KJC Korea
Journal Central

The aim of this study is to draw practical policy implications in terms of the management of public rental housing to cope with internal and external stigma against tenants of public rental housing. This is based on the analysis was on physical features and the demographic/economic/social characteristics of the tenants in public rental housing in Seoul. The analysis reveal that there are structural factors that cause concentrations of specific groups of tenants with the same features in terms of income and age in certain types of public rental housing such as ‘permanent public rental housing’, and these factors give rise to social prejudices. Also, semi-structured interviews with residents and the managers of public rental housing were conducted to examine discrimination and marginalization. The study foundfindings include that 1) stigma matters not only in relation with private housing residents but also amongst residents in public rental housing, 2) more active engagement on the anti-social behaviors of a few residents is crucial, and 3) it is important to establish detailed and practical goals for social mixing policy when managing public rental housing. The study concludes with practical suggestions to alleviate this stigma by reallocating residents with a consideration of the size of housing units and household, and their locations.

The spread of the COVID-19 virus has had a serious impact on Korea as well as the world. Although there is a great deal of discussion on changes in urban planning that are necessary to cope with infectious diseases, studies on the impact of regional characteristics on infectious diseases are lacking. Therefore, the purpose of this study was to analyze the impact of regional characteristics and spatial connectivity on the spread of COVID-19 infection for 246 cities in Korea. Spatial regression analysis was conducted to control spatial autocorrelation, and as a result of the analysis, the optimal model was found to be a spatial error model (SEM). Specifically, it was found that higher populations, population density, building density, and pedestrian road density was correlated with higher numbers of confirmed COVID-19 cases, while higher job-housing balance was correlated with lower numbers of confirmed patients. In addition, an important factor that influences spread of the virus was not only internal traffic, but also external traffic to areas with severe COVID-19 infection numbers. This study establishes some basic data and empirically analyzes the influence of regional characteristics at a time when urban problem solving is required due to the rapid spread of COVID-19.

The purpose of this study is to conduct risk-response analysis by region considering the spatio-temporal linkage structure of an emergency response system, to derive regions requiring intensive investment, and to analyze their characteristics. First, we looked at how urgent it is to build an emergency response system in our city or region. To this end, golden time standards were set for each response stage, and based on this, the golden time satisfaction rate for each response stage was analyzed for 229 cities(si), counties(gun), and districts(gu) across the country. On the other hand, even if the level of response is low, additional efforts may not be necessary in low-risk areas. Accordingly, in this study, 229 cities, counties, and districts were classified into four types by comparing risk levels and response levels, and areas requiring special measures (priority investment areas) were identified. Afterwards, the characteristics of the priority investment areas were also analyzed, and some useful policy implications were derived. However, it is difficult to obtain data on the current status of violent crimes by region, so there is a limit to analyzing only 77 cities. Additional analysis is required in the future to derive more meaningful results.

This study estimates eight types of regression models using number of visitors based on mobile phone data and the number of searches for tourist destinations from car navigation systems as the dependent variables, and four types of daily weather conditions as the independent variables. The model estimation results suggest two consistent implications regarding the correlation between the number of tourists visiting Gangneung and weather conditions in the area. First, the number of tourists increases when the daily high temperature rises. The number of tourists reaches its peak when the daily high temperature is between 30 to 33 degrees Celsius. The number of tourists gradually decreases as the daily high temperature exceeds 33 degrees Celsius, and steeply decreases as the temperature exceeds 35 degrees Celsius. Also, a statistically significant decrease in the number of tourists occurs when daily precipitation is greater than 25 millimeters. This result is based on the analysis of the circumstances during the summer of 2018, but can be interpreted in relation to the RCP scenarios in the region. The RCP scenarios for the region predicts that by year 2100 the average temperature will increase along with the number of summer days and extreme heat days. When the results of the regression analysis and the RCP scenarios are taken together, the number of tourists during the summer is expected to increase as the climate changes, but increasing days of extreme heat may offset this effect, which suggests the necessity of policy measures to promote tourism in response to excessive heat.