• Journal of the Korean Geographical Society
• /
• v.52 no.1
• /
• pp.123-141
• /
• 2017

This study predicted urban green spaces for 2020 based on two scenarios keeping or freeing the green-belt in the Daegu metropolitan city using a hybrid Cellular Automata(CA)-Markov model and analyzed the spatial dynamics of urban green spaces between 2009 and 2020 using a land cover change detection technique and spatial metrics. Markov chain analysis was employed to derive the transition probability for projecting land cover change into the future for 2020 based on two land cover maps in 1998 and 2009 provided by the Ministry of Environment. Multi-criteria evaluation(MCE) was adopted to develop seven suitability maps which were empirically derived in relation to the six restriction factors underlying the land cover change between the years 1998 and 2009. A hybrid CA-Markov model was then implemented to predict the land cover change over an 11 year period to 2020 based on two scenarios keeping or freeing the green-belt. The projected land cover for 2009 was cross-validated with the actual land cover in 2009 using Kappa statistics. Results show that urban green spaces will be remarkably fragmented in the suburban areas such as Dalseong-gun, Seongseo, Ansim and Chilgok in the year 2020 if the Daegu metropolitan city keeps its urbanization at current pace and in case of keeping the green-belt. In case of freeing the green-belt, urban green spaces will be fragmented on the fringes of the green-belt. It is thus required to monitor urban green spaces systematically considering the spatial change patterns identified by this study for sustainably managing them in the Daegu metropolitan city in the near future.

• 재활복지
• /
• v.17 no.4
• /
• pp.311-338
• /
• 2013

The Act of Disability Discrimination and Rights Restriction was enacted in 2008 states that all services including cultural and artistic activities should be fair and easily accessible for disabled. The aim of this study was to determine how to improve the facilities and services for the disabled in exhibition facilities and provide complementary guidelines for amenities designed for people with disabled. The conclusions identified through this study are as follows: 1) It should be installed pedestrian safe passage so that visitors could access from the entrance of the ground to inside of the exhibition facility. 2) The floor of the exhibition hall should be installed not slip, and dangerous obstacles placed in the corridor should be removed. 3) It Should be considered that visually impaired and wheelchair users are use different facilities at toilet and elevator. 4) To guide the visually impaired and hearing impaired it should be prepared palpable map, braille signage, miniature and brochures. 5) Visually impaired and hearing impaired can experience the exhibits by using a variety of senses, therefore it may need to provide assistive devices and human services.

• Journal of the Korean association of regional geographers
• /
• v.3 no.1
• /
• pp.63-80
• /
• 1997

This study attempts to establish the criteria of site selection for establishing solid waste disposal facility, to determine optimal solid waste disposal sites with the criteria, and to examine the suitability of the selected sites. The Multi-Criteria Evaluation(MCE) module in Idrisi is used to determine optimal sites for solid waste disposal. The MCE combines the information from several criteria in interval and/or ratio scale to form a single index of evaluation without leveling down the data scale into ordinal scale. The summary of this study is as follows: First, the considerable criteria are selected through reviewing the literature and the availability of data: namely, percent of slope, fault lines, bedrock characteristics, major residential areas, reservoirs of water supply, rivers, inundated area, roads, and tourist resorts. Second, the criteria maps of nine factors have been developed. Each factor map is standardized and multiplies by its weight, and then the results are summed. After all of the factors have been incorporated, the resulting suitability map is multiplied by each of the constraint in turn to "zero out" unsuitable area. The unsuitable areas are discovered in urban district and its adjacencies, and mountain region as well as river, roads, resort area and their adjacency districts. Third, the potential sites for establishing waste disposal facilities are twenty five districts in Youngyang-gun. Five districts are located in Subi-myun Sinam-ri, nine districts in Chunggi-myun Haehwa-ri and Moojin-ri, and eleven districts in Sukbo-myun Posan-ri. The first highest score of suitability for waste disposal sites is shown at number eleven district in Chunggi-myun Moojin-ri and the second highest one is discovered at number twenty one district in Sukbo-myun Posan-ri that is followed by number nine district in Chunggi-myun Haehwa-ri, number seventeen and twenty three in Sukbo-myun Posan-ri, and number two in Subi-myun Sinam-ri. The first lowest score is found in number six district in Chunggi-myun Haehwa-ri, and the second lowest one is number five district in Subi-myun Sinam-ri. Finally, the Geographic Information System (GIS) helps to select optimal sites with more objectively and to minimize conflict in the determination of waste disposal sites. It is important to present several potential sites with objective criteria for establishing waste disposal facilities and to discover characteristics of each potential site as a result of that final sites of waste disposal are determined through considering thought of residents. This study has a limitation of criteria as a result of the restriction of availability of data such as underground water, soil texture and mineralogy, and thought of residents. To improve selection of optimal sites for a waste disposal facility, more wide rage of spatial and non-spatial data base should be constructed.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.4
• /
• pp.99-112
• /
• 2021

