• Journal of the Korean Geographical Society
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• v.37 no.2
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• pp.191-202
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• 2002

Characteristics and patterns of geographic features and human activities can be interpreted in terms of spatiality and temporality. The necessity to record the historical changes and the ability to reason in the real world has lead to a new field of research so called Integrated Spatio-Temporal analysis. The objective of this study is to investigate temporal maps for Spatio-temporal analysis, which have the integration functionality for visualizing spatiality and temporality of the geographic appearances and human activities. Land information is composed of spatial, attribute and temporal data and requires spatio-temporal representations. It is possible to visualize spatio-temporal variations with spatio-temporal databases and temporal map produced by integrated data models. This study constructs spatio-temporal model for temporal maps of land price variation analysis. Taking advantage of the spatio-temporal model proposed here, it is possible to visualize spatio-temporal variations with spatio-temporal database and temporal map. On a practical level, this study would be extended and utilized to various geographic features.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.21-31
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• 2012

Recently, according to the development of ubiquitous computing technology, the efficient management of locations of moving objects is increasing rapidly in various fields. However, MODBMS and DSMS can not support the efficient real-time management of spatio-temporal stream data of moving objects. Therefore, this paper designed and implemented a spatio-temporal DSMS which can support the efficient real-time management of spatio-temporal stream data of moving objects. Especially, to develop the spatio-temporal DSMS, we extended STREAM of Stanford University and used GEOS that supports spatial data types and spatial operators of OGC. Finally, this paper proved the efficiency of the spatio-temporal DSMS by applying it to the real-time monitoring field which requires the real-time management of spatio-temporal stream data of moving objects.

• Spatial Information Research
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• v.20 no.3
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• pp.95-106
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• 2012

Recently, with increase and development of the wireless access network area, u-GIS Service is supported in various fields. Especially, spatio-temporal data is used in the mobile environment for the u-GIS service. However, there is no standard for the spatio-temporal data used in different spaces, spatio-temporal data processing technology is necessary to makes interoperability among mobile u-GIS services. Furthermore, it is also necessary to develop the system of gathering, storing, and managing the spatio-temporal data in consideration of small capacity and low performance of mobile devices. Therefore, in this paper, we designed and implemented a spatio-temporal query processing system based on GML to manage spatio-temporal data efficiently in the mobile environment. The spatio-temporal query processing system based on GML can offer a structured storage method which maps a GML schema to a storage table and a binary XML storage method which uses the Fast Infoset technique, so as to support interoperability that is an important feature of GML and increase storage efficiency. we can also provide spatio-temporal operators for rapid query processing of spatio-temporal data of GML documents. In addition, we proved that this system can be utilized for the u-GIS service to implement a virtual scenario.

• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.178-181
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• 2003

The temporal GIS data modeling methods are discussed in this paper. At first, two conceptual models of spatio-temporal data are introduced, and then some typical STDMs based on these two models are summed up and compared. After that, the spatio-temporal changes are analyzed thoroughly, and then how to model spatio -temporal data from different aspects is discussed. At last, several issues that need further research are pointed out.

• Spatial Information Research
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• v.18 no.1
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• pp.77-87
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• 2010

Spatio-temporal join operators are essential to the management of spatio-temporal data such as moving objects. For example, the join operators are parts of processing to analyze movement of objects and search similar patterns of moving objects. Various studies on spatio-temporal join queries in outdoor space have been done. Recently with advance of indoor positioning techniques, location based services are required in indoor space as well as outdoor space. Nevertheless there is no one about processing of spatio-temporal join query in indoor space. In this paper, we introduce continuous spatio-temporal self-join queries in indoor space and propose a method of processing of the join queries over stream data of moving objects. The continuous spatio-temporal self-join query is to update the joined result set satisfying spatio-temporal predicates continuously. We assume that positions of moving objects are represented by symbols such as a room or corridor. This paper proposes a data structure, called Candidate Pairs Buffer, to filter and maintain massive stream data efficiently and we also investigate performance of proposed method in experimental study.

• Journal of Platform Technology
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• v.11 no.1
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• pp.52-59
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• 2023

The importance of spatio-temporal trajectories for contact tracing has increased due to the recent COVID-19 pandemic. Spatio-temporal trajectories store time and spatial data of moving objects. In this paper, I propose query processing for spatio-temporal trajectories of moving objects. The spatio-temporal trajectory model of moving objects has point type spatial data for storing locations and timestamp type temporal data for time. A trajectory query is a query to search for pairs of users who have been in close contact by boarding the same bus. To process the trajectory query, I use the Geolife dataset provided by Microsoft. The proposed trajectory query processing method divides trajectory data by date and checks whether users' trajectories were nearby for each date to generate information about contacts as the result.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.49-63
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• 2009

The performance of spatio-temporal moving pattern mining depends on how to analyze and process the huge set of spatio-temporal data due to the nature of it. The several method was presented in order to solve the problems in which existing spatio-temporal moving pattern mining methods[1-10] have, such as increasing execution time and required memory size during the pattern mining, but they did not solve properly yet. Thus, we proposed the STMP/MST method[11] as a preceding research in order to extract effectively sequential and/or periodical frequent occurrence moving patterns from the huge set of spatio-temporal moving data. The proposed method reduces patterns mining execution time, using the moving sequence tree based on hash tree. And also, to minimize the required memory space, it generalizes detailed historical data including spatio-temporal attributes into the real world scopes of space and time by using spatio-temporal concept hierarchy. In this paper, in order to verify the effectiveness of the STMP/MST method, we compared and analyzed performance with existing spatio-temporal moving pattern mining methods based on the quantity of mining data and minimum support factor.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.564-566
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• 2003

There are no immutable phenomena in reality. A lot of applications are dealing with data characterized by spatial and temporal and/or uncertain features. Currently, there has no any data model accommodating enough those three elements of spatial objects to directly use in application systems. For such reasons, we introduce a fuzzy spatio -temporal data model (FSTDM) and a method of integrating temporal and fuzzy spatial operators in a unified manner to create fuzzy spatio -temporal (FST) operators. With these operators, complex query expression will become concise. Our research is feasible to apply to the management systems and query processor of natural resource data, weather information, graphic information, and so on.