• The Korean Journal of Ecology
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• v.19 no.5
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• pp.465-476
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• 1996

There are several environmental variables that may be influential to the spatial distribution of forest vegetation. To create a map of forest vegetation zone over southern part of Korean Peninsula, digital map layers were produced for each of environmental variables that include topography, geographic locations, and climate. In addition, an extensive set of field survey data was collected at relatively undisturbed forests and they were introduced into the GIS database with exact coordinates of survey sites. Preliminary statistical analysis on the survey data showed that the environmental variables were significantly different among the previously defined five forest vegetation zones. Classification of the six layers of digital map representing environmental variables was carried out by a supervised classifier using the training statistics from field survey data and by a clustering algorithm. Although the maps from two classifiers were somewhat different due to the classification procedure applied, they showed overall patterns of vertical and horizontal distribution of forest zones. considering the spatial contents of many ecological studies, GIS can be used as an important tool to manage and analyze spatial data. This study discusses more about the generation of digital map and the analysis procedure rather than the outcome map of forest vegetation zone.

• Journal of Korean Physical Therapy Science
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• v.29 no.3
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• pp.75-84
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• 2022

Background: This study was to investigate the effect of non-invasive transcranial direct current stimulation due to hemiplegic patients due to stroke on temporal and spatial gait ability. Design: Randomized sham controlled trial. Methods: For the study method, 42 patients with hemiplegia due to stroke were randomly assigned to 14 patients each, and the general walking group, tDCS walking group, and tDCS (sham) walking group were subjected to 5 times a week, 30 minutes a day, and 6 weeks. In the temporal gait variables of hemiplegic patients due to stroke, the effect of the gait time, gait cycle, single support, double support, swing phase, stance phase, gait speed, cadence were measured. In spatial variables, one step length and one step length were measured. Results: As a result of the study, the EG group significantly increased in the step time, gait velocity, and cadence of the paralysis side in the comparison of temporal walking variables between groups according to the application of tDCS of walking ability in hemiplegic patients due to stroke patients(p<.05). In the change in spatial walking variables between groups according to the application of tDCS, the step length and stride length of the EG group showed a significant increase. Both the comparison of temporal and spatial symmetry walking variables between groups according to tDCS application was not significant(p>.05) Conclusion: As a result, tDCS has an effective effect on the improvement of the gait ability of stroke patients. In particular, it is an effective method of physical therapy that can improve the cadence and speed of gait, which can be combined with the existing gait training to effectively increase the gait of hemiplegia due to stroke patients.

• Journal of Ecology and Environment
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• v.43 no.2
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• pp.191-201
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• 2019

Macroecologists and biogeographers continue to predict the distribution of species across space based on the relationship between biotic processes and environmental variables. This approach uses data related to, for example, species abundance or presence/absence, climate, geomorphology, and soils. Researchers have acknowledged in their statistical analyses the importance of accounting for the effects of spatial autocorrelation (SAC), which indicates a degree of dependence between pairs of nearby observations. It has been agreed that residual spatial autocorrelation (rSAC) can have a substantial impact on modeling processes and inferences. However, more attention should be paid to the sources of rSAC and the degree to which rSAC becomes problematic. Here, we review previous studies to identify diverse factors that potentially induce the presence of rSAC in macroecological and biogeographical models. Furthermore, an emphasis is put on the quantification of rSAC by seeking to unveil the magnitude to which the presence of SAC in model residuals becomes detrimental to the modeling process. It turned out that five categories of factors can drive the presence of SAC in model residuals: ecological data and processes, scale and distance, missing variables, sampling design, and assumptions and methodological approaches. Additionally, we noted that more explicit and elaborated discussion of rSAC should be presented in species distribution modeling. Future investigations involving the quantification of rSAC are recommended in order to understand when rSAC can have an adverse effect on the modeling process.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.891-905
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• 2011

Climate vulnerability index is usually defined as a function of the climate exposure, sensitivity, and adaptive capacity, which requires adequate selection of proxy variables of each variable. We selected and used 9 proxy variables related to climate exposure in the literature, and diagnosed the adequacy of them for application in Korean peninsula. The selected proxy variables are: four variables from temperature, three from precipitation, one from wind speed, and one from relative humidity. We collected climate data over both previous year (1981~2010) and future climate scenario (A1B scenario of IPCC SERES) for 2020, 2050, and 2100. We introduced the spatial and temporal diagnostic statistical parameters, and evaluated both spatial and time variabilities in the relative scale. Of 9 proxy variables, effective humidity indicated the most sensitive to climate change temporally with the biggest spatial variability, implying a good proxy variable in diagnostics of climate change vulnerability in Korea. The second most sensitive variable is the frequency of strong wind speed with a decreasing trend, suggesting that it should be used carefully or may not be of broad utility as a proxy variable in Korea. The A1B scenario of future climate in 2020, 2050 and 2100 matches well with the extension of linear trend of observed variables during 1981~2010, indicating that, except for strong wind speed, the selected proxy variables can be effectively used in calculating the vulnerability index for both past and future climate over Korea. Other local variabilities for the past and future climate in association with climate exposure variables are also discussed here.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.729-737
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• 2019

This study defined the animation space as the concept of map based on the space of Joseph Campbell's heroic narrative. The space in which the animated character exists is an image symbolic language that reflects the inner and outer growth stages of the hero. I focused on the spatiality that plays the role of the power to lead the narrative and examined the meaning as the mediator that leads the heroic narrative. First, 6 spatial variables were derived by observing 'Hero's Journey 'which is used as a basis of US commercial animation scenario. Second, spatial variables are defined as 64 zones and proposed as 'Spatial Map Model of the Hero's Journey' (SMMH). Third, the character space of was applied to 'SMMH', and the change of space utilization rate and spatiality and the narrative meaning were analyzed. This study extended the narrative of animation space which was not actively studied to map concept. It is possible to provide a different viewpoint in animation production and research.

