This study reviews theoretical considerations and past studies about real estate prices, macroeconomic variables, and real estate policies. Monthly data from January 2003 to June 2021 are used, and a VEC model, the most widely used multivariate time series analysis method, is employed for analysis. Through the model, the effects of macroeconomic variables and real estate regulatory policies on real estate prices in Seoul are analyzed. Findings are summarized as follows. First, macroeconomic variables such as money supply and interest rates do not have a significant impact on Seoul's apartment prices. Due to the high demand for housing and insufficient supply, there is a demand for buying a home regardless of macroeconomic booms or recessions. Second, tax and financial regulatory policies have an initial impact on the rise in apartment prices in Seoul, and their influence diminishes over time. Third, anti-speculation zones are expected to decrease apartment prices through the suppression of demand. However, these zones cause a rise in apartment prices. This could be understood as a lock-in effect due to the strengthening of capital gains tax. Fourth, the price ceiling did not decrease apartment prices. These findings propose that, in Seoul, where demand is high and supply is insufficient, the supply of high-quality and sufficient housing should be prioritized over various regulations such as tax regulations, financial regulations, anti-speculation zones, and price caps. Moreover, the findings provide an implication that city-specific real estate policies should be implemented for Seoul rather than regulation-oriented approaches in public policy.
Journal of the Korean Association of Geographic Information Studies
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v.25
no.1
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pp.77-100
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2022
In the era of low growth, many provincial cities are experiencing population decline and aging. Population decline phenomena such as reduction of productive manpower, reduction of finances, deterioration of quality of life, and collapse of the community base are occurring in a chain and are being pushed to the brink of extinction of the cities. This study aims to propose a methodology to objectively estimate the employment creation centers and setting the basic unit of industrial-centered zoning by applying spatial statistical techniques and GIS for the application of the compact city plan as an efficient spatial management policy in a city with a declining population. In details, based on reviewing previous studies on compact city, 'employment complex index(ECI)' were defined considering the number of workers, the number of settlers, and the area of development land, the employment creation center was estimated by applying the 'Local Moran's I' and 'Getis-Ord's Hot-Spot Analysis'. As a case study, changes in the four years of 2013, 2015, 2017, and 2019 were compared and analyzed for Changwon City. As a result, it was confirmed that the employment creation center is becoming compacted and polycentric, which is a significant result that reflects the actual situation well. This results provide the basic data for functional and institutional territorial governance for the regional revitalization platform, and provide meaningful information necessary for spatial policy decision-making, such as population reduction, regional gross domestic product, and public facility arrangement that can respond to energy savings, transportation plans, and medical and health plans.
The purpose of this study is to verify that several theories explaining the determinants of welfare expenditure is applied to the family policy expenditure and to find out if there' re unique determinants of the family policy expenditure. We analyzed the data (OECD 14 countries for 1980~2005) by pooled time series analysis. As for industrialization theory, female labor force participation rate has positive effect on family policy expenditure while population under 15-year children has negative effect, which refers to the demand of family policies is that of female workers, not children's. Power resource theory is applied to the determinants of family policy expenditure as those of welfare expenditure. Women's political & economic empowerment has partly positive effects on family policy expenditure, which is the evidence of the effectiveness of feminist theory. In the institutional theory, we verified the effect of policy legacy but couldn't find out the crowding-out effect. The theoretical implication of this study is the empirical verification of the theories explaining the determinants of welfare expenditure being applied to the family policy expenditure. We also suggested the political and institutional foundation to effectively respond to the new social risks in spite of budget constraints, which can be a policy implication.
In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.
Park, Do-Hyung;Chung, Jaekwon;Chung, Yeo Jin;Lee, Dongwon
Journal of Intelligence and Information Systems
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v.20
no.4
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pp.1-23
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2014
Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.
