• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.137-147
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• 2006

Spatiotemporal data are records of the spatial changes of moving objects over time. Most data in corporate databases have a spatiotemporal nature, but they are typically treated as merely descriptive semantic data without considering their potential visual (or cartographic) representation. Businesses such as geographical CRM, location-based services, and technologies like GPS and RFID depend on the storage and analysis of spatiotemporal data. Effectively handling the data analysis process may be accomplished through spatiotemporal data warehouse and spatial OLAP. This paper proposes a multidimensional model for spatiotemporal data analysis, and cartographically represents the results of the analysis.

• Physical Therapy Korea
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• v.8 no.1
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• pp.20-34
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• 2001

This paper presents the relevance between GMFM and the spatiotemporal parameters of gait in children with cerebral palsy. Twenty-one children ($73.11{\pm}30.06$ months) with cerebral palsy participated in this study. GMFM was performed and spatiotemporal parameters of gait were measured by foot print gait analysis. A correlation analysis was used to investigate the correlation between GMFM scores and spatiotemporal parameters of gait. A linear regression analysis was employed to find how much each gait spatiotemporal parameters could be predicted from GMFM scores. The total GMFM scores was significantly correlated with walking speed, cadence, and stride length. Dimensions D (standing) and E (walking, running, and jumping) were more significantly correlated with gait spatiotemporal parameters than dimensions A (lying and rolling), B (sitting), and C (crawling and kneeling). The GMFM scores were useful for predicting spatiotemporal parameters. However, it is difficult to predict the status of gait development using GMFM scores because GMFM scores and gait spatiotemporal parameters are only measured as quantities not qualities. In the field, it is easily found that many children with cerebral palsy are unable to walk in any way. Consequently, gait analysis cannot be performed in many cases. Therefore, it is more reasonable to investigate the influence of GMFM on spatiotemporal parameters, rather than vice versa.

• Journal of KIISE:Databases
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• v.32 no.1
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• pp.43-55
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• 2005

Spatiotemporal databases support methods of recording and querying for spatiotemporal data to user by offering both spatial management and historical information on various types of objects in the real world. We can answer to the following query in real world: 'What is the average of volume of pesticide sprayed for cach farm land from April to August on 2001, within some query window' Such aggregation queries have both temporal and spatial constraint. However, previous works for aggregation are attached only to temporal aggregation or spatial aggregation. So they have problems that are difficult to apply for spatiotemporal data directly which have both spatial and temporal constraint. Therefore, in this paper, we propose spatiotemporal aggregate functions for analysis of spatiotemporal data which have spatiotemporal characteristic, such as stCOUNT, stSUM, stAVG, stMAX, stMIN. We also show that our proposal resulted in the convenience and improvement of query in application systems, and facility of analysis on spatiotemporal data which the previous temporal or spatial aggregate functions are not able to analyze, by applying to the estate management system. Then, we show the validity of our algorithm performance through the evaluation of spatiotemporal aggregate functions.

• Journal of Biomedical Engineering Research
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• v.30 no.5
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• pp.373-380
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• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.37-50
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• 2003

In the paper, we describe the query extension techniques for spatiotemporal query functionalities on object-relational DBMS. The spatial objects in real world change the shapes over time. Spatiotemporal databases support to manage a temporal dimension as well as a spatial dimension for history of the objects. The proposed techniques can make conventional object-relational databases to support spatiotemporal databases system by the implementation and inheritance of abstract data types. We define and implement spatial and temporal classes as superclass. And, spatiotemporal classes inherits and extends the classes. The proposed extensions make it easy that conventional database systems not only are transformed into the spatiotemporal database systems, but also do not need to be changed to support spatiotemporal applications.

• 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.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.933-945
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• 2014

Bayesian spatiotemporal analysis is of considerable interest to epidemiological applications because health data is collected over space-time with complicated dependency structures. A basic concept in spatiotemporal modeling is introduced in this paper to analyze space-time disease data. The paper reviews a range of Bayesian spatiotemporal models and analyzes Hepatitis A data in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• Physical Therapy Korea
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• v.22 no.4
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• pp.27-33
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• 2015

The purpose of this study was to find which spatiotemporal gait parameters gained from stroke patients could be predictive factors for the gait part of Tinetti Performance-Oriented Mobility Assessment (POMA-G). Two hundred forty-six stroke patients were recruited for this study. They participated in two assessments, the POMA-G and computerized spatiotemporal gait analysis. To analyze the relationship between the POMA-G and spatiotemporal parameters, we used Pearson's correlation coefficients. In addition, multiple linear regression analyses (stepwise method) were used to predict the spatiotemporal gait parameters that correlated most with the POMA-G. The results show that the gait velocity (r=.67, p<.01), cadence (r=.66, p<.01), step length of the affected side (r=.49, p<.01), step length of the non-affected side (r=.53, p<.01), swing percentage of the non-affected side (r=.47, p<.01), and single support percentage of the affected side (r=.53, p<.01) as well as the double support percentage of the non-affected side (r=-.42, p<.01) and the step-length asymmetry (r=-.64, p<.01) correlated with POMA-G. The gait velocity, step-length asymmetry, cadence, and single support percentage of the affected side explained 67%, 2%, 2%, and 1% of the variance in the POMA-G, respectively. In conclusion, gait velocity would be the most predictive factor for the POMA-G.

• 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.