Geography Reference
In-Depth Information
The images are clipped scene from Google earth engine which constitutes
a major part of the generated database system. A fundamental benefit of this
modeling method, which involve locally aligning a well-integrated, carefully
and logically generated flood risk mask with high resolution remotely sensed
data is that it is possible to track down a specific residential building,
monument, hotel, administrative building, reli-gious or shopping complex, as
the case may be, that lies within the risk zone.
From the perspective of interpretation, the result is highly simplified and
visually alluring for policy makers who do not have expert knowledge in GIS
and spatial analysis. Progressively, the vectorized road and rail network data-
base was exported as KML file from Google earth engine to ArcMap as shape-
file. This database was then overlaid (using spatial intersect technique) on the
flood risk database to calculate the amount of accessibility infrastructures that
are exposed to the disaster (Figure 12).
For the purpose of detailed visualization, Figure 12 was zoomed in on the
most urbanized area of the state-Lagos Island, Eti-Osa, Surulere, Lagos Main-
land, Apapa, Ajeromi-Ifelodun and Amuwo-Odofin administrative areas. The
result of the spatial database integration shows that the overall length of road
potentially at risk is 300.91 km. High priority infrastructure like dual carriage
road has a total length of 54.5 km exposed to flooding disaster and this is
about 18 percent of the overall length.
Figure 12. An integrated geo-database for assessing susceptible infrastructures.