Journal of the Korean Institute of Landscape Architecture
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v.46
no.2
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pp.27-36
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2018
This study suggests the necessity of behavior analysis as changes to a park environment to reflect user desires can be implemented only by grasping the needs of park users. Online data (blog) were defined as the basic data of the study. After collecting data by 5 - year units, data mining was used to derive the characteristics of the time series behavior while the significance of the online data was verified through social network analysis. The results of the text mining analysis are as follows. First, primary results included 'walking', 'photography', 'riding bicycles'(inline, kickboard, etc.), and 'eating'. Second, in the early days of the collected data, active physical activity such as exercise was the main factor, but recent passive behavior such as eating, using a mobile phone, games, food and drinking coffee also appeared as a new behavior characteristic in parks. Third, the factors affecting the behavior of park users are the changes of various conditions of society such as internet development and a culture of expressing unique personalities and styles. Fourth, the special behaviors appearing at Olympic Park were derived from educational activities such as cultural activities including watching performances and history lessons. In conclusion, it has been shown that people's lifestyle changes and the behavior of a park are influenced by the changes of the various times rather than the original purpose that was intended during park planning and design. Therefore, it is necessary to create an environment tailored to users by considering the main behaviors and influencing factors of Olympic Park. Text mining used as an analytical method has the merit that past data can be collected. Therefore, it is possible to form analysis from a long-term viewpoint of behavior analysis as well as to measure new behavior and value with derived keywords. In addition, the validity of online data was verified through social network analysis to increase the legitimacy of research results. Research on more comprehensive behavior analysis should be carried out by diversifying the types of data collected later, and various methods for verifying the accuracy and reliability of large-volume data will be needed.
The WRF-Chem model was applied to simulate the Asian dust event affecting the Korean Peninsula from 11 to 13 November 2010. GOCART dust emission schemes, RADM2 chemical mechanism, and MADE/SORGAM aerosol scheme were adopted within the WRF-Chem model to predict dust aerosol concentrations. The results in the model simulations were identified by comparing with the weather maps, satellite images, monitoring data of $PM_{10}$ concentration, and LIDAR images. The model results showed a good agreement with the long-range transport from the dust source area such as Northeastern China and Mongolia to the Korean Peninsula. Comparison of the time series of $PM_{10}$ concentration measured at Backnungdo showed that the correlation coefficient was 0.736, and the root mean square error was $192.73{\mu}g/m^3$. The spatial distribution of $PM_{10}$ concentration using the WRF-Chem model was similar to that of the $PM_{2.5}$ which were about a half of $PM_{10}$. Also, they were much alike in those of the UM-ADAM model simulated by the Korean Meteorological Administration. Meanwhile, the spatial distributions of $PM_{10}$ concentrations during the Asian dust events had relevance to those of both the wind speed of u component ($ms^{-1}$) and the PBL height (m). We performed a regressive analysis between $PM_{10}$ concentrations and two meteorological variables (u component and PBL) in the strong dust event in autumn (CASE 1, on 11 to 23 March 2010) and the weak dust event in spring (CASE 2, on 19 to 20 March 2011), respectively.
Korea has accomplished the afforestation of its forest land in the early 1980's. To meet the increasing demand for forest products and forest recreation, a development of scientific forest management system is needed as a whole. For this purpose the development of efficient forestfire management system is essential. In this context, the purpose of this study is to develop a theoretical foundation of forestfire danger rating system. In this study, it is hypothesized that the degree of forestfire risk is affected by Weather Factor and Man-Caused Risk Factor. (1) To accommodate the Weather Factor, a statistical model was estimated in which weather variables such as humidity, temperature, precipitation, wind velocity, duration of sunshine were included as independent variables and the probability of forestfire occurrence as dependent variable. (2) To account man-caused risk, historical data of forestfire occurrence was investigated. The contribution of man's activities make to risk was evaluated from three inputs. The first, potential risk class is a semipermanent number which ranks the man-caused fire potential of the individual protection unit relative to that of the other protection units. The second, the risk sources ratio, is that portion of the potential man-caused fire problem which can be charged to a specific cause. The third, daily activity level is that the fire control officer's estimate of how active each of these sources is, For each risk sources, evaluate its daily activity level ; the resulting number is the partial risk factor. Sum up the partial risk factors, one for each source, to get the unnormalized Man-Caused Risk. To make up the Man-Caused Risk, the partial risk factor and the unit's potential risk class were considered together. (3) At last, Fire occurrence index was formed fire danger rating estimation by the Weather Factors and the Man-Caused Risk Index were integrated to form the final Fire Occurrence Index.
