## 2 Materials and Methods
### Case Study 1: Application of the Location Analysis Model  
This study applied a data analysis model to Seoul, a representative heat island city in South Korea. To this end, the study area analysis DB and GIS S/W were utilized to identify rooftop greening and roadside tree planting locations and to calculate their area. Seoul has an area of 605 square kilometers and a population of 9.7 million people, and is a basin-type city surrounded by mountains. Due to its highly concentrated development, heat island phenomena occur frequently in Seoul (Figure 4a). According to the Korea Centers for Disease Control and Prevention (KCDCP), the temperature has been increasing yearly, and the number of heat patients in Seoul also grew from 27 in 2011 to 116 in 2019; the damage is expected to continue. In response, the city government has been making efforts to reduce heat islands through various pilot projects such as the introduction of green roofs and the planting of roadside trees. There are a total of 742,770 buildings that were included in the study area; if rooftop greening is actively introduced, the effect is expected to be considerable. The city government established a plan to utilize rooftop greening in 2007 to create an institutional framework for expanding rooftop greening across the city, but the pilot project was only partially implemented because the space needed was not clearly identified. In addition, the study area is a mixture of old and new regions, and the age of buildings, roof forms, and walking widths vary depending on the region, making it a city suitable to apply the location analysis model developed in this study. Figure 2: Algorithm for analyzing suitable locations for green roofs. Figure 4: The study area (Seoul, South Korea). (**a**) Satellite image (Source: [[http://maps.kakao.com](http://maps.kakao.com)]([http://maps.kakao.com](http://maps.kakao.com)), accessed date: 20 August 2020) and administrative boundary. (**b**) Distribution of daily average temperatures of the study Area. (Source: Observation Data from Korea Meteorological Administration (public) and SK techX (private) Automated Weather Systems; no precipitation in 2017; average daily cloud amount below 3; the average air temperature of the nine days when the average wind speed is below 5.4 m/s). Figure 3: Algorithm for analyzing suitable locations for roadside tree planting. Figure 4b maps out the average daily temperature using spatial interpolation based on the results of 280 automatic weather observation stations in operation within the study area. It can be confirmed that heat island phenomena are occurring frequently around the interiors where urbanization has long been underway. The heat island areas in the study area are relatively large, accounting for 23.88% of the total area, and strong heat island phenomena are evident in the east-west direction. Moreover, large-scale heat island areas are distributed in the eastern part of the study area. Case Study 2: Analysis of Air Temperature Reduction Effect by Rooftop Greening and Roadside Tree Planting  
It is important to first consider areas where rooftop greening and roadside tree planting require high financial costs and where temperature reduction effects can be maximized even if the area is small. This study selected three of the heat island areas presented in Figure 4b with different urban climate zone types of Lee, Oh, and Jung [35] as the targets of case study 2 (Table 2 and Figure 5). This study analyzed the effectiveness of temperature reduction by rooftop greening and roadside tree planting by applying Envi-met (ver. 4.0), which is a CFD model. The simulation day (one day) was selected in the summer period of 2015 (June-August) when there is no precipitation, the wind is calm, and there are few clouds, to minimize the impact