• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.7
• /
• pp.80-86
• /
• 2015

As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

• Journal of information and communication convergence engineering
• /
• v.13 no.2
• /
• pp.105-112
• /
• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• Journal of Korea Trade
• /
• v.26 no.1
• /
• pp.99-112
• /
• 2022

Purpose - This paper empirically investigates via a spatial lag model from the perspective of space economy to find the influencing factors of South Korea's OFDI along with 60 countries. Design/methodology - In the study of regional economic phenomena, we must first test the corresponding spatial correlation, and on this basis, complete the construction of the spatial model. For the target research object, after testing the spatial correlation, if there is spatial correlation, a spatial measurement model is needed. This paper uses the global Moran's I index for calculation. Based on the characteristics and research needs of the research object, this paper selects the spatial lag model to verify the existence of the spatial effect and factors affecting OFDI. Findings - Our results show that export scale, infrastructure, technology level, political stability, resource endowment, market size, distance and labor cost have a certain impact on Korea's OFDI, but at present the distance and market size factors are the most important influencing factors for South Korea's OFDI, The technical level and political stability have little effect on South Korea's OFDI, and are not main factors determining South Korea's OFDI. Originality/value - Through spatial measurement verification, it was found that the spatial effect has a significant impact on OFDI, along with more than 60 countries. On this basis, relevant suggestions are put forward, which have strong practical significance for South Korea's OFDI to achieve healthy and sustainable development.

• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.337-350
• /
• 2006

The characteristics of neighborhood correlation images for change detection were explored at different spatial resolution scales. Bi-temporal QuickBird datasets of Las Vegas, NV were used for the high spatial resolution image analysis, while bi-temporal Landsat $TM/ETM^{+}$ datasets of Suwon, South Korea were used for the mid spatial resolution analysis. The neighborhood correlation images consisting of three variables (correlation, slope, and intercept) were evaluated and compared between the two scales for change detection. The neighborhood correlation images created using the Landsat datasets resulted in somewhat different patterns from those using the QuickBird high spatial resolution imagery due to several reasons such as the impact of mixed pixels. Then, automated binary change detection was also performed using the single and multiple neighborhood correlation image variables for both spatial resolution image scales.

• ETRI Journal
• /
• v.30 no.6
• /
• pp.765-773
• /
• 2008

Spatial correlation is a result of insufficient antenna spacing among multiple antenna elements, while temporal correlation is caused by Doppler spread. This paper compares the effect of spatial and temporal correlation in order to investigate the performance of multiuser scheduling algorithms in multiple-input multiple-output (MIMO) broadcast channels. This comparison includes the effect on the ergodic capacity, on fairness among users, and on the sum-rate capacity of a multiuser scheduling algorithm utilizing statistical channel state information in spatio-temporally correlated MIMO broadcast channels. Numerical results demonstrate that temporal correlation is more meaningful than spatial correlation in view of the multiuser scheduling algorithm in MIMO broadcast channels. Indeed, the multiuser scheduling algorithm can reduce the effect of the Doppler spread if it exploits the information of temporal correlation appropriately. However, the effect of spatial correlation can be minimized if the antenna spacing is sufficient in rich scattering MIMO channels regardless of the multiuser scheduling algorithm used.

• Environmental and Resource Economics Review
• /
• v.33 no.1
• /
• pp.59-86
• /
• 2024

Recently, research on city gas demand is increasing by reflecting the characteristics of each region. The similarity of the social structure of the adjacent region and the density of the supply infrastructure induce spatial correlation with the clustering that has a microscopic relationship between regions. Accordingly, as a result of analyzing the spatial correlation after dividing the demand for city gas for civilian use into a total of 54 regions based on the jurisdiction of 34 city gas companies, it was confirmed that there was a positive spatial correlation from a global and local perspective. In this study, the demand for city gas for civilian use for 54 regions from January 2014 to December 2022 was composed of panel data, and the spatial panel regression analysis and the general panel regression analysis were compared, and it was found that the spatial error model (SEM) was the most suitable model. This presents policy and practical implications by confirming that the demand for city gas for civilian use in one region has a significant relationship with the adjacent region.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.553-558
• /
• 2002

Physical sensor model in pushbroom satellite images can be made from sensor modeling by rotation parameters and attitude parameters on the satellite track. These parameters are determined by the information obtained from GPS, INS, or star tracker. Provided from satellite image, an auxiliary data error is connected directly with an error of rotation parameters and attitude parameters. This paper analyzed how obtaining satellite images influenced errors of rotation parameters and attitude parameters. furthermore, for detailed analysis, this paper generated simulated satellite image, which was changed variously by rotation parameters and attitude parameters of satellite sensor model. Simulated satellite image is generated by using high-resolution digital aerial image and DEM (Digital Elevation Model) data. Moreover, this paper determined correlation of rotation parameter and attitude parameters through error analysis of simulated satellite image that was generated by various rotation parameters and attitude parameters.

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

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.4_1
• /
• pp.343-351
• /
• 2014

The correlation between regional Walkability Index and their physical socio-economic characteristics has evaluated by the spatial statistical analysis to understand the urban pedestrian environments, where has been emerging the significance, recently. Following to the study, the Walkability Indexes were calculated quantitatively from two administrative districts of Busan and measured Global Local spatial autocorrelation indices. Additionally, the Geographically Weighted Regression model was applied to define the correlation between Walkability Indexes and urban environmental variables. The spatial autocorrelation values and clusters on the Walkability Indexes were derived in statistically significant level. Furthermore, the Geographically Weighted Regression model has been derived more improved inference than the OLS regression model, so as the influence of local level pedestrian environment was identified. The results of this study suggest that the spatial statistical approach can be effective on quantitative assessing the pedestrian environment and navigating their associated factors.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.34 no.4
• /
• pp.69-76
• /
• 2018

The purpose of this study is to propose and verify a spatial network construction method that integrated roads and subway lines to improve the predictability of the urban spatial analysis model. The existing axial map for urban spatial analysis did not reflect the subway line that serves as an important moving space in modern cities. To improve this axial map, proposed a Ground-Underground Spatial Network by integrating the underground spatial network with the axial map. As a result of the integration analysis, the Ground-Underground Spatial Network(GUSN) were similar to the movement frequency. Correlation of GUSN was 0.723, which showed higher explanatory power than correlation coefficient of 0.575 in axial map. The result of this study is expected to be a theoretical basis for constructing spatial network in urban space analysis with subway.