• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.3
• /
• pp.80-91
• /
• 2008

The Purpose of this study is the applicaion of social network analysis for expanding the use of spatial data in local government. Spatial data generated from UIS projects play very important roles as a means of supporting decision making and solving complicated urban problems, but the utilization of the spatial data has not reach the expected level, considering to the huge amount of investment. Accordingly, there should be efforts in efficient management of spatial data, establishment of a sharing system, and expanded utilization of spatial data. Social network analysis applied to this research is a theory that explains the behaviors and patterns of units forming the system and measures distances between nodes, strength, etc. based on relations among nodes forming the network and the structural characteristics of the network. According to the results of surveying civil servants who were using spatial data on Busan Metropolitan City, obstacles to the sharing of spatial data were mostly non technical factors related to data users' attitude and their relations with circumstances. In order to expand the use of spatial data, this study performed social network analysis that applied the theory of planned behavior and examined the flow of spatial data, and by doing so, we analyzed related personnel's perception, identified obstacles to data sharing, and suggested a framework for promoting the expanded utilization of spatial data.

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

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.551-554
• /
• 2003

Aggregate operator which belongs to query operations are important in specialized systems such as geographic information system(GIS) and spatial database system. Most of data describing objects in the real world are characterized by space and time attributes. Till now, however, works on aggregate operations have only dealt with spatial or temporal aspect of object. The current demand of aggregate operations relates to spatiotemporal data which are contained both spatial and temporal data concurrently. Therefore, work on spatiotemporal operations is focused on database area. In this paper, we propose spatiotemporal aggregate functions that operate on spatiotemporal data. Above all, we support spatiotemporal aggregate functions on the basis of three dimensional spatiotemporal models that are defined with the linear one dimensional temporal domain. The proposed algorithms are evaluated through some implementation results. We are sure that the achievement of our work is useful and efficient.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.479-484
• /
• 1999

Rice crop has relatively short growing season during the summer in Korea and, therefore, it is often difficult to acquire cloud-free imagery on time. This study was attempt to define the temporal characteristics of radar backscattering observed from satellite L-band SAR data on different growing stages of rice crop. Six scenes of multi-temporal JERS SAR data were obtained from the transplanting season to the harvesting month of October. Six layers of multi-temporal SAR data were registered on a common geographic coordinate system. Using topographic maps, field collected data, and Landsat TM data, several sample rice fields were delineated from the imagery and their relative radar backscatters were calculated by using a set of reference targets. The temporal pattern of radar backscattering was very distinctive by the growing stage of rice crop. It was also separable between two types of rice fields having different cultivation practices. Considering the temporal characteristics of radar backscattering observed from the study, it is obvious that a certain date of the growing season can be more effective to delineate the exact area of the cultivated rice crop field.

• Journal of Fisheries and Marine Sciences Education
• /
• v.17 no.1
• /
• pp.76-85
• /
• 2005

GIS (Geographic Information Systems) technology is a necessary function to support location based on service by using GPS in the mobile environment. These mobile systems have basic functional limitations such as a low rate of processing, limited memory capacity, and small screen size. Because of these limitations, most of the mobile systems require development of a reduced digital map to overcome problems with large-volume spatial data. In this paper, we suggest using the reduced digital map format in order to use location based on service in a PDA environment. The processing of the proposed data format consists of map generation, redefinition of layers, creating polygons, and format conversion. The proposed data format reduces the data size by about 98% comparing with DXF format based on the digital map of Busan.

• Journal of Korean Society for Geospatial Information Science
• /
• v.4 no.1 s.6
• /
• pp.57-66
• /
• 1996

For the effective use of spatial data, it is necessary to translate the diverse digital map data (here, digital map for car navigation system) into common GIS data structure(here, ARC/INFO). For this purpose, analysis on the structure of the established digital map was fulfilled, the data model for effective translation tool was designed. As a result, the possibility to translate diverse digital map structures (database) into GIS tools and to share them, with the users' point of view, was successfully verified.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.129-131
• /
• 2003

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. The topographic and geologic data and satellite image were collected, processed and constructed into a spatial database using GIS and image processing. The used factors that influence landslide occurrence are topographic slope, topographic aspect topographic curv ature and distance from drainage from topographic database, geology and distance from lineament from the geologic database, land use from TM satellite image and vegetation index value from SPOT satellite image. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by probability - likelihood ratio - method. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide location.

• Korean Journal of Geomatics
• /
• v.4 no.1
• /
• pp.17-22
• /
• 2004

It is very important to manage efficient data for safety and maintenance of those constructs. Estimation for structural safety can be evaluated by using data that surveys various structural durability and safety elements. so, it should be based on synthetic and efficient data that includes a variety of related safety elements obtained from a structure. It will subsequently be managed properly and economically. Accordingly, we will approach efficient maintenance management using a Geographic Information System (GIS) with data from structural-safety diagnosis and a Global Positioning System (GPS). In this study, we noted that by using the data that measures the factors (crack, incline, settlement etc.) of various structures as evaluate safety degree. And the horizontal coordinate variation/time of structure was monitored using the GPS easily.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.5
• /
• pp.359-366
• /
• 2019

Indoor spatial data has great importance as the demand for representing the complex urban environment in the context of providing LBS (Location-based Services) is increasing. IndoorGML (Indoor Geographic Markup Language) has been established as the data standard for spatial data in providing indoor navigation, but its definitions and relationships must be expanded to increase its applications and to successfully delivering information to users. In this study, we propose an approach to integrate IndoorGML with Indoor POI (Points of Interest) data by extending the IndoorGML notion of space and topological relationships. We consider two cases of representing Indoor POI, by 3D geometry and by point primitive representation. Using the concepts of the NRS (node-relation structure) and multi-layered space representation of IndoorGML, we define layers to separate features that represent the spaces and the Indoor POI into separate, but related layers. The proposed methodology was implemented with real datasets to evaluate its effectiveness for performing indoor spatial analysis.

• International journal of advanced smart convergence
• /
• v.7 no.4
• /
• pp.27-39
• /
• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.