• Journal of the Korean Data and Information Science Society
• /
• v.28 no.1
• /
• pp.39-47
• /
• 2017

Precipitation is an important component for hydrological and water control study. In general, AWS data provides more accurate but low dense information for precipitation while radar data gives less accurate but high dense information. The objective of this study is to construct adjusted precipitation field based on hierarchical spatial model combining radar data and AWS data. Here, we consider a Bayesian hierarchical model with spatial structure for hourly accumulated precipitation. In addition, we also consider a redistribution of hourly precipitation to 2.5 minute precipitation. Through real data analysis, it has been shown that the proposed approach provides more reasonable precipitation field.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.8 no.5
• /
• pp.57-63
• /
• 2008

Recently, OGC(Open Geospatial Consortium) take the lead in SWE(Sensor Web Enablement) research that collection various context information from sensor networks and show it on map by web. OGC SWE WG(Working Group) defines a standard encoding about realtime spatiotemporal appear geographical feature, sensing data and support web services. This paper proposes a visualization scheme of sensing data based location on 2D maps. We show realtime sensing data on moving node that mapping GPS data on map. First, we present an algorithm and procedure that location information change to position of maps for visualization sensing data based on 2D maps. For verifying that algorithm and scheme, we design and implement a program that collecting GPS data and sensing data, and displaying application on 2D maps. Therefore we confirm effective visualization on maps based on web which realtime image and sensing data collected from sensor network.

• Journal of Korean Society of Transportation
• /
• v.35 no.6
• /
• pp.525-544
• /
• 2017

With the advent of the big data era, the interest in the development of land using traffic data has increased significantly. However, the current research on traffic big data lingers around organizing or calibrating the data only. In this research, a novel method for discovering the hidden values within the traffic data through data mining is proposed. Considering the fact that traffic data and network structures have similarities, network analysis algorithms are used to find valuable information in the actual traffic volume data. The PageRank and HITS algorithms are then employed to find the centralities. While conventional methods present centralities based on uncomplicated traffic volume data, the proposed method provides more reasonable centrality locations through network analysis. Since the centrality locations that we have found carry detailed spatiotemporal characteristics, such information can be used as an objective basis for making policy decisions.

• Journal of Korea Spatial Information System Society
• /
• v.11 no.3
• /
• pp.63-69
• /
• 2009

Climate change has influenced on various sectors including ecosystem, water resource, natural hazards and health and so on. Thus, it is essential to more accurately assess climate change impact and prepare adaptation strategy. However, it is difficult to assess for climate change impact on various sectors with integrated form due to various data format by sectors. In this study, we prepared criteria and indicators for assessing climate change impact and integrated GIS based data which in correspond to indicators based on spatio-temporal information using GIS. Finally we suggest a guideline to assess vulnerability of each sectors to climate change based on integrated spatio-temporal information.

• Annual Conference of KIPS
• /
• 2005.05a
• /
• pp.43-46
• /
• 2005

다차원 분석을 위한 OLAP 연산에서는 사용자의 요청에 빠르게 응답하기 위해 집계 값을 미리 계산하여 저장해 두는 사전 집계 방식을 이용한다. 시공간 데이터에 대한 사전 집계 기법으로는 R-트리의 각 노드에 대한 과거 집계 값을 요약 테이블로 관리하는 기법과 R-트리의 노드에서 현재 집계 값을 관리하는 기법이 있다. 그러나 이 기법들은 현재와 과거 모두의 집계 정보를 필요로 하는 시스템에서는 성능이 저하되며, 특히 과거 집계 정보의 경우 시간에 따른 계층화가 되어있지 않아 시간에 대한 계층 분석에 어려움이 있다. 본 논문에서는 시공간 데이터의 현재와 과거 집계 정보를 효율적으로 관리하는 기법을 제안한다. 제안 기법은 aR-tree를 이용하여 해당 영역에 대한 현재 집계 정보를 저장하고, 각 노드에 과거 집계 정보에 대한 연결을 위하여 링크를 추가하였다. 과거 집계 정보는 각 노드의 과거에서 현재까지의 집계 정보를 계층 구조로 유지하는 시간 요약 집계 테이블을 만들어 저장한다. 따라서 제안한 기법은 현재와 과거 집계 정보를 모두 유지할 수 있으므로 현재와 과거 집계 정보에 대한 처리 성능을 향상시킨다. 또한 제안 기법에서는 공간 정보를 공간 인덱스인 R-트리로 유지하고, 과거로부터의 시간 정보를 시간 요약 집계 테이블을 이용하여 계층화시켜 유지하므로 시간과 공간에 대한 계층 분석이 용이하다.

