• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.3-13
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• 1999

In this study, an alternative spatial analysis method against conventional methods such as Thiessen method, Inverse Distance method, and Kriging method, named Spatial-Analysis Neural-Network (SANN) is presented. It is based on neural network modeling and provides a nonparametric mean estimator and also estimators of high order statistics such as standard deviation and skewness. In addition, it provides a decision-making tool including an estimator of posterior probability that a spatial variable at a given point will belong to various classes representing the severity of the problem of interest and a Bayesian classifier to define the boundaries of subregions belonging to the classes. In this paper, the SANN is implemented to be used for analyzing a mean annual precipitation filed and classifying the field into dry, normal, and wet subregions. For an example, the whole area of South Korea with 39 precipitation sites is applied. Then, several useful results related with the spatial variability of mean annual precipitation on South Korea were obtained such as interpolated field, standard deviation field, and probability maps. In addition, the whole South Korea was classified with dry, normal, and wet regions.

• Journal of Distribution Science
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• v.16 no.4
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• pp.83-88
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• 2018

Purpose - This study conducts an empirical analysis on gasoline pricing of Korean retail gas stations focusing on spatial effects. Unlike previous studies, the study uses an official land price for a proxy of the importance of location, and also allows the spatial effects from other competing gas stations as well. Research design, data, and methodology - In collection of data, we obtain more abundant data than those of previous studies. The gasoline prices used in the study are 909,084 observations as daily data from January 1 to July 31 of the year 2016. A proxy for the land price is collected by linking official public land price data with address information on each gas station. For the estimation, the study employs the Panel Spatial Dubin Model to make the best use of the collected location information. Results - As expected, spatial properties of gas stations have significant effects on the gasoline price. As the price per square meter increases by 100 thousands won, the price of gasoline rises 9 won per liter. Among other characteristics, the price increases by 16 won per liter if the station has a convenience store, and about 5 won if it has a car wash service. Gasoline price in Singapore accounted for 26% of variations in domestic gasoline prices. SK Energy and GS Caltex are the top brands in terms of price. The study also finds prices and other important properties of competing gas stations have significant effects on others' prices. Prices of competing gas station have a positive relationship with those of others. If a competing gas station raises the price, the gas station also raises the price, and lowering the price lower the price. Among brands, GS Caltex has the greatest downward pressure on nearby gas stations. Conclusions - The study confirms that location value of gas stations affect their gasoline prices, and the prices of the competing gas stations also have a significant effects on their prices. It suggests that the prices in the competing retail areas tend to be synchronized with each other.

• Journal of Environmental Science International
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• v.24 no.3
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• pp.303-316
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• 2015

The relationship between urban spatial structures and GHG-AP integrated emissions was investigated by statistically analyzing those from 25 administrative districts of Seoul. Urban spatial structures, of which data were obtained from Seoul statistics yearbook, were classified into five categories of city development, residence, environment, traffic and economy. They were further classified into 10 components of local area, population, number of households, residential area, forest area, park area, registered vehicles, road area, number of businesses and total local taxes. GHG-AP integrated emissions were estimated based on IPCC(intergovernmental panel on climate change) 2006 guidelines, guideline for government greenhouse inventories, EPA AP-42(compilation of air pollutant emission factors) and preliminary studies. The result of statistical analysis indicated that GHG-AP integrated emissions were significantly correlated with urban spatial structures. The correlation analysis results showed that registered vehicles for GHG (r=0.803, p<0.01), forest area for AP (r=0.996, p<0.01), and park area for AP (r=0.889, p<0.01) were highly significant. From the factor analysis, three groups such as city and traffic categories, economy category and environment category were identified to be the governing factors controlling GHG-AP emissions. The multiple regression analysis also represented that the most influencing factors on GHG-AP emissions were categories of traffic and environment. 25 administrative districts of Seoul were clustered into six groups, of which each has similar characteristics of urban spatial structures and GHG-AP integrated emissions.

• Spatial Information Research
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• v.18 no.4
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• pp.141-149
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• 2010

One of recent trends in Web-based GIS is the system development using FOSS (Free and Open Source Software). Open Source software is independent from the technologies of commercial software and can increase the reusability and extensibility of existing systems. In this study, we developed a Web-based GIS for interactive visualization of geographic information using Google Earth and spatial DBMS(database management system). Google Earth Plug-in and Google Earth API(application programming interface) were used to embed a geo-browser in the Web browser. In order to integrate the Google Earth with a spatial DBMS, we implemented a KML(Keyhole Markup Language) generator for transmitting server-side data according to user's query and converting the data to a variety of KML for geovisualization on the Web. Our prototype system was tested using time-series of LAI(leaf area index), forest map, and crop yield statistics. The demonstration included the geovisualization of raster and vector data in the form of an animated map and a 3-D choropleth map. We anticipate our KML generator and system framework will be extended to a more comprehensive geospatial analysis system on the Web.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.41-53
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• 2020

The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the near- and off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure,sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.