Lee, Ho-young;Park, Hong-chul;Lee, Na-yeon;Lee, Ho
Korean Journal of Environment and Ecology
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v.34
no.2
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pp.170-178
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2020
There are few landscape ecological analyses of Khingan fir (Abies nephrolepis) and other habitats of the sub-alpine zone in South Korea. In this study, we tried to quantitatively interpret and assess the habitat characteristics by analyzing 15 landscape indices according to the differences in tree layer coverage, in the Khingan fir habitat growing naturally in the sub-alpine zone of Seoraksan National Park. It was difficult to identify the tendency of landscape ecology to increase and decrease the tree layer coverage in the study site, which was the entire Khingan fir habitat in Seoraksan National Park. However, the Khingan fir habitat was found to be generally low in coverage, and population density as the tree layer coverage of less than 50 percent accounts for 85 percent of the total habitat. Moreover, the Khingan fir habitat in the 10 to 50 percent range was fragmented into a total of 286 patches, making it relatively less connected to the habitat. The total edge length and edge density, which could determine the edge effect of the main part according to the physical form, were the highest in the habitat of 26 to 50 percent coverage, indicating a relatively high impact from outside than habitats of other coverages. The shape with the tree layer coverage of between 10 and 50 percent was more complex even with patches of the same size, and it is believed that these characteristics make it more susceptible to habitat fragmentation and external confounding. We expect that the results of this study can be useful for time series analysis of spatial expansion or reduction of the Khingan fir habitat in Seoraksan National Park and provide the reference data for the morphological change and movement of patches and the connectivity and break-off between forests.
Kim, Eun-Mi;Kwon, Jin-O;Kang, Chang-Wan;Chun, Jung-Hwa
Journal of the Korean Association of Geographic Information Studies
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v.16
no.4
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pp.79-90
/
2013
The altitude range of habitats in which Fairy Pitta inhabited in 1960s is different from the present in Jeju Island. We studied on the habitat environment to understand the causes of difference through the comparison of satellite image data(Landsat) between 1975 and 2002, the literature review in relation to habitats, vegetations, and forest landscapes. The area of below 600m asl.(above sea level) where is mainly Fairy Pitta inhabited at the present with a lot of forests, was massive pasture with small isolated forests nearby valley. The forests were broad-leaved evergreen forests, and second forests with poor condition in the size and forest structure. The forests around 700m asl. were also second forests with approximately 3m height trees. The forests from 800m to 1300m asl. were also disturbed by mushroom cultivation by local people. The authors believe that Fairy Pitta could not inhabited in the area above 1300m because of the poor forest conditions in the size and structure in which consist of Ilex crenata, Rhododendron mucronulatum var. ciliatum and coppice forests. Therefore it might be possible that the best forests for the Fairy Pitta habitat were located in the area of 1,000m to 1,300m above sea level in 1960s. Compared to present habitats, forests at 100m up to 800m above sea level, the authors believe that the size of habitats were smaller with less population of Fairy Pitta. Since 1960s the forest landscape of Jeju Island has been improved successfully, and because of that the population of Fairy Pitta also has been increased. To protect the Fairy Pitta and habitats in Jeju Island, it is suggested that sustainable forest management focusing on the species composition and stand structure maintain or enhance the biodiversity.
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