• Smart Structures and Systems
• /
• v.25 no.6
• /
• pp.733-749
• /
• 2020

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.166-166
• /
• 2023

Compound hazards (CHs) are two or more extreme climate events combined which occur simultaneously in the same region at the same time. Compared to individual hazards, the combination of hazards that cause CHs can result in greater economic losses and deaths. While several extreme climate events have been recorded across Asia for the past decades, many studies have only focused on a single hazard. In this study, we assess the spatiotemporal pattern of dry compound hazards which includes drought, heatwave, fire and wind across Asia for the last 42 years (1980-2021) using the historical data from ERA5 Reanalysis dataset. We utilize a daily spatial data of each climate event to assess the occurrence of such compound hazards on a daily basis. Heatwave, fire and wind hazard occurrences are analyzed using daily percentile-based thresholds while a pre-defined threshold for SPI is applied for drought occurrence. Then, the occurrence of each type of compound hazard is taken from overlapping the map of daily occurrences of a single hazard. Lastly, a multivariate assessment are conducted to quantify the occurrence frequency, hotspots and trends of each type of compound hazard across Asia. By conducting a multivariate analysis of the occurrence of these compound hazards, we identify the relationships and interactions in dry compound hazards including droughts, heatwaves, fires, and winds, ultimately leading to better-informed decisions and strategies in the natural risk management.

• Annual Conference of KIPS
• /
• 2008.11a
• /
• pp.878-881
• /
• 2008

To index the object's trajectory is an important aspect in moving object database management. This paper implements an optimizing index structure named Rend 3D R-tree based on 3D R-Tree. This paper demonstrates the time period update method to reconstruct the MBR for the moving objects in order to decrease the dead space that is produced in the closed time dimension of the 3D R-tree, then a rend method is introduced for indexing both current data and history data. The result of experiments illustrates that given methods outperforms 3D R-Tree and LUR tree in query processes.

• Journal of Cadastre & Land InformatiX
• /
• v.47 no.2
• /
• pp.233-254
• /
• 2017

The purpose of this study is to analyze the main factors influencing the severity of traffic accidents and to visualize spatiotemporal characteristics of traffic accidents in Seoul. To do this, we collected the traffic accident data that occurred in Seoul for four years from 2012 to 2015, and classified as slight, serious, and death traffic accidents according to the severity of traffic accidents. The analysis of spatiotemporal characteristics of traffic accidents was performed by kernel density analysis, hotspot analysis, space time cube analysis, and Emerging HotSpot Analysis. The factors affecting the severity of traffic accidents were analyzed using decision tree model. The results show that traffic accidents in Seoul are more frequent in suburbs than in central areas. Especially, traffic accidents concentrated in some commercial and entertainment areas in Seocho and Gangnam, and the traffic accidents were more and more intense over time. In the case of death traffic accidents, there were statistically significant hotspot areas in Yeongdeungpo-gu, Guro-gu, Jongno-gu, Jung-gu and Seongbuk. However, hotspots of death traffic accidents by time zone resulted in different patterns. In terms of traffic accident severity, the type of accident is the most important factor. The type of the road, the type of the vehicle, the time of the traffic accident, and the type of the violation of the regulations were ranked in order of importance. Regarding decision rules that cause serious traffic accidents, in case of van or truck, there is a high probability that a serious traffic accident will occur at a place where the width of the road is wide and the vehicle speed is high. In case of bicycle, car, motorcycle or the others there is a high probability that a serious traffic accident will occur under the same circumstances in the dawn time.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.807-821
• /
• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.