sensor data of a potential violation.
How does this evidence stack up
against the enforcement record for
the complaints? Citizen complaints
submitted via 311 and routed to the
DEP trigger an inspection, and pub-lic-record repositories made available by the city include information
about how each complaint was resolved. Examining the records, we
found that, for all complaints in this
study, 78% resulted in a “No violation could be observed” status and
only 2% in a violation ticket being issued. Figure 4b shows, in the specific
case of after-hours construction
noise, no violation could be observed
in 89% of all cases, and none of the inspections resulted in a violation ticket
being issued.
There are multiple possible explanations for the significant gap between the evidence collected by the
sensor network and the results of the
inspections. For example, we speculate it is due in part to the delay in the
city’s response to complaints, four to
five days on average, which is too
great for phenomena that are both
transient and traceless. Another factor is the conspicuousness of the inspection crew that alone modifies the
behavior of potentially offending
sources, as we observed during our
site visits with the DEP. Moreover, under some circumstances the city government grants special, after-hours
construction permits under the assumption of minimal noise impact,
as defined by the noise code. It is
thus possible that some after-hours
activity results from such permits.
We are currently mining after-hours-construction-permit data to understand this relationship better.
In all cases, the SONYC sensing
and analytical framework is able to
address the shortcomings of cur-
rent monitoring and enforcement
mechanisms by providing hard data
to: quantify the actual impact of af-
ter-hours construction permits on
the acoustic environment, and thus
nearby residents; provide historical
data that can validate complaints
and thus support inspection efforts
on an inconspicuous and continuous
basis; and develop novel, data-driven
strategies for the efficient alloca-
tion of inspection crews in space and
cluding crowdsourcing workers and
volunteers, and bear meaningful rela-
tionship to the properties of the data
in the physical world that, in the case
of sound, implies the need for three-
dimensional visualization.
We have been working on a three-dimensional, urban geographic information system (GIS) framework
called Urbane9 (see Figure 3), an
interactive tool, including a novel
three-dimensional map layer, we developed from the ground up to take
advantage of the GPU capabilities
of modern computing systems. It
allows for fast, potentially real-time
computation, as well as integration
and visualization of multiple data
streams commonly found in major
cities like New York City. In the context of SONYC, we have expanded
Urbane’s capabilities to include efficient management of high-resolution temporal data. We achieve
this efficiency through a novel data
structure we call the “time lattice”
that allows for fast retrieval, visualization, and analysis of individual
and aggregate sensor data at multiple time scales (such as hours, days,
weeks, and months). An example of
data retrieved through this capability can be seen in Figure 3, right plot.
We have since used Urbane and the
time lattice to support the preliminary noise analysis we cover in the
next section, but their applicability
goes well beyond audio.
We are currently expanding Urbane to support visual spatiotemporal queries over noise data, including
computational-topology methods for
pattern detection and retrieval. Similar
tools have proved useful in smart-cities
research projects, including prior collaborations between team members
and the New York City Department of
Transportation and Taxi and Limousine Commission.
7, 10
Data-Driven Mitigation
We conducted a preliminary study in
2017 on the validity and response of
noise complaints around the Wash-
ington Square Park area of Manhattan
using SONYC’s sensing and analytics
infrastructure.
19 The study combined
information mined from the log of civ-
ic complaints made to the city over the
study period through the 311 system,
For the study we chose an area in
Greenwich Village with a relatively
dense deployment of 17 nodes. We
established a 100-meter boundary
around each node and merged them
to form the focus area. From 311,
we collected all non-duplicate noise
complaints occurring within this area
that had been routed to the DEP while
neighboring sensors were active. Note
this criterion discards complaints
about noise from residents that are
routed to the police department and
tend to dominate the 311 log; see Fig-
ure 4a for a breakdown of selected
complaint types.
Over an 11-month period—May
2016 to April 2017—51% of all noise
complaints in the focus area were related to after-hours construction activity ( 6 P.M.– 7 A.M.), three times the
amount in the next category. Note combining all construction-related complaints adds up to 70% of this sample,
highlighting how disruptive to the lives
of ordinary citizens this particular category of noise can be.
Figure 4c includes SPL values (blue
line) at a five-minute resolution for
the after-hours period during or immediately preceding a subset of the
complaints. Dotted green lines correspond to background levels, computed as the moving average of SPL measurements within a two-hour window.
Dotted black lines correspond to SPL
values 10dB above the background,
the threshold defined by the city’s
noise code to indicate potential violations. Finally, we were able to identify events (in red) in which instantaneous SPL measurements were above
the threshold. Our analysis resulted
in detection of 324 such events we
classified by noise source and determined 76% (246) were related to construction as follows: jackhammering (223), compressor engines ( 16),
metallic banging/scraping ( 7), and
the remainder to non-construction
sources, mainly sirens and other traffic noise. Our analysis found for 94%
of all after-hours construction complaints quantitative evidence in our