• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.266-268
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• 2003

This is based on the AGENT-LUC model framework. Luangprabang Province has the largest percentage of shifting cultivation area in Laos PDR. The model simulates the spatial and temporal patterns of the shifting cultivation in the study area, using a GIS database while the total area of shifting cultivation is controlled by supply and demand balance of food. The model simulation period is from 1990 to 1999, at a spatial resolution of 500m. The results are evaluated using statistical data and remote sensing images. Through the validation, it is concluded that the trends simulated agrees to that of statistical data and the spatial and temporal patterns are also replicated satisfactorily.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.20-41
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• 2023

Recently, urban shrinkage due to low birth rate and aging population and the decline of local cities are causing a new urban problem of empty houses. This study examines the distribution of vacant homes using spatial panel data collected from 2015 to 2019 at local administraitve districts and estimates the factors of vacant house occurrence using a spatial panel model considering spatio-temporal dependency. As a result, the spatio-temporal dependence of vacant houses was identified and it was estimated using spatial panel model not OLS model. Based on the spatial panel model, it was found that the most influential factor in the occurrence of vacant houses was the housing-related factor. This result shows that policy considerations for housing supply are necessary for the management of vacant housing as well as population movement and poor infrastructure.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2679-2689
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• 1997

In this paper, a multimedia authoring system using temporal/spatial synchronization manager is designed and implemented to support easy and efficient generation of multimedia title. For this goal, a flowchart-oriented logic generator which represents a title author's design intent into a practical title composition logic without extra translation process, and a logic interpreter which translate and implement the generated title logic, are designed. Furthermore, a temporal/spatial synchronization manager which manages temporal/spatial synchronization information between media data for multimedia representation, is designed. Especially, a temporal specification model and MRL, a formal language for the model, are designed to synchronize the temporal relation between media objects. The MRL represents a complex temporal relation by simple and clear form, and synchronizes efficiently multimedia representation according to the author's intent. A presentation frame editor which makes coincidence between visible size of representation media and attachment point, is implemented for spatial synchronization.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.164-164
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• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.553-563
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• 1999

The Spatial Database Engine(SDETM) developed by Environmental Systems Research Institute, Inc. is a spatial database that employs a client-server architecture incorporated with a set of software services to perform efficient spatial operations and to manage large, shared and geographic data sets. It currently supports a wide variety of spatial search methods and spatial relationships determined dynamically. Spatial objects in the space world can be changed by either non-spatial operations or spatial operations. Conventional geographical information systems(GISs) did not manage their historical information, however, because they handle the snapshot images of spatial objects in the world. In this paper we propose a spatio-temporal data model and an algorithm for temporal relationship macro which is able to manage and retrieve the historical information of spatial objects. The proposed spatio-temporal data model and its operations can be used as a software tool for history management of time-varying objects in database without any change.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

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

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.221-237
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• 2005

This study attempts at building a GIS computing environment that incorporates object-relational spatio-temporal database for the time geography model with space-time path, space-time prism and space-time accessibility. The proposed computing environment is composed of ( i ) mobile GIS application for collecting spatio-temporal trajectory data of an individual, ( ii ) spatio-temporal database server that includes time geography model, and (iii) geovisualization client that performs time geographic queries to the spatio-temporal database. The spatio-temporal trajectory data collected by GPS-PDA client is automatically processed and sent to server through data management middleware. The spatio-temporal database implemented by extending a generic DBMS provides spatio-temporal objects, functions, and SQL. The geovisualization client illustrates 3D visual results of the queries about space-time path, space-time prism, and space-time accessibility. This study confirms the possibility of integrating mobile GIS and DBMS for time geography model, and it presents the appropriate database model with spatio-temporal objects and functions that may handle very large data for time geography application.