• Spatial Information Research
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• v.18 no.5
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• pp.107-117
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• 2010

Taking the East-Hwasung area as the case, this study first builds gridded population data based on the municipal population survey raw data, and then measures, by way of GIS tools, the major urban spatial variables that are thought to influence the composition of the regional population. For the purpose of comparison, the urban models based on the Genetic Algorithm technique and the regression technique are constructed using the same input variables. The findings indicate that the GA output performed better in differentiating the effective variables among the pilot model variables, and predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression models. The study results indicate that GA technique could be a very useful and supplementary research tool in understanding the urban phenomena.

• Journal of Environmental Impact Assessment
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• v.23 no.1
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• pp.39-50
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• 2014

In recent years, the frequency and scale of the natural disasters are growing rapidly due to the global climate change. In case of the urban flooding, high-density of population and infrastructure has caused the more intensive damages. In this study, we analyzed the spatial characteristics of urban flooding damage factors using GWR(Geographically Weighted Regression) for effective disaster prevention and then, classified the causes of the flood damage by spatial characteristics. The damage factors applied consists of natural variables such as the poor drainage area, the distance from the river, elevation and slope, and anthropogenic variables such as the impervious surface area, urbanized area, and infrastructure area, which are selected by literature review. This study carried out the comparative analysis between OLS(Ordinary Least Square) and GWR model for identifying spatial non-stationarity and spatial autocorrelation, and in the results, GWR model has higher explanation power than OLS model. As a result, it appears that there are some differences between each of the flood damage areas depending on the variables. We conclude that the establishment of disaster prevention plan for urban flooding area should reflect the spatial characteristics of the damaged areas. This study provides an improved understandings of the causes of urban flood damages, which can be diverse according to their own spatial characteristics.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.487-496
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• 2020

This study introduces how GISs (Geographic Information Systems) are used to assess spatial disparities in urban green spaces in the Chicago. Green spaces provide us with a variety of benefits, namely environmental, economic, and physical benefits. This study seeks to explore socioeconomic relationships between green spaces and their surrounding communities and to evaluate spatial disparities from a variety of perspectives, such as health-related, socioeconomic, and physical environment factors. To achieve this goal, this study used spatial statistics, such as optimized hotspot analysis, network analysis, and space-time cluster analysis, which enable conclusions to be drawn from the geographic data. In particular, 12 variables within the three factors are used to assess spatial disparities in the benefits of the use of green spaces. Finally, the variables are standardized to rank the community areas and identify where the most vulnerable community areas or parks are. To evaluate the benefits given to the community areas, this study used the z- and composite scores, which are compared in the three different combinations. After identifying the most vulnerable community area, crime data is used to spatially understand when and where crimes occur near the parks selected. This work contributes to the work of urban planners who need to spatially evaluate community areas in considering the benefits of the uses of green spaces.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.669-683
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• 2006

Model-based methods are generally used for small area estimation. Recently Shin and Lee (2003) suggested a method which used spatial correlations between areas for data set including some auxiliary variables. However in case of absence of auxiliary variables, Direct estimator is used. Even though direct estimator is unbiased, the large variance of the estimator restricts the use for small area estimation. In this paper, we suggest new estimators which take into account spatial correlation when auxiliary variables are not available. We compared Direct estimator and the newly suggested estimators using MSE, MAE and MB.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.1
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• pp.89-96
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• 2020

Adverse impact of Particulate Matters(PM10, PM2.5; PMs) significantly affects daily lives. Major countermeasures for reducing concentration of PMs were focused on emission source without considering spatial difference of PMs concentration. Thus, this study analyzed spatial·temporal distribution of PMs with observation data as well as potential contributing factors on PMs concentration. The annual average concentration of PMs have been decreased while the particulate matter warnings and alerts were significantly increased in 2018. The average concentration of PMs in spring and winter was higher than the other seasons. Also, the spatial distribution of PMs were also showed seasonality while concentration of PMs were higher in Seoul-metropolitan areas in all seasons. Climate variables, emission source, spatial structure and potential PM sinks were selected major factors which could affects on ambient concentrations of PMs. This paper suggest that countermeasures for mitigating PM concentration should consider characteristics of area. Climatic variables(temperature, pressure, wind speed etc.) affects concentrations of PMs. The effects of spatial structure of cities(terrain, ventilation corridor) and biological sinks(green infrastructure, urban forests) on concentration of PMs should be analyzed in further studies. Also, seasonality of PMs concentration should be considered for establishing effective countermeasures to reduce ambient PMs concentration.