that trajectory information based on spatial data is related to characteristics such as student academic achievement, and it is possible to analyze characteristics of students through spatial trajectory sequence information.
Regional air quality over East Asia, including South Korea, has been a center of public attention recently because of a few episodes in which very high particulate matter (PM) concentrations have been observed. Predicting PM variation with lead time of a few hours up to days is one of the key areas that the governments are working on because it can benefit from early warning system to short-term mitigation effort. In this study, the influence of synoptic weather conditions on regional air quality was investigated with the occurrence frequencies of PM episodes as a function of various synoptic weather patterns during winter and spring. (1) During winter, dry moderate (DM) types occur frequently alongside high PM cases (24-h mean PM10 concentration > ). The composite weather map showed a weak northwesterly wind field as a potential cause. On the contrary, it is interesting to note that dry polar (DP) types can be associated with low PM cases (24-h mean PM10 concentration < ) as well as high PM depending on near-surface wind speed. (2) Furthermore, during spring, DM and dry tropical (DT) types were found to be highly correlated with high (much higher) PM concentrations, likely because of the enhanced static stability in the lower troposphere. It should be noted that PM concentration depends on the lower atmospheric stability. The close relationship between synoptic weather patterns and PM concentration suggests that synoptic weather can play an important role in regional air quality.
The purpose of this paper is to classify two study sites into the biotope types and investigate the landscape ecological characteristics of them. This will be available for the rural planning in the aspect of environmental preservation. The summaries of the result are as follows. 1) In the result of the area assessment in biotope groups, a dry field (32%) and a paddy field (28%) are more than 50%, but settlement space and water space are less than 10%. The result shows the land use condition of rural areas. 2) In the investigation result of elongation, running water spaces are higher than other biotope groups relatively, it is because they long shaped and 1-3m narrow. 3) In case of Fractal index analysis, residential spaces and cultivated lands are investigated to be lower in numerical value, it is because they have the definite borders and get simple in the border of landscape by human intervention. 4) In case of dispersion degree, the dry field has the highest value because they are located close by forests spread widely around study sites. It means that the land which is used by artificial purpose get more value rather than natural lands 5) In the connectivity analysis, a paddy field and a residential space appear the highest. It is because residence spaces spread intensively through roads and a paddy field, through streams. 6) In rural landscape, the diversity of landscape is investigated to be simple. A paddy field and a dry field contain small sized patches that have been divided by human intervention. Besides, there appear much different vegetation around waterways and farm-roads.
Spatial big data (SBD) has been utilized in many fields and we propose SBD analytics to apply to education with semantic trajectory data of undergraduate students in Songdo International Campus at Yonsei University. Higher education is under a pressure of disruptive innovation, so that colleges and universities strive to provide not only better education but also customized service to every single student, for a matter of survival in upcoming drastic wave. The entire research plan is to present a smart campus with SBD analytics for education, safety, health, and campus management, and this research is composed of four specific items: (1) to produce 3D mapping for project site; (2) to build semantic trajectory based on class attendance records, dorm gate entry records, etc.; (3) to collect pedagogical and other parameters of students; (4) to find relationship among trajectory patterns and pedagogical characteristics. Successful completion of the research would set a milestone to use semantic trajectory to predict student performance and characteristics, even further to go to proactive student care system and student activity guiding system. It can eventually present better customized education services to participating students.
Smart city has been a popular research agenda for the past years and have been trying to provide various new services to aid and improve life quality of the public. In this study, the authors utilize floating population analysis to provide 'floating population map', which can better reflect real movement of publics living in Songdo Incheon area. By implementing floating population analysis which contains more information than traditional census population such as hourly based population and weekly based population, the authors used Getis Ord Gi* algorithm and STSS (Space Time Scan Statistics) algorithm to conduct case studies and provided with key scenario which can be implemented into developing smart cities around the world. By using floating population older than 60 years old, new sights for elderly care facilities were derived, also by using floating population data of night time movement, areas which require more security service in the night time were derived. These new insights derived from floating population data could be used as key information for emerging smart cities.
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