Paving the Way with Analytics
Turning information overload into actionable intelligence
- By Kevin Taylor
- Sep 01, 2019
How many of you remember learning about the Great Chicago
Fire of 1871? Some of you might even recall the ditty
that sprung from the legend of its origin:
Late last night when we were all in bed
Mrs. O’Leary hung a lantern in the shed
And when the cow kicked it over
She winked her eye and said,
“It’s going to be a hot time in the old town tonight.
Fire! Fire! Fire!”
While there’s no irrefutable proof that the cow was the culprit in
the devastation that left miles of the Windy City in ruins, imagine
how different the outcome might have been if Mrs. O’Leary had been
connected to an electric grid to light her property or if she had a
monitored system that detected the situation and communicated out
to emergency responders.
Technology—from street lights to telephones—has been at the
heart of modern cities for more than a century. Even the idea of
“Smart Cities” isn’t new. There is a publication on the concept dating
back more than a quarter century. (The Technopolis Phenomenon:
Smart Cities, Fast Systems, Global Networks, D.V. Gibson, G.
Kozmetsky, and R.W. Smilor, eds., Lanham, MD: Rowman & Littlefield
Publishers, 1992). Though actual implementation of the concept
is far more recent, cities have been using advanced technology to
address the challenges of densely populated urban centers for years.
Thanks to the Internet of Things (IoT), there are now rapidly
emerging platforms that leverage interconnected devices to help cities
oversee everything from power plants and water supply networks to
mass transit, emergency communications, and law enforcement.
The unfortunate downside to all this interconnectivity is that
amassing these large, and seldom uniform, data sets can lead to organizational paralysis rather than the desired results of knowledge,
action, and measurable positive outcomes.
This leads to the suggestion that what we really need are “Smarter
Cities,” ones in which we can intelligently sift through that vast
amount of IoT-generated data, quickly extract the most essential information,
and act decisively based on what we’ve learned. And this
is precisely where analytics excel. Analytics can be a powerful force
multiplier when it comes to efficiently processing volumes of multisourced
data—whether thousands of video cameras, environmental
sensors, access control switches, or other devices.
While there are countless analytics on the market today, I’d like to
focus on those related to video surveillance. These analytics generally
fall into three categories: detection, data collection, or forensic review.
Most video surveillance systems are used primarily in post-event investigation.
When coupled with analytics, however, you can transform
them into proactive detection systems that alert operators in
real-time that something of interest is taking place. Some of the more
common examples of detection analytics include intelligent motion
detection, crowd gathering, loitering, and object left behind.
There is tremendous upside to the early warning these analytics
provide—whether it’s notifying police that a crowd is building in an
unanticipated area so that they can respond to a possible fight before
it escalates into an all-out riot; or letting event organizers and public
safety officers know that a temporary barrier at a parade or festival
has been breached so they can work together to mitigate a potential
threat and re-establish the barrier; or even recognizing that a suspicious
object has been left behind near a cultural landmark or mass
transit depot so that an operator can quickly validate the alarm and
initiate a pre-planned emergency response.
Most detection analytics operate based on a set of filters that the
user must initially configure for the specific use case. With the emergence
of Deep Learning, though, some analytics can be trained over
time to improve their accuracy.
However, no detection analytic can be 100 percent accurate. Their
performance and delivered value largely depend on how you define
the specific use case and the desired outcomes. Working through this
pre-deployment with all the stakeholders will ultimately lead to the
best selection and provisioning of hardware and software to accomplish
your city’s goals for detection. You’ll not only miss fewer critical
events but also avoid excessive nuisance alarms.
Analytics can also play an important role in city planning. Because
cities are constantly evolving, municipal officials are continually
monitoring, measuring, and evaluating conditions so development
decisions can be made based on actual current data.
For example, pushing a video stream through an analytic application
can help city departments collect specific data on pedestrian, vehicle,
and bicycle traffic. The collected data then provides a basis for
informed decisions about crosswalk and bike lane placement—even
mass transit schedules.
Another emerging trend is to bridge data, such as lane-by-lane
vehicle counts and travel times, in real-time to traffic signal control
equipment. There are now cities that use analytics to minimize traffic
jams by moving traffic signals off hard-set timers and operating them
dynamically based on real-time conditions captured by intelligent
Expeditious Forensic Review
As more police departments partner with the private sector to enhance
video coverage of public spaces, analytics are becoming essential
tools for intelligently processing the massive amount of video
data flowing into command centers from multiple sources. In the
past, it would take officers days, and even sometimes weeks, to sift
through all the video related to an incident before finding the few
critical seconds of footage.
Now, instead of searching for a needle in a haystack, there are
analytics that enable officers to query the data set using specific identifying
parameters such as someone wearing a green shirt or driving
a red car in a certain window of time.
Relevant video pops up on the video monitor significantly expediting
Analytics can also help law enforcement recognize patterns over
time, like the migration of crime from one neighborhood to another.
Some police departments are even using analytics like dwell
time and vehicle/pedestrian traffic patterns to pinpoint the location
of drug dealers.
Given that many police departments are understaffed and light
on resources, it becomes even more important to include analytics
in their crime-fighting arsenal. Swifter, more efficient forensic review
frees officers to spend more time building relationships with the community
and fostering trust.
The Burgeoning Portfolio
of Analytic Solutions
As more metropolitan areas embrace the concept of Smart City,
we’re bound to see a rise in analytics development and performance.
New companies will emerge, eager to capitalize on the opportunity
to promote their suite of products. But before a city considers the
burgeoning portfolio of analytics, officials need to ask themselves:
• What pain point are we trying to resolve or what goal are we trying
• Is analytics the best way to address the issue or can existing tools
do the job?
• Can the analytics be easily scaled with equity for the betterment
of the entire community
• Is the solution provider in the relationship for the long haul with
a strong record of service and support?
Analytics hold great allure for cities exploring ways to improve efficiencies
and create a safer, more secure environment for their citizens
and visitors. And with constant development in code and algorithms,
today’s analytics are continuously becoming more reliable and accurate
in detection, data collection, and forensic review. In addition, they’re
becoming more efficient in terms of processing requirements.
When you couple these achievements with more robust processing
power of edge IoT devices, video analytics for cities becomes even
These developments broaden the selection of analytic applications
that can run directly on edge IoT devices and sensors, overcoming
the challenges of solution architecture and cost that are common
when application processing must be de-coupled from the sensor (run
on different hardware such as a hardened PC or in the cloud).
will likely see more analytic offerings in the near future that will run
at the edge and scale well for cities—a true 1:1 ratio architecture of
sensor and application processing in a single device.
Going from Smart to Smarter
With more people flocking to urban centers than ever before, cities
will continue to look for innovative technology to help offset the
strain on resources and improve the quality of life
for their citizens. By adding analytics to their operational
toolbox, municipalities can raise the bar
and transform their smart cities into even smarter
(and safer) ones.
This article originally appeared in the September 2019 issue of Security Today.