61. Mapping Schemes Tool (SIDD)
Use Cases Description
Update national default mapping schemes Updating default assumptions of national‐level construction practices inherent in
the GED
Update regional mapping schemes Default mapping schemes applied in the GEM GED are at the national level,
although construction practices may vary in known ways throughout a country.
Incorporate building data sets (attributes) Building data is typically available for select buildings within an area, but
sometimes data may be available representing every building. Examples may
include U.S. tax assessor data or postal code databases. These will typically lack
attribute information to assess on a building –specific level.
Incorporate building data sets (footprints) Building footprints are often available for local municipalities based on
digitization of aerial photographs. In addition, these data can be automatically
extracted from the same data sets with varying degrees of success‐ depending
on the built environment. Although building footprints are seldom accompanied
with attribute data to assign structure type, in aggregate, they significantly
increase the accuracy of estimated number of buildings in the default GED
database.
Incorporate aggregate exposure data In some cases, building data may be available at the national level, but not
distributed in raw form due to privacy or security concerns. Typically, this data is
aggregated to a grid or postal code.
Calculate replacement cost The GEM GED provides basic estimates of replacement costs that are only
applicable to residential construction, and do not vary by construction type.
These data are developed from global assumptions.
Characterize local building patterns It is common for development patterns to be strong indicators of construction
practices, either due to land use, era of development, or other factors.
Incorporate land use data Construction practices can vary significantly by land use practices, often available
in vector datasets at the national, regional, or local level. Urban density may
even be inferred from moderate or low resolution satellite data‐ which can be
used to develop crude land use zones.