• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.403-419
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• 2017

This study analyzed the forces to drive the change of urban green spaces in Daegu from 1989 to 2009. First, the loss and fragmentation of green spaces in the past 20 years were spatially identified by performing the hot spots analysis for the cell-based spatial metrics quantifying the size and diversity of green spaces. Next, seven drivers such as slope, distance to roads, land price, population density, ratios of residential, commercial, and industrial areas were selected based on the previous studies and the direction of the association between the loss and fragmentation of green spaces and seven drivers was analyzed with the stepwise logistic regression. Finally, the relative importance of the seven drivers and their interactions in the past 20 years were analyzed with the geographical detector. The results show that the loss of green spaces was concentrated on a part of the Anshim housing development district from 1989 to 2009 and green spaces were highly fragmented around the housing development districts such as Seongseo, Anshim, Dalseong-gun and Chilgok. The forces to drive the loss and fragmentation of green spaces in these areas were different at the administrative levels, but the drivers such as slope and ratios of residential and industrial areas were commonly significant. These drivers were positively correlated with largest patch index(LPI) quantifying the loss of green spaces while they were negatively correlated with Shannon's diversity index(SHDI) measuring the fragmentation of green spaces. In other words, the loss and fragmentation of urban green spaces in Daegu appeared around such regions with lower slope and lower ratios of residential and industrial areas. The relative importance of drivers for LPI was listed as ratio of industrial area, land price, and ratio of commercial area in descending order whereas that of drivers for SHDI was listed as ratio of industrial area, land price, and distance to roads in descending order. Also, the interaction between slope and ratio of residential area had a great impact on LPI and SHDI. The ratio of industrial area was a single driver to most significantly explain the loss and fragmentation of urban green spaces in Daegu in the past 20 years. The interaction between slope and ratio of residential area was greater than the independent influence of a single driver. This study will provide the base data to build a sustainable urban green policy for the city of Daegu in the near future.

• Journal of Dental Rehabilitation and Applied Science
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• v.36 no.2
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• pp.88-94
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• 2020

Purpose: The purpose of this study was to analyze the sagittal root position of maxillary anterior teeth and report the frequency of each classification in Korean for immediate implant placement. Materials and Methods: A retrospective review of cone-beam computed tomography (cone-beam CT) images was conducted on 120 patients (60 male and 60 female) who fulfilled the inclusion criteria. After reorientation of the axis, cone-beam CT images were evaluated and the relationship of the sagittal root position (SRP) of the maxillary anterior teeth to its associated osseous housing was recorded. Class I, II, and III were classified respectively when the root was positioned on the labial, central, and palatal aspect of the alveolar bone. Class IV was the position that at least two thirds of the root is engaging both the labial and palatal cortical plates. Then, the angulation of the root axis and the alveolar bone axis was measured. Descriptive statistics and Kruskal-Wallis test were used to compare the angulation according to the root position and SRP class. Results: The frequency distribution of sagittal root position of maxillary anterior teeth indicated that 81.1%, 10.3%, 1.9%, and 6.7% were classified as Class I, II, III, and IV, respectively. The sagittal angulation at approximately 77.5% of central incisor, lateral incisor, and canine was < 20 degrees, but the angle at more than 42.7% of canine was ≥ 20 degrees. Within the class, the angulation was statistically significantly greater in Class I (16.19) compared to Class II (8.72) and Class III (9.93), and smaller in Class IV (3.79). Conclusion: Within the limitation of this study, a majority of the maxillary anterior roots were positioned close to the buccal cortical plate. However, some roots have very thin alveolar bone and sagittal angulation larger than 30 degrees. Therefore, cone-beam CT analyses of the sagittal root position and the sagittal angulation are recommended for the selection of the appropriate dental implant treatment approach.

• Journal of environmental and Sanitary engineering
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• v.18 no.1
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• pp.51-57
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• 2003

Indoor air quality(IAQ) in workplace and residential environments has been concern of people. Recently, Ministry of Environment in Korea has recognized the potential risk on the healthy effect related to indoor air pollution at home. Therefore, the purpose of this study was performed to measure the indoor air pollutants of IAQ at different homes and investigate to compare the perception of IAQ recognition at home from questionnaire survey in Seoul. We estimated the IAQ of selected 6 homes based on site region and housing type. The indoor air pollutants and parameters such as temperature, relative humidity, respirable suspended particulate matter($PM_{10}$), formaldehyde(HCHO), total bacteria counts, carbon monooxide(CO) and carbon dioxide($CO_2$) were monitored for summer and winter. In monitoring results, the respirable suspended particulate matter(($PM_{10}$) and indoor airborne bacteria level of home 5 and 6 were higher than the standard of the public $150{\;}{\mu}g/m^3$ and $500{\;}{\mu}g/m^3$, the level formaldehyde(HCHO) was exceed 0.1 ppm of the standard of Korea at all monitored homes. In statistics analysis, we could find a correlation between the building age and the concentration of CO, TBC were significant at 0.01 level and Relative Humidity was significant at 0.05 level for summer. Finally, the important air pollutants of IAQ in home were HCHO and total bacteria counts(TBC). And we performed a questionnaire survey of 500 people about their awareness for the importance of IAQ in our home during same period. In results, all most response of occupant has recognized the importance of IAQ at home. Therefore, it can be concluded that the IAQ of selected 6 home studied was perceived as acceptable, it is recommended that the government related IAQ was suggested the guideline and control of IAQ problems, and the occupants need to be effort to reduce the exposure of sources to undesirable pollutants.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.148-160
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• 2012

The main purpose of this paper is to deliver a climate analysis and evaluation method based on GIS by using airborne LiDAR data and Biotop type map and to provide spatial information of climate analysis and evaluation based on Biotop type Map. At first stage, the area, slope, slope length, surface, wind corridor function and width, and obstacle factors were analyzed to obtain cold/fresh air production and wind corridor evaluation. In addition, climate evaluation was derived from those two results in the second stage. Airborne LiDAR data are useful in wind corridor analysis during the study. Correlation analysis results show that ColdAir_GRD grade was highly correlated with Surface_GRD (-0.967461139) and WindCorridor_ GRD was highly correlated with Function_GRD (-0.883883476) and Obstacle_GRD (-0.834057656). Climate Evaluation GRID was highly correlated with WindCorridor_GRD (0.927554516) than ColdAir_GRD (0.855051646). Visual validations of climate analysis and evaluation results were performed by using aerial ortho-photo image, which shows that the climate evaluation results were well related with in-situ condition. At the end, we applied climate analysis and evaluation by using Biotop map and airborne LiDAR data in Gwangmyung-Shiheung City, candidate for the Bogeumjari Housing District. The results show that the aerial percentile of the 1st Grade is 18.5%, 2nd Grade is 18.2%, 3rd Grade is 30.7%, 4th Grade is 25.2%, and 5th Grade is 7.4%. This study process provided both the spatial analysis and evaluation of climate information and statistics on behalf of each Biotop type.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.