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With the increase in disasters and damage caused by climate change, the global population and infrastructure are gradually being concentrated in urban areas as a result of rapid urbanization, and concerns for disasters in urban areas are increasing. Vulnerability to and risks of disasters are particularly serious issues in declining areas with poor physical and social environment. The improvement of urban resilience is necessary to prevent disasters in these areas, and an index for evaluating the current resilience level in these areas is necessary. To determine the suitability of evaluation index application for disaster resilience in declining areas, this study generated a list of 24 evaluation indices by reviewing published studies on this topic and examined the applicability of these indices in a survey conducted with 30 experts. The independence of each index was assessed through policy network analysis. In addition, the relevance and applicability of the indices were determined, and their effectiveness was assessed using the fuzzy multiple-criteria decision making method. Lastly, the priorities of the evaluation indices were suggested by combining the two analysis methods mentioned above. The results of this study will likely contribute to the development of an evaluation index for effective evaluation of urban resilience in declining areas.
The uncontrolled urban expansion causes various social, economic problems and natural/environmental problems. Therefore, it is necessary to forecast urban expansion by identifying various factors related to urban expansion. This study aims to forecast it using a decision tree that is widely used in various areas. The study used geographic data such as the area of use, geographical data like elevation and slope, the environmental conservation value assessment map, and population density data for 2006 and 2018. It extracted the new urban expansion areas by comparing the residential, industrial, and commercial zones of the zoning in 2006 and 2018 and derived a decision tree using the 2006 data as independent variables. It is intended to forecast urban expansion in 2030 by applying the data for 2018 to the derived decision tree. The analysis result confirmed that the distance from the green area, the elevation, the grade of the environmental conservation value assessment map, and the distance from the industrial area were important factors in forecasting the urban area expansion. The AUC of 0.95051 showed excellent explanatory power in the ROC analysis performed to verify the accuracy. However, the forecast of the urban area expansion for 2018 using the decision tree was 15,459.98㎢, which was significantly different from the actual urban area of 4,144.93㎢ for 2018. Since many regions use decision tree to forecast urban expansion, they can be useful for identifying which factors affect urban expansion, although they are not suitable for forecasting the expansion of urban region in detail. Identifying such important factors for urban expansion is expected to provide information that can be used in future land, urban, and environmental planning.