• Journal of KIISE
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• v.43 no.2
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• pp.135-142
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• 2016

Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.

• ETRI Journal
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• v.30 no.3
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• pp.421-431
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• 2008

In this paper, we offer a new technique to discover frequent spatiotemporal patterns from a moving object database. Though the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and timing constraints on moving sequences makes the computation feasible. The proposed technique includes two algorithms, AllMOP and MaxMOP, to find all frequent patterns and maximal patterns, respectively. In addition, to support the service provider in sending information to a user in a push-driven manner, we propose a rule-based location prediction technique to predict the future location of the user. The idea is to employ the algorithm AllMOP to discover the frequent movement patterns in the user's historical movements, from which frequent movement rules are generated. These rules are then used to estimate the future location of the user. The performance is assessed with respect to precision and recall. The proposed techniques could be quite efficiently applied in a location-based service (LBS) system in which diverse types of data are integrated to support a variety of LBSs.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.294-298
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• 2015

In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.61-66
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• 2005

Spatiotemporal data mining represents the confluence of several fields including spatiotemporal databases, machine loaming, statistics, geographic visualization, and information theory. Exploration of spatial data mining and temporal data mining has received much attention independently in knowledge discovery in databases and data mining research community. In this paper, we introduce an algorithm Max_MOP for discovering moving sequences in mobile environment. Max_MOP mines only maximal frequent moving patterns. We exploit the characteristic of the problem domain, which is the spatiotemporal proximity between activities, to partition the spatiotemporal space. The task of finding moving sequences is to consider all temporally ordered combination of associations, which requires an intensive computation. However, exploiting the spatiotemporal proximity characteristic makes this task more cornputationally feasible. Our proposed technique is applicable to location-based services such as traffic service, tourist service, and location-aware advertising service.

• Physical Therapy Rehabilitation Science
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• v.11 no.1
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• pp.105-112
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• 2022

Objective: Nonspecific low back pain (NS-LBP) causes pain and disability, affecting the neuromuscular system and altering gait patterns. The purpose of this study is to investigate the effect of improvement of low back pain symptoms through physical therapy on foot pressure and spatiotemporal gait parameters. Design: A pilot study. Methods: Participants received manual therapy and supervised therapeutic exercise, which consisted of 12 sessions for 6 weeks. Participants were assessed for pain intensity (a numeric pain rating scale), disability index (oswestry disability index), and spatiotemporal gait parameters before and after intervention. Wilcoxon signed rank test was used to analyze the before-and-after differences in a single group. Results: All seven NS-LBP patients completed the study without dropout. After six weeks of physical therapy, the numeric pain rating scale and oswestry disability index showed significant improvement (Z= -2.388, P=0.017). There was no significant improvement in both static and dynamic conditions in foot pressure (P>0.05). However, in the spatiotemporal gait parameters, there were significant differences in all variables except the right stance phase and left mid stance (P<0.05). Conclusions: In our pilot study, 12 sessions of physical therapy in NS-LBP patients improved gait quilty in spatiotemporal gait parameters. Similarly, it has resulted in clinically positive improvements in pain and disability.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1103-1114
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• 2003

Currently, one of the most critical issues in developing the service support system for various spatio-temporal applications is the discoverying of meaningful knowledge from the large volume of moving object data. This sort of knowledge refers to the spatiotemporal moving pattern. To discovery such knowledge, various relationships between moving objects such as temporal, spatial and spatiotemporal topological relationships needs to be considered in knowledge discovery. In this paper, we proposed an efficient method, MPMine, for discoverying spatiotemporal moving patterns. The method not only has considered both temporal constraint and spatial constrain but also performs the spatial generalization using a spatial topological operation, contain(). Different from the previous temporal pattern methods, the proposed method is able to save the search space by using the location summarization and generalization of the moving object data. Therefore, Efficient discoverying of the useful moving patterns is possible.

• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.74-84
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• 2009

GIS based new postal marketing method is presented in this paper with spatiotemporal mining to cope with domestic mail volume decline and to strengthening competitiveness of postal business. Market segmentation technique for socialogy of population and spatiotemporal prediction of consumer propensity to purchase through spatiotemporal multi-dimensional analysis are suggested to provide meaningful and accurate marketing information with customers. Internal postal acceptance & external statistical data of local districts in the Seoul Metropolis are used for the evaluation of geo-lifestyle clustering and spatiotemporal cube mining. Successfully optimal 14 maketing clusters and spatiotemporal patterns are extracted for the prediction of consumer propensity to purchase.

• Kyungpook Mathematical Journal
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• v.60 no.2
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• pp.335-347
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• 2020

In this paper, we take into account a parasite-host system with reaction-diffusion. Firstly, we derive conditions for Hopf, Turing, and wave bifurcations of the system in the spatial domain by means of linear stability and bifurcation analysis. Secondly, we display numerical simulations in order to investigate Turing pattern formation. In fact, the numerical simulation discloses that typical Turing patterns, such as spotted, spot-stripelike mixtures and stripelike patterns, can be formed. In this study, we show that typical Turing patterns, which are well known in predator-prey systems ([7, 18, 25]), can be observed in a parasite-host system as well.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.173-173
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• 2022

In this study, we investigated whether there is another amaranth GBSS isoform in an attempt to characterize the synthesis of amylose in the pericarp. We used I2/KI staining to analyze the temporal and spatial starch accumulation patterns during seed development. The spatiotemporal starch accumulation patterns in developing seeds were observed by staining with I2/KI. Starch granules were observed in the pericarp in the initial developmental stage (3 DAP). A few starch granules were detected in the perisperm in the early-late developmental stage (8 DAP), during which the pericarp starch contents rapidly decreased. Starch granules were distributed throughout the perisperm in the mid-late developmental stage (15 DAP). Similar results were reported for other cereal crops, including barley, rice, and sorghum. Starch granules in the pericarp are synthesized during the early seed developmental stages but are absent in mature seeds. We recently reported that starch deposits in the perisperm of developing amaranth seeds are detectable only after the initial developmental stage. Prior to this stage, the pericarp is the major site of starch deposition. A recent study suggested that GBSSII isoforms are responsible for amylose synthesis in pericarps.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.179-186
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• 2008

The converge of location-aware devices, GIS functionalities and the increasing accuracy and availability of positioning technologies pave the way to a range of new types of location-based services. The field of spatiotemporal data mining where relationships are defined by spatial and temporal aspect of data is encountering big challenges since the increased search space of knowledge. Therefore, we aim to propose algorithms for mining spatiotemporal patterns in mobile environment in this paper. Moving patterns are generated utilizing two algorithms called All_MOP and Max_MOP. The first one mines all frequent patterns and the other discovers only maximal frequent patterns. Our proposed approach is able to reduce consuming time through comparison with DFS_MINE algorithm. In addition, our approach is applicable to location-based services such as tourist service, traffic service, and so on.