Table 1 Data source.
Index | Meaning | Secondary indicators | Preprocessing |
|---|---|---|---|
Exposure index | Focus on quantifying the physical exposure of coastal zones to sea level rise and storm surges through natural conditions | Wave exposure | Wave exposure characterizes the likelihood of coastline erosion, with a given section of coastline typically exposed to waves or wind driven waves. Obtained through the third generation wave numerical prediction model (WWIII, WAVEWATCH III) |
Wind exposure | Strong winds blowing through an area for a sufficient amount of time will generate huge waves, and the level of storm exposure is determined based on the relative exposure of coastal segments to strong winds. Obtained through the third generation wave numerical prediction model (WWIII, WAVEWATCH III) | ||
Habitat | Habitat data includes a global mangrove observation dataset provided by UNEP (https://data.unep-wcmc.org/datasets/45) and Global Coral Reef Distribution Dataset (https://data.unep-wcmc.org/datasets/1) | ||
Topography | When facing disasters, high-altitude areas have lower risks compared to low altitude areas. Calculate the surface roughness based on DEM and classify it into levels | ||
Sensitivity index | Characterizes the sensitivity of coastal socio-economic systems during marine disasters. Coastal areas with high population density have concentrated economies and carry diverse urban functions. They are more sensitive to marine risks and suffer greater economic losses | Population density | Based on the Guangdong Statistical Yearbook (2020), the QGIS platform uses the Heatmap tool for visualization and the natural breakpoint method to classify population density levels. High density population gathering areas have high disaster sensitivity |
Adaptability index | The ability of coastal cities to reduce the impact of marine disasters is closely related to the level of urban development and infrastructure resilience | Transportation facilities | POI data of subway stations and medical facilities were collected through web crawling. These were then used to calculate the spatial distribution and accessibility of transportation and healthcare infrastructure |
Hospital | Crawling POI hospital vector point data from medical facilities using Python language, verifying it with the current map, and importing it into ArcGIS platform for spatial visualization processing |
