• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.347-351
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• 2015

Sea ice is a key parameter for understanding the climate change in cryosphere. In this study, we investigated the correlation with the factors that influenced change of the sea ice extent. We used the Sea Ice Concentration (SIC) from Ocean and Sea Ice Satellite Application Facility (OSI-SAF), and surface albedo provided by The Satellite Application Facility on Climate Monitoring (CM SAF). We converted the same temporal and spatial resolution of the data and detected the sea ice using SIC data. We performed the relationship analysis between SIC and sea ice albedo. As a result, we found they have a strong positive correlation. We performed the linear regression between SIC and sea ice albedo, and found they have high-level coefficient of determination. It shows using either SIC or sea ice albedo is possible to estimate the sea ice products.

• Spatial Information Research
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• v.21 no.5
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• pp.15-22
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• 2013

The purpose of this work is to investigate correlation between $CO_2$ concentration and NDVI (Normalized Difference Vegetation Index) in North East Asia. Geographically weighted regression techniques were used to evaluate the spatial relationships between GOSAT (Greenhouse Observing SATellite) $CO_2$ measurement and MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index. The results reveals that $CO_2$ concentration to be negatively associated with NDVI. The analysis of Global Morans' I index and Anselin Local Morasn's I showed spatial autocorrelation between the overall spatial pattern of $CO_2$ and NDVI. Ultimately, there were clustered patterns in both data sets. The results show that carbon dioxide concentration shows non-random distribution patterns in relation to NDVI clusters, which proves that intense development activities such as deforestation are influencing carbon dioxide emission across the area of analysis. However, as the concentration of carbon dioxide varies depending on a variety of factors such as artificial sources, plant respiration, and the absorption and discharge of the ocean, follow-up studies are required to evaluate the correlations among more related variables.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.61-69
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• 2014

In this study spatial analysis of PM10 was performed to Particulate Materials(PM) less than $10{\mu}m$ in diameter in Seoul city. Because PM10 are responsible for the increasing mortality rate of lung cancer and cardiovascular diseases, spatial distribution of PM10 are special interest in air pollution of Seoul. In this study, spatial analysis of Particulate Materials were monitored by monthly averaged PM10 concentration of 2010, 2011. The monthly spatial patterns of PM10 showed the west area of Seoul(Youngdungpo) higher PM10 concentration than northern part of Seoul in early spring and winter seasons. In the comparison of PM10 concentration distribution patterns in 2010 and 2011, the PM10 concentration of 2011 at Gangnam and Songpa-gu were more increased than yearly averaged patterns of 2010. The distribution patterns of PM10 in Seoul city showed the high concentration PM10 of several areas with Youngdungpo-gu, Gangnam-gu and Cheongnyangni. Therefore we need to establish PM10 management strategy for these area.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.64-77
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• 2021

The purpose of this study is to examine the spatial distribution of highly educated human capital and to identify key factors affecting their spatial distribution. We analyzed the spatial concentration and inequality using Gini's coefficient and exploratory spatial data analysis and identified the economic and amenity factors to affect the spatial concentration of highly educated human capital using spatial regression model. The findings show that the spatial pattern of highly educated human capital is concentrated, imbalanced, and clustered in Capital region and part of Chungcheong and Gangwon region. The spatial concentration were more affected by economic factor than by amenity factors. This study provides some implication on the regional economic strategies to attract the human capital.

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.253-260
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• 2005

The aim of this study is to analyze the temporal and spatial distribution aspects of red tide using GIS techniques. The damage caused by red tide appears various aspects according to the species, concentration and spatial distribution of red tide plankton. Therefore, in order to prevent the damage of red tide it is important to understand the distribution characteristics of red tide by each species according to time and space. In this perspective, we analyzed the beginning outbreak area, spatial occurrence frequency and spatial migration of red tide. The spatial data used by this study was constructed by digitizing the red tide quick report and coupled with various attributes such as species, concentration and water temperature for construction of red tide database. We used various spatial analysis methods such as union, intersect, tracking, buffer and spatial interpolation for analyzing temporal and spatial characteristics of red tide. From the result of these spatial analyses, we could get the spatial information on the temporal and spatial distribution characteristics of red tide at the Southern Sea.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.333-338
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• 2017

We compared sea ice concentration(SIC) and sea ice extent(SIE) using EUMETSAT Ocean and Sea Ice Satellite Application Facilities(OSI SAF) and NASA Team(NT) sea ice algorithm in the Arctic during 1980-2010 to investigate the difference between sea ice data applied different algorithms. SIC and SIE of the two data showed different consistency by season and by sea area. Seasonally, SIC of OSI SAF was 0.85 % overall, 0.48 % in spring, 0.97 % in summer, 1.38 % in autumn and 0.66 % in winter higher than NT SIC. By sea area, OSI SAF SIC was 2.7 %, SIE was $198,000km^2$ higher than NT in Arctic Ocean, but in Lincoln Sea, OSI SAF SIC was 2.3 %, SIE was $20,000km^2$ lower than NT.

