• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• Journal of the Korean Geographical Society
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• v.46 no.2
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• pp.197-211
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• 2011

Due to the complexity of spatial interaction and the necessity of spatial representation and modeling, aggregation of spatial interaction data is indispensible. Given this, the purpose of this paper is to evaluate the effects of modifiable areal unit problem (MAUP) on a spatial interaction model. Four aggregation schemes are utilized at eight different scales: 1) randomly select seeds of district and then allocate basic spatial units to them, 2) minimize the sum of population weighted distance within a district, 3) maximize the proportion of flow within a district, and 4) minimize the proportion of flow within a district. A simple Poisson regression model with origin and destination constraints is utilized. Analysis results demonstrate that spatial characteristics of residuals, parameter values, and goodness-of-fit of the model were influenced by aggregation scale and schemes. Overall, the model responded more sensitively to aggregation scale than aggregation schemes and the scale effect on the model was varied according to aggregation schemes.

• Spatial Information Research
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• v.16 no.4
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• pp.441-453
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• 2008

With the rapid progress of location based services, GIS, and ubiquitous computing technologies, the space that we are dealing with is no longer limited to outdoor space but being extended to indoor space. Indoor space has some differences from outdoor space, therefore to provide integrated spaces and seamless services, it is required to establish new theories, data models, and systems. For this reason, ambitious project has been launched last year to establish a theoretical background, develop a core technologies and systems, and provide services of indoor spatial awareness. In this paper, we present an overall sketch on the project and major research topics. First, we present the ISA (indoor spatial awareness) project with its goal and research topics. Second, a simplified 3D spatial model, called prism model, is proposed as a basic data types and operators of indoor spatial DBMS. Third, a indoor feature data model, developed T. Kolbe et al. who is a member of this project team, is introduced in this paper. This model provides a basis for the integration of different spaces.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.231-245
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• 2000

This paper considers a linear regression model with space and time data in where the disturbances follow spatially correlated error components. We provide the best linear unbiased predictor for the one way error components. We provide the best linear unbiased predictor for the one way error component model with spatial autocorrelation. Further, we derive two diagnostic test statistics for the assessment of model specification due to spatial dependence and random effects as an application of the Lagrange Multiplier principle.

• Korean Institute of Interior Design Journal
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• v.20 no.4
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• pp.83-91
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• 2011

The purpose of this study is to set a relationship between intentional system and spatial orientation, and with space model in the human world to orient yourself is to present the basic spatial concepts. This is composed of parts of intentional system and spatial orientation on the research. Through the study of intentional system which is based on the orientation with Daniel Dennett's, it suggests the space-model that composed with aspects of intentionality and spatial elements. With space-model and judgement of spatial types, it makes relationship confirm between them. Through this process, following the results of this study were derived. First, intentional stance of space is the key for building knowledge and memories about space, and for identifying external environment images which are experienced in the senses of human. Second, changing the meaning from space to place makes horizons of space broader and creates a new sense of space with put the intentional orientation into the space. Third, this study can make confirm what the aspect of the space-schema-elements for orientation in space, serves as the presentation elements. This study leaps an old-view of the architectural customs about spatial orientation, and creates an opportunity to refine the newer concept of space. This concept of space is a basic essential for 'site-selection' and 'spot-catching' as an intentional system for spatial orientation and to establish a relationship with human beings in the world of his own orientation to the concept of space.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.132-144
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• 2018

3D Geo-spatial information models have been widely used in the field of Civil Engineering, Medical, Computer Graphics, Urban Management and many other. Especially, in surveying and geo-spatial field, the demand for high quality 3D geospatial information and indoor spatial information is so highly increasing. However, it is so difficult to provide a low-cost and high efficiency service to the field which demand the highest quality of 3D model, because pre-constructed spatial data are composed of different formats and storage structures according to the application purpose of each institutes. In fact, the techniques to construct a high applicable 3D geo-spatial model is very expensive to collect and analyze geo-spatial data, but most demanders of 3D geo-spatial model never want to pay the high-cost to that. This study, therefore, suggest the effective way to construct 3D geo-spatial model with low-cost of construction. In general, the effective way to reduce the cost of constructing 3D geo-spatial model as presented in previous studies is to combine the raw data obtained from point cloud observatory and UAV imagery, however this method has some limitation of usage from difficulties to approve the use of raw data because of those have been managed separately by various institutes. To solve this problem, we developed the linking & management system for unifying a high-Resolution raw geo-spatial data based on the point cloud DB and apply this system to extract the basic database from 3D geo-spatial mode for the road database registration. As a result of this study, it can be provided six contents of main entries for road registration by applying the developed system based on the point cloud DB.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.81-95
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• 2009

The CDB(Constraint Database) model is a new paradigm for massive spatial data processing such as GIS(Geographic Information System). This paper will identify the limitation of the schema structure and query processing through prior spatial database research and suggest more efficient processing mechanism of constraint data model. We presented constraint model concept, presentation method, and the examples of query processing. Especially, we represented TIN (Triangulated Irregular Network) as a constraint data model which displays the height on a plane data and compared it with prior spatial data model. Finally, we identified that we were able to formalize spatial data in a simple and refined way through constraint data modeling.