1 Mapping the buildings in Manhattan Storm Surged Area and identify the most impacted building in the Surged Area Haozhe Wang (William) Center for Urban Science and Progress New York University Introduction After Hurricane Sandy swiped the east coast of United States, many states suffered severe financial lost. Estimated 60 million people were affected by the hurricane. Economists say as high as 100 billion lost might be caused by the storm (NPR News, 2012). Yet, what matters more is people’s life disturbed by the cyclone. According to NOAA (Blake, Eric, 2013), the highest storm surge hit 11.4 ft. near Battery Bark in lower Manhattan. What does that mean to us? The damage was caused through loss of lives, water damages, and power loss. Last semester, I examined the impact of Hurricane Sandy on Manhattan residents. For this project, the buildings in Manhattan will be examined, and the result will be presented on maps and in 3-D views. Two tools, ArcMap and ArcGlobe, were used to implement the study. ArcMap allows information to be presented in a 2-D fashion, and ArcGlobe is an adequate tool to show the buildings in 3-D views. The goal is to identify the buildings in the storm surged area and classify them by the weather normalized Energy Use Index to mark buildings with their energy Figure 1 Map view: PLUTO data joined with LL84
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1
Mapping the buildings in Manhattan Storm Surged Area and identify the
most impacted building in the Surged Area Haozhe Wang (William)
Center for Urban Science and Progress
New York University
Introduction
After Hurricane Sandy swiped the east coast of United States, many states suffered severe
financial lost. Estimated 60 million people were affected by the hurricane. Economists say as
high as 100 billion lost might be caused by the storm (NPR News, 2012). Yet, what matters more
is people’s life disturbed by the cyclone. According to NOAA (Blake, Eric, 2013), the highest
storm surge hit 11.4 ft. near Battery Bark in lower Manhattan. What does that mean to us? The
damage was caused through loss of lives, water damages, and power loss. Last semester, I
examined the impact of Hurricane Sandy on Manhattan residents. For this project, the buildings
in Manhattan will be examined, and the result will be
presented on maps and in 3-D views. Two tools,
ArcMap and ArcGlobe, were used to implement the
study. ArcMap allows information to be presented in
a 2-D fashion, and ArcGlobe is an adequate tool to
show the buildings in 3-D views. The goal is to
identify the buildings in the storm surged area and
classify them by the weather normalized Energy Use
Index to mark buildings with their energy
Figure 1 Map view: PLUTO data joined with LL84
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consumption. In the final maps, buildings with high energy consumption will be in darker color,
which also indicate that they suffer more from power loss because their operations relied heavily
on electricity before Hurricane Sandy.
To conduct the idea of calculating the affected buildings in Manhattan, several files are
needed: the storm surge area shape file (due to the availability of reliable data, the relative sea
level rise and coast erosion are ignored here), PLUTO data, Manhattan borough shape file
(another name to look for is New York County, most government released documents take this
name as the officially recognized name), and Local Law 84 Disclosure (LL84) data. LL84 data
contains many attributes of building energy consumption information. PLUTO data included all
the buildings in each lot in five boroughs of New York City. By linking the two sets of data, we
can portray the city with building energy usage. Unfortunately, Local Law 84 only regulates
private buildings; therefore, we will not be able to have every New York City building’s energy
usage data for this study.
Methodology
The key of this study is to join the PLUTO
data and LL84 data by “BBL”. PLUTO data and
shapefile can be found on NYC Department of
City Planning website. Inner join based on LL84
data will allow us to keep all the matching records.
Be cautious that the headers of LL84 file need to
be modified according to ArcGIS’s requirement.
The original file has special formatting and
Figure 2 Joined data
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special characters. All of them need to be
removed in order to perform the inner join.
Once, joined, ArcMap will generate new
names for each field using the file name
and column number. It is recommended to
change the alias in order to distinguish each
building characteristics in ArcMap attribute
table. In this study, the weather normalized
EUI was changed to “My_LL84_7” in the
exported joined file. The second ‘trick’ of
this study is about properly selecting the
buildings sitting in the storm surged area.
The principle is selecting all the buildings that intersect with the storm surged area. Using ‘Select
by Location’, the buildings impacted in Manhattan during Hurricane Sandy can be exported to a
separate file. A total 4,620 records matching records were exported from PLUTO and LL84 data.
The second part of the study is to present building energy use in three dimensions. The
work is done in ArcGlobe using the floor number from PLUTO data. The calculation of the