A Multilayered Analytical Visualization Method for assessing Forest-Urban-Disaster Resilience

This paper presents a method of multilayered analytical visualization for detecting time-series changes in urban vegetation and forest distribution by integrating distributed time-series geographic information data, socioeconomic indicator data, and natural disaster data. As the world's large areas have been deforested seriously in recent years, there has been a growing demand for methods that accurately assess the importance of forests and their effects and impacts. This study aims to establish a process to assess forests' role in urban disaster resilience. Specifically, the method is designed to analyze the relationship between 1) forest distribution and population density, 2) forest distribution and urban infrastructure development, and 3) forest distribution and disaster occurrence, using GIS, satellite data, demographic, highway, flood, and inundation data. From the results of the analysis, the relationship between forest-urban development and natural disasters will be evaluated. This paper demonstrates the feasibility of the proposed method for the realization of SDG 15 and the potential for the integrated use of various and distributed open data through the experiments conducted in the Kanto region of Japan.