This study was performed to predict spatial change of future forestland area in South Korea at regional level for supporting forest-related plans established by local governments. In the study, land use was classified to three types which are forestland, agricultural land, and urban and other lands. A logistic regression model was developed using transitional interaction between each land use type and topographical factors, land use restriction factors, socioeconomic indices, and development infrastructures. In this model, change probability from a target land use type to other land use types was estimated using raster dataset(30m×30m) for each variable. With priority order map based on the probability of land use change, the total annual amount of land use change was allocated to the cells in the order of the highest transition potential for the spatial analysis. In results, it was found that slope degree and slope standard value by the local government were the main factors affecting the probability of change from forestland to urban and other land. Also, forestland was more likely to change to urban and other land in the conditions of a more gentle slope, lower slope criterion allowed to developed, and higher land price and population density. Consequently, it was predicted that forestland area would decrease by 2027 due to the change from forestland to urban and others, especially in metropolitan and major cities, and that forestland area would increase between 2028 and 2050 in the most local provincial cities except Seoul, Gyeonggi-do, and Jeju Island due to locality extinction with decline in population. Thus, local government is required to set an adequate forestland use criterion for balanced development, reasonable use and conservation, and to establish the regional forest strategies and policies considering the future land use change trends.

• The Korean Journal of Air & Space Law and Policy
• /
• v.20 no.1
• /
• pp.87-107
• /
• 2005

Land Remote Sensing' is defined as the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. Narrowly speaking, this is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information. Remote sensing technology was initially developed with certain purposes in mind ie. military and environmental observation. However, after 1970s, as these high-technologies were taught to private industries, remote sensing began to be more commercialized. Recently, we are witnessing a 0.61-meter high-resolution satellite image on a free market. While privatization of land remote sensing has enabled one to use this information for disaster prevention, map creation, resource exploration and more, it can also create serious threat to a sensed nation's national security, if such high resolution images fall into a hostile group ie. terrorists. The United States, a leading nation for land remote sensing technology, has been preparing and developing legislative control measures against the remote sensing industry, and has successfully created various policies to do so. Through the National Oceanic and Atmospheric Administration's authority under the Land Remote Sensing Policy Act, the US can restrict sensing and recording of resolution of 0.5 meter or better, and prohibit distributing/circulating any images for the first 24 hours. In 1994, Presidential Decision Directive 23 ordered a 'Shutter Control' policy that details heightened level of restriction from sensing to commercializing such sensitive data. The Directive 23 was even more strengthened in 2003 when the Congress passed US Commercial Remote Sensing Policy. These policies allow Secretary of Defense and Secretary of State to set up guidelines in authorizing land remote sensing, and to limit sensing and distributing satellite images in the name of the national security - US government can use the civilian remote sensing systems when needed for the national security purpose. The fact that the world's leading aerospace technology country acknowledged the magnitude of land remote sensing in the context of national security, and it has made and is making much effort to create necessary legislative measures to control the powerful technology gives much suggestions to our divided Korean peninsula. We, too, must continue working on the Korea National Space Development Act and laws to develop the necessary policies to ensure not only the development of space industry, but also to ensure the national security.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.57-73
• /
• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.