• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.36-44
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• 2018

VR(Virtual Reality) and AR(Augmented Reality) devices are becoming more common, and the need for proper contents presentation techniques in such environments has been growing ever since the popularization of the devices. One of the contents is the spatio-temporal data, which has become more prominent since it could be both generated and consumed by a large number of ordinary users. In this work, the researcher analyzed the characteristics of spatio-temporal data as a source for visualization in VR and AR environment, and categorized prior visualization methods for such data, which were devised for traditional monitors. The researcher also reviewed the hardware specification of state-of-the-art devices, and examined the possibility of adopting the previous visualization approaches. This work is expected to contribute in designing spatio-temporal visualization for VR and AR environment by utilizing their unique characteristics.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1150-1160
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• 2004

In this paper, we propose a new 3D video coding method for heterogeneous display systems and network infrastructure over enhanced Access Grid (e-AG) using spatio-temporal scalability defined in MPEG-2. The proposed encoder produces several bit-streams for providing temporally and spatially scalable 3D video service. The generated bit-streams can be nelivered with proper spatio-temporal resolution according to network bandwidths and processing speeds, visualization capabilities of client systems. The functionality of proposed spatio-temporal scalability can be exploited for construction of highly scalable 3D video service in heterogeneous distributed environments.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Journal of Information Technology Services
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• v.9 no.1
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• pp.127-139
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• 2010

Due to the development of information technology and business related to geographical location of customer, the need for the storage and analysis of geographical location data is increasing rapidly. Geographical location data have a spatio-temporal nature which is different from typical business data. Therefore, different methods of data storage and analysis are required. This paper proposes a multi-dimensional data model and data visualization to analyze geographical location data efficiently and effectively. Purchase order data of an online farm products brokerage business was used to build prototype datamart. RFM scores are calculated to classify customers and geocoding technology is applied to display information on maps, thereby to enhance data visualization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.417-426
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• 2019

Conventional visualization of PM (Particulate Matter)10 flows applies superimposition of concentration distribution maps and wind field maps. This method is efficient for small scale maps where only macro flow trends are of interest. However, in the case of urban areas, local flows are difficult to model at micro level using wind fields, and therefore different methods of flow extraction is deemed necessary. In this study, flow information is extracted and visualized directly from the PM10 density data by using the gravity model. This method has the advantage that additional information such as wind field is not necessary for estimating the intensity and direction of PM10 flow. The extracted spatio-temporal flow patterns of PM10 are analyzed with relation to traffic information.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.2
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• pp.158-172
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• 2017

This study analyzes the time-space variability for the weekly passenger flow of the Seoul Subway system based on the dynamic visualization methods. For the purpose, we utilize one-week T-card transaction databases. By applying data mining algorithms, we extract passenger data for edge flows, on/off passengers at each subway station per minute interval time. It is practically intractable to analyze such spatio-temporal passenger flows by general statistical techniques. We employ dynamic visualization methods to analyze intuitively and to grasp effectively characteristics of the diurnal passenger flows on the Seoul Metropolitan Subway system during one week. As the result, we found that substantial differences exist on the spatio-temporal distribution patterns among days as well as between weekdays and weekend. We also investigates the time-space variability among eight major centers, and we found wide differences in their spatio-temporal distribution patterns.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.15-37
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• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• Journal of the Economic Geographical Society of Korea
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• v.21 no.1
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• pp.20-33
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• 2018

Population distribution in urban space varies with transportation flow changing along time of day. Transportation flow is directly affected by the activities of urbanites and the distribution of related facilities, since the flow is the result of moving to the point where the facilities associated with their activities are located. It is thus necessary to analyze the spatio-temporal characteristics of the urban population distribution by integrating the distribution of activity spaces related to the daily life of urbanites and the flow of transportation. The purpose of this study is to analyze the population distribution in urban space with daily and weekly time bases using the building database and T-card database in the city of Seoul, which is rich in information on land use and transportation flow. For a time-based analysis that is difficult to grasp by general statistical techniques, a four-dimensional visualization method combining time and space using a Java program is devised. Dynamic visualization in the four-dimensional space and time allows intuitive analysis and makes it possible to understand more effectively the spatio-temporal characteristics of population distribution. For this purpose, buildings are classified into three activity groups: residential, working, and commercial according to their purpose, and the number of passengers traveling to and from each stop site of bus and subway networks in the T-card database for one week is calculated in one-minute increments, Visualizing these and integrating transportation and land use, we analyze spatio-temporal characteristics of the population distribution in Seoul. As a result, it is found that the population distribution of Seoul displays distinct spatio-temporal characteristics according to land use. In particular, there is a clear difference in the population distribution pattern along the time axis according to the mixed aspects of working, commercial, and residential activities. The results of this study can be very useful for transportation and location planning of city facilities.

• Journal of the Korean Society of Visualization
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• v.21 no.2
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• pp.14-23
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• 2023

The pressure difference across stenotic blood vessels is a commonly used clinical metric for diagnosing many cardiovascular diseases. At present, most clinical pressure measurements rely solely on invasive catheterization. In this study, we propose a novel method for non-invasive pressure estimation using the incompressible Navier-Stokes equations and a 3D multi-path integration approach. We verify spatio-temporal convergence on an in-silico dataset of a cylindrical straight pipe phantom with steady and pulsatile flow fields. We then evaluate the proposed method on an in vitro dataset of reconstructed control, pre-operative, and post-operative carotid artery cases acquired from 4D flow MRI. The performance of our method is compared to existing approaches based on the pressure Poisson equation and work-energy relative pressure. The results demonstrate the proposed method's high accuracy, robustness to spatio-temporal subsampling, and reduced sensitivity to noise, highlighting its great potential for non-invasive pressure estimation.