Biometrics, Iris Recognition, Video Analysis & Predictive Software – “Most Wanted Police Tech 2014″

Embracing the Police Force of the Future

Article Courtesy of:  CNN

BGer Daly – Article Courtesy of: CNN

Predictive Policing Data Analysis Software


  • Police forces around the world are fighting crime with new data-mining tools
  • San Diego’s streetcars have video analytics that can spot suspicious behavior
  • Major crime in Memphis fell 30% with software to predict where crimes might take place
  • Next on the horizon for law enforcement: biometrics, including facial recognition

(CNN) — Contrary to the Hollywood image in movies like “Minority Report,” technology hasn’t served law enforcement particularly well over the years.

Fragmented and complex operating systems have challenged police officers to manually enter information into multiple programs. And yet officers still struggle to retrieve the information they need — especially in the field, where it can be a matter of life or death.

A large number of law enforcement agencies are still hindered by antiquated technologies. But agencies that have upgraded their operating and investigative systems have been tremendously effective in ensuring the safety of their citizens. Police forces like the Guardia Civil in Spain and An Garda Siochana in Ireland were early technology adopters and now benefit from some of the most efficient police operations and investigative systems in the world.

These are the police forces of the future — the ones that others will be modeling themselves after in the years to come.

Accenture recently studied police forces from around the world and found that in every region, police are hungry for new technology. They see tech such as analytics, biometrics (identification of humans by their characteristics or traits) and facial recognition as keys to effectively fighting crime and maximizing the time officers spend in the field.

Despite the reality of reduced budgets, law enforcement agencies that adopt new technologies can prevent crimes more effectively and solve crimes faster.

ACLU raises privacy concerns about police technology tracking drivers

Video Analytics

Predictive Policing Video Analysis

What many people don’t know is that there’s a solid infrastructure of closed-circuit TV in most cities. Historically, these CCTV cameras — both publicly and privately owned — have been used retrospectively to examine crime scenes for evidence.

Images from street cameras along the Boston Marathon route helped identify the two bombing suspects there last April.

In California, the San Diego Trolley Corporation now safeguards light-rail passengers with a video-analytics system that can alert security guards when it spots suspicious behaviors, such as an unmarked vehicle in a pedestrian zone.

Cities such as London and Singapore also are testing pilot programs to apply predictive analytics to video feeds. Singapore’s government and economic leaders recently launched a one-year “Safe City” pilot program to bring automated analytics to existing CCTV infrastructure across the city. The program will apply predictive analytics to video feeds to detect which of a multitude of street incidents, such as crowd and traffic movements, pose real concerns for public safety.

These video feeds also will identify environmental threats to public safety, such as fire or flooding, as they arise. When a serious incident is identified, an alert will be sent to the authorities.

This program enables real-time information sharing and will give law enforcement deeper insight into public safety across Singapore’s densely populated urban landscape. It also will increase police ability to anticipate and respond to incidents as they occur.

Police embracing tech that predicts crimes

Data Mining & Predictive Analytics

Data Mining & Video Analysis

Other cities are using statistical analysis and predictive modeling to identify crime trends and highlight “hidden” connections between disparate events.

This helps police gain a more complete picture of crime, predict patterns of future criminal behavior and identify the key causal factors of crime in their area.

Police in Richmond, Virginia, adopted an advanced data-mining and predictive-analytics program in 2006 in an ambitious campaign to reduce crime. In the first year of use, the city’s homicide rate dropped 32%, rapes declined 19%, robberies fell 3% and aggravated assaults dropped by 17%.

Police in Memphis, Tennessee, also applied predictive analytics — which relies on data-analysis software to predict where crimes will likely take place — and saw immediate results. Serious crime in that city fell 30% between 2006 and 2010. Such technology also has been hailed for helping to lower crime rates in Los Angeles since its introduction by the LAPD in 2011.

And Lafourche Parish, Louisiana, uses an analytics model that brings together location-based crime and traffic-crash data to develop effective methods for deploying law enforcement and other resources. Using geo-mapping to identify “hot spots” — areas with high rates of crimes and car accidents — the parish saw the number of fatal drunk-driving crashes fall from 27 in 2008 to 11 in 2009, with a corresponding increase in drunk-driving arrests.

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Article Courtesy of:  CNN

By Ger Daly – Article Courtesy of: CNN

Facebook & Big Data Collide

Big Data Could Cripple Facebook

Article Courtesy of:  TechCrunchJON EVANS

Big Data - Investigative Database

So there’s this startup called SmogFarm, which does big-data sentiment analysis, “pulse of the planet” stuff. I spotted them last year, and now they’ve got an actual product with an actual business model up and running in private beta: KredStreet, “The Social Stock Trader Rankings,” which performs sentiment analysis on StockTwits data and a sampling of the Twitter firehose to determine traders’ overall bullish/bearish feeling. They also compare reality against past sentiment to score and rank traders based on their accuracy, which is more interesting.

It’s a first iteration, but it looks pretty nifty, and I like the idea of a ranking system wherein unknowns can leave high-profile loudmouths in their dust by virtue of simply being right more often. Even if I feel slightly uneasy when I imagine such a system being applied to, say, tech bloggers.

Actually being held accountable for what I’ve written in the past?  

Doesn’t that just seem terribly wrong?

And of course it’s early days yet for companies like SmogFarm/KredStreet, and sentiment analysis, and natural language processing (such as that which powered Summly), and Palantir-style data mining. Just imagine what they’ll be able to do in five years.

And when they turn all that big-iron, big-data searchlight power on, say, Facebook timelines… what won’t they be able to determine???

A few years ago the EFF discovered that something as simple as your browser settings make you a lot less anonymous online than you might believe. Last week a study found that “human mobility traces are highly unique,” and when polling allegedly anonymous cell-phone location data, “four spatio-temporal points are enough to uniquely identify 95% of the individuals.” Good software can mine a lot of meaning out of apparently sparse and empty data.

So just imagine what happens when next-generation language and image-processing software, and then the generation after that, and the generation after that, is unleashed on your Facebook timeline. It seems very plausible that all those innocuous things you say, and how you say them, and the pictures you post, and the games you play, will subtly and invisibly add up to a terrifyingly accurate portrait of you, including any and/or all of the things about yourself that you never actually wanted to make public.

What’s worse is that it will be ridiculously easy. Would-be employers won’t have to scroll through your Facebook timeline themselves, they’ll just need to point their profiling software in your direction and 30 seconds later read its high-confidence predictions of your work habits, neuroses, personal failures, emotional instabilities, attitude towards authorities, and sexual proclivities, all expertly extrapolated from the tapestry of subtle-to-invisible nuances accumulated from all of your photos, comments, Likes, upvotes, etc.; all individually meaningless, but collectively highly illuminating. Individual profiling is a huge business just waiting to be tapped by ethically challenged startups.

(This could be mitigated somewhat if you were to keep all your activity friends-only, of course; but even then, every app or distant acquaintance you’re connected to will be able to learn more about you than you ever intended. And it’s easy to envision employers requesting that you connect to them on Facebook as part of the job-application process, and filtering out those who refuse…)

I can imagine what that kind of profiling software would have said about me, early in my career: Hopeless bibliophile. Afflicted with incurable wanderlust. Doesn’t like being told what to do. Extremely chancy hire: likely to quit any job after six months to travel or try to write the Great Canadian Novel.

Which, er, would have been one thousand per cent true; but obviously I didn’t want my potential employers back then to know about it.

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