• Journal of Korean Society for Geospatial Information Science
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• v.10 no.5 s.23
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• pp.17-28
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• 2002

Time is regarded as an essential feature of land information enabling to track historical landmarks of land uses, ownerships, and taxations based on cadastral maps. Object-oriented temporal modeling helps to simulate and imitate time-varying cadastral data in a chronological and persistent manner. The aim of study is to analyze the role of temporal cadastre tracing footprints of foregoing events in response to various needs and demands associated with historical information of cadastral transactions. In this paper, temporal cadastral object model (TCOM) is proposed to delineate object version history. As an evidence of a new approach and conceptual idea for the importance of temporal cadastre, a part of spatio-temporal processes is illustrated to explain major changes of cadastral map. The feasibility and application of the approach is confirmed by proof-of-concept of temporal cadastre in land information management.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• 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.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.832-837
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• 2002

As diverse researches are working about location acquisition, storing method, data modeling and query processing of moving objects, the moving object database systems, which can gain, store and manage location information and query processing, are tuning up. As the mobile device is moving but have constraints, the convenience user interface for spatio-temporal query and viewing query result needs. In this paper, we designed user Interface for spatio-temporal query related moving objects, viewing query result, tracing current and past location of those and monitoring. And we designed system for implementation of these interfaces.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.67-80
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• 2017

This paper presents a hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized view. A new temporal background model using non-overlapped patch based background codebook is introduced to extrapolate temporal background information In addition, a depth-map driven spatial local background estimation is addressed to define spatial background constraints that represent the lower and upper bounds of a background candidate. Background holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. Additionally, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view after 3-D warping. Finally, an inpainting is applied to fill in the remaining holes with the priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over the state-of-the-art methods.

• Journal of Korean Society on Water Environment
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• v.29 no.3
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• pp.365-376
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• 2013

The purpose of this study was to evaluate effects of spatio-temporal changes in land uses and rainfall magnitude using the Soil and Water Assessment Tool (SWAT). Prior of application of the model to real-world problem, the model should be calibrated and validated properly. In most modeling approaches, the validation process is done assuming no significant changes occurring at the study watershed between calibration and validation periods, which is not proper assumption for agricultural watersheds. If simulated results obtained with calibrated parameters match observed data with higher accuracy for validation period, this does not always mean the simulated result represents rainfall-runoff, pollutant generation and transport mechanism for validation period because temporal and spatial variables and rainfall magnitude are often not the same. In this study SWAT was applied to Mandae study watershed in Korea to evaluate effects of spatio-temporal changes in landuses using 2009 and 2010 crop data for each field at the watershed. The Nash-Sutcliffe model efficiency (NSE) values for calibration and validation with either 2009 or 2010 was evaluated and the NSE value for calibration with 2009 and calibration with 2010 were compared. It was found that if there is substantial change in land use and rainfall, model calibration period should be determined to reflect those changes. Through these approaches, inherent limitation of the SWAT, which does not consider changes in land uses over the simulation period, was investigated. Also, Effects of changes in rainfall magnitude during calibration process were analyzed.

• Atmosphere
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• v.21 no.4
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• pp.337-347
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• 2011

In this paper, we developed a detection and correction method of noisy pixels embedded in the time series of normalized difference vegetation index (NDVI) data based on the spatio-temporal continuity of vegetation conditions. For the application of the method, 25-year (1982-2006) GIMMS (Global Inventory Modeling and Mapping Study) NDVI dataset over the Korean peninsula were used. The spatial resolution and temporal frequency of this dataset are $8{\times}8km^2$ and 15-day, respectively. Also the land cover map over East Asia is used. The noisy pixels are detected by the temporal continuity check with the reference values and dynamic threshold values according to season and location. In general, the number of noisy pixels are especially larger during summer than other seasons. And the detected noisy pixels are corrected by the iterative method until the noisy pixels are completely corrected. At first, the noisy pixels are replaced by the arithmetic weighted mean of two adjacent NDVIs when the two NDVI are normal. After that the remnant noisy pixels are corrected by the weighted average of NDVI of the same land cover according to the distance. After correction, the NDVI values and their variances are increased and decreased by 5% and 50%, respectively. Comparing to the other correction method, this correction method shows a better result especially when the noisy pixels are occurred more than 2 times consistently and the temporal change rates of NDVI are very high. It means that the correction method developed in this study is superior in the reconstruction of maximum NDVI and NDVI at the starting and falling season.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.40-46
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• 2008

3D visualization of geological environments using remotely sensed data and the various sources of data provides new methodology to interpret geological observation data and analyze geo-information in earth science applications. It enables to understand spatio-temporal relationships and causal processes in the three-dimension, which would be difficult to identify without 3D representation. To build more realistic geological environments, which are useful to recognize spatial characteristics and relationships of geological objects, 3D modeling, topological analysis, and database should be coupled and taken into consideration for an integrated configuration of the system. In this study, a method for 3D visualization, extraction of geological data, storage and data management using remotely sensed data is proposed with the goal of providing a methodology to utilize dynamic spatio-temporal modeling and simulation in the three-dimension for geoscience and earth science applications.