• Korean Journal of Veterinary Research
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• v.53 no.1
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• pp.49-54
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• 2013

To investigate the transmission pattern of geographical area and temporal trends of the 2010~2011 foot-and-mouth disease (FMD) outbreaks in Korea, and to explore temporal intervals at which spatial clustering of FMD cases space-time analysis based on georeferenced database of 3,575 burial sites, from 30 November 2010 to 23 February 2011, was performed. The cases represent approximately 98.1% of all infected farms (n = 3,644) during the same period. Descriptive maps of spatial patterns of the outbreaks were generated by ArcGIS. Spatial Scan Statistics, using SaTScan software, was applied to investigate geographical clusters of FMD cases across the country. Overall, spatial heterogeneity was identified, and the transmission pattern was different by province. Cattle have more clusters in number but smaller in size, as compared to the swine population. In addition, spatiotemporal analysis and the comparison of clustering patterns between the first 7 days and days 8 to 14 of the outbreak revealed that the strongest spatial clustering was identified at the 7-day interval, although clustering over longer intervals (8~14 days) was also observed. We further discussed the importance of time period elapsed between FMD-suspected notice and the date of confirmation, and emphasized the necessity of region-specific and species-specific control measures.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.699-709
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• 2011

The uses of multi-dimensional hydrodynamic and water quality models are increasing to support a sustainable management of large dam reservoirs in Korea. Any modeling study requires selection of a proper spatial dimension of the model based on the characteristics of spatial variability of concerned simulation variables. For example, a laterally averaged two-dimensional (2D) model, which has been widely used in many large dam reservoirs in Korea, assumes that the lateral variations of hydrodynamic and water quality variables are negligible. However, there has been limited studies to give a justification of the assumption. The objectives of this study were to present the characteristics of spatial variations of water quality variables through intensive field monitoring in Daechung Reservoir, and provide information on a proper spatial dimension for different water quality parameters. The monitoring results showed that the lateral variations of water temperature are marginal, but those of DO, pH, and conductivity could be significant depending on the hydrological conditions and local algal biomass. In particular, the phytoplankton (Chl-a) and nutrient concentrations showed a significant lateral variation at R2 (Daejeongri) during low flow periods in 2008 possibly because of slow lateral mixing of tributary inflow from So-oak Stream and wind driven patchiness.

• Monthly Notices of the Royal Astronomical Society
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• v.483 no.3
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• pp.3950-3970
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• 2019

We investigate the spatial distribution of Ly α-emitting galaxies (LAEs) at z ≈ 2.67, selected from the NOAO Deep Wide-Field Survey, using two-point statistics and topological diagnostics adopted from network science. We measure the clustering length, r₀ ≈ 4 h^-1 Mpc, and the bias, b_LAE = 2.2^+0.2_-0.1. Fitting the clustering with halo occupation distribution (HOD) models results in two disparate possibilities: (1) where the fraction of central galaxies is <1 per cent in haloes of mass >10¹² M_⊙ and (2) where the fraction is ≈20 per cent. We refer to these two scenarios as the 'Dusty Core Scenario' for Model#1, since most of the central galaxies in massive haloes are dead in Ly α emission, and the 'Pristine Core Scenario' for Model#2, since the central galaxies are bright in Ly α emission. Traditional two-point statistics cannot distinguish between these disparate models given the current data sets. To overcome this degeneracy, we generate mock catalogues for each HOD model using a high-resolution N-body simulation and adopt a network statistics approach, which provides excellent topological diagnostics for galaxy point distributions. We find three topological anomalies from the spatial distribution of observed LAEs, which are not reproduced by the HOD mocks. We find that Model#2 matches better all network statistics than Model#1, suggesting that the central galaxies in >10¹² h^-1 M_⊙ haloes at z ≈ 2.67 need to be less dusty to be bright as LAEs, potentially implying some replenishing channels of pristine gas such as the cold mode accretion.

• International Commerce and Information Review
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• v.18 no.3
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• pp.47-72
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• 2016

The Government of Korea has always focused on developing and maintaining a surplus on the balance of payments as a successful trade policy. The focus should now be on spatial information hiding, revealing patterns in trade activities that enable viewing trade in a more sophisticated manner. This study utilizes trade statistical data such as the United States-South Korea imports and exports from 2003 to 2015 officially released by the two countries. It allows us to analyze and extract the spatial information pertaining to the origin, transit, and destination. First, in the case of export data to the United States, the origin of the trade goods has expanded and decentralized from the metropolitan area. With regard to transit, in 2003, most of the exported goods were shipped by ocean vessels and arrived at the ports on the western coast of the United States. However, trade patterns have changed over the 12-year period and now more of that trade has moved to the southern ports of the United States. In terms of destination, California and Texas were importing goods from South Korea. With the development of the automotive industry in Georgia and Alabama, these two states also imported huge volumes of automobile parts. Second, in case of import data, most imported goods from the United States originated from California and Texas. In this case, 40% of goods were shipped by air freight and arrived at the Incheon-Seoul International Airport; most ocean freight was handled at the Port of Busan. The purpose of this study is to decompose the spatial information from the trade statistics data between Korea and the United States and to depict visualized bilateral trade structure by origin, transit, and destination.