• Korean Institute of Interior Design Journal
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• v.23 no.2
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• pp.90-98
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• 2014

This study has analyzed the meaning of observation time in the course of acquiring the information of subjects who observed the space of hall in subway stations to figure out the process of spatial information excluded and the features of intensive searching. The followings are the results from the analysis of searching process with the interpretation of the process for information acquirement through the interpretation of observation area and time. First, based on the general definition of observation time, the reason for analyzing the features of acquiring spatial information according to the subjects' observation time has been established. The feature of decreased analysis data reflected that of observation time in the process of perceiving and recognizing spatial information, which showed that the observation was focused on the enter of the space during the time spent in the process of observing the space and the spent time with considerable exclusion of bottom end (in particular, right bottom end). Second, while the subjects were observing the space of hall in subway stations, they focused on the top of the left center and the signs on the right exit the most, which was followed by the focus on the both side horizontally and the clock on the top. Third, the analysis of consecutive observation frequency enabled the comparison of the changes to the observation concentration by area. The difference of time by area produced the data with which the change to the contents of spatial searching in the process of searching space could be known. Fourth, as the observation frequency in the area of I changed [three times -> six times -> 9 times], the observation time included in the area increased, which showed the process for the change from perception to recognition of information with the concentration of attention through visual information. It makes it possible to understand that more time was spent on the information to be acquired with the exclusion of the unnecessary information around.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.573-586
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• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• Korean Institute of Interior Design Journal
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• v.25 no.4
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• pp.80-89
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• 2016

The analysis of spatial search direction and its concentration through eye movement can produce some useful data in that it enables to know the features of space elements and their effects on one another. The results by analysing the search features and concentration of spatial sections through the eye-tracking in shops in a department store makes it possible to define the followings. First, the features of 'eye's in & out' could be estimated through the division of sections by the characteristics of those shops and the extraction of central point based on the decision of continuative observation. The decision of continuative observations enabled to analyse the frequency of observation data which can be considered to be 'things watched longtime' and the stared points that is equivalent to 'things seen very often', by which the searching characteristics of spatial sections could be estimated. Second, as with the eye's [in], the right shops had 0.6 times more (3.5%) than those left and as with the eye's [out] the left ones had 0.6 times more (3.5%). It indicates that [in, out] of the right and the left shops had the same difference, which lets us know that with starting point of the middle space, [in] and [out] were paid more attention to the right shops and the left shops respectively. Third, as with the searching directions by section, the searching times [2.9 times] from [B] to [A] were than that [2.6 times] from [A] to [B]. It was also found that the left shops had more searching direction toward [C, D] than the right ones and that those searching activities at the left shops were more active. Fourth, when the searching directions by section are reviewed, the frequency of searching from [B] to [A] was 2.9 and that of the other way 2.6. Also the left shops were found to have more searching direction toward [C, D] than the right ones and those searching activities at the left shops were estimated to be more active.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.1
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• pp.11-23
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• 2000

Photochemical air quality models are essential tools in predicting future air quality and assessing air pollution control strategies. To evaluate air quality using a photochemical air quality model, emission inventories are important inputs to these models. Since most emission inventories are provided at a county-level, these emission inventories need to be geographically allocated to the computational grid cells of the model prior to running the model. The conventional method for the spatial allocation of these emissions uses "spatial surrogate indicators", such as population for mobile source emissions and county area for biogenic source emissions. In order to examine the applicability of such approximations, more detailed spatial surrogate indicators were developed using Geographic Information System(GIS) tools to improve the spatial allocation of mobile and boigenic source emissions, The proposed spatial surrogate indicators appear to be more appropriate than conventional spatial surrogate indicators in allocating mobile and biogenic source emissions. However, they did not provide a substantial improvement in predicting ground-level ozone(O3) concentrations. As for the carbon monoxide(CO) concentration predictions, certain differences between the conventional and new spatial allocation methods were found, yet a detailed model performance evaluation was prevented due to a lack of sufficient observed data. The use of the developed spatial surrogate indicators led to higher O3 and CO concentration estimates in the biogenic source emission allocation than in the mobile source emission allocation.llocation.