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This week, fighting crime before it happens. | :00:00. | :00:00. | |
We are now more surveilled than we have ever been. | :00:00. | :00:38. | |
Authorities are gathering data on its citizens. | :00:39. | :00:44. | |
It would be all too easy to confuse the real world | :00:45. | :00:47. | |
Mr Marks, my mandate of the District of Columbia Pre-Crime Division. | :00:48. | :00:53. | |
I'm placing you under arrest for the future murder | :00:54. | :00:55. | |
of Sarah Marks and Donald Dubin, that was due to take | :00:56. | :00:57. | |
place today, April 22, at 0800 hrs and four minutes. | :00:58. | :01:00. | |
In the movie Minority Report, the Pre-crimes Unit race to arrest | :01:01. | :01:05. | |
would-be offenders before they have a chance to | :01:06. | :01:07. | |
Now, they use psychics but it turns out, something similar | :01:08. | :01:12. | |
In Chicago, where the violent crime rate has exploded, | :01:13. | :01:19. | |
law enforcement has been forced to try out unconventional | :01:20. | :01:22. | |
Authorities are attempting to combine various technologies | :01:23. | :01:28. | |
in an effort to predict where and when violent | :01:29. | :01:30. | |
Marc Cieslak went to Chicago to find out more. | :01:31. | :01:39. | |
Violent crime in Chicago has seen a dramatic increase. | :01:40. | :01:42. | |
RADIO: A 15-year-old male, shot in the neck. | :01:43. | :01:45. | |
We need a wagon with a body bag also. | :01:46. | :01:54. | |
The drug industry is what helps them fuel the violence, | :01:55. | :01:57. | |
by being able to pay for their activity. | :01:58. | :02:01. | |
In 2016, 726 murders were committed in the city, a 19-year high. | :02:02. | :02:08. | |
That's more than the number of murders committed in New York | :02:09. | :02:10. | |
Chicago is a city most famously known as the Windy City. | :02:11. | :02:19. | |
More recently, it has earned a nickname that few residents | :02:20. | :02:22. | |
That's because gun crime is so extreme in some | :02:23. | :02:28. | |
neighbourhoods, they are comparing them to war zones. | :02:29. | :02:35. | |
The issue has received increasingly negative attention in the US, | :02:36. | :02:40. | |
with President Trump tweeting, "If Chicago doesn't fix | :02:41. | :02:44. | |
the horrible carnage going on, I will send in the Feds". | :02:45. | :02:50. | |
But many believe that to fight crime in the city, first, | :02:51. | :02:53. | |
the authorities must understand its causes. | :02:54. | :02:57. | |
Eddie Bocanegra has for years worked to help young people surrounded | :02:58. | :03:01. | |
Now a director of the YMCA, he also serves on the mayor's | :03:02. | :03:07. | |
So this space here that you've got, what do you use this for? | :03:08. | :03:14. | |
So we use this space for a lot of our kids, | :03:15. | :03:17. | |
Many of them who are on probation or parole. | :03:18. | :03:23. | |
More importantly, kids who experience a lot | :03:24. | :03:24. | |
When you see the front page of a paper, saying a 15-year-old | :03:25. | :03:31. | |
person killed someone else, these are the kids. | :03:32. | :03:35. | |
The response from Chicago's Police Department is a new initiative, | :03:36. | :03:45. | |
driven by technology, which aims to predict where crimes | :03:46. | :03:48. | |
The University of Chicago's Urban Labs are assisting the police | :03:49. | :03:59. | |
in its efforts to integrate this technology into its operations. | :04:00. | :04:07. | |
We have a lot of expertise in analysing crime patterns | :04:08. | :04:09. | |
and trends in the city, from years of working with data | :04:10. | :04:12. | |
And so we are leveraging that expertise to really help | :04:13. | :04:18. | |
the Police Department think about where it should be | :04:19. | :04:20. | |
allocating its resources to be most effective. | :04:21. | :04:22. | |
So what kind of data or information is it that the police are providing | :04:23. | :04:25. | |
We have a number of datasets that we work with from them, | :04:26. | :04:30. | |
including data on crime patterns, actual crime incidents, | :04:31. | :04:32. | |
A number of different methods of analysis are used, | :04:33. | :04:39. | |
including machine learning and predictive analytics. | :04:40. | :04:43. | |
This is software which takes large volumes of data and tries | :04:44. | :04:46. | |
These trends can then help predict where a crime might occur next. | :04:47. | :04:55. | |
This is a heat map of homicides in District 7. | :04:56. | :04:58. | |
And we are looking at this year over year, from 2011 to 2016. | :04:59. | :05:02. | |
And basically, what you see on the map is the darker the red, | :05:03. | :05:05. | |
the more concentrated homicides were in a given area. | :05:06. | :05:09. | |
What sort of factors are you finding are influencing crime in these | :05:10. | :05:12. | |
Yeah, so, most of the prediction that we're doing is space-based. | :05:13. | :05:17. | |
So, yeah, it's locations that are nearby that | :05:18. | :05:20. | |
are high-risk locations, like a 24-hour liquor | :05:21. | :05:26. | |
store, a gas station, where people tend to congregate. | :05:27. | :05:28. | |
The weather seems to be playing a very big role in the data. | :05:29. | :05:31. | |
You know, we've just had a beautiful weekend and we just had | :05:32. | :05:34. | |
significantly worse amount of shootings than we had | :05:35. | :05:36. | |
The police are using these predictive tools to inform | :05:37. | :05:39. | |
the deployment of officers and resources to areas | :05:40. | :05:41. | |
where they think crimes are likely to occur. | :05:42. | :05:47. | |
Neighbourhoods in Chicago's West and South Side are some | :05:48. | :05:50. | |
It is these neighbourhoods which have been chosen to test | :05:51. | :05:55. | |
We are just driving through Chicago's South Side now. | :05:56. | :06:02. | |
Now, this is one of the areas which has experienced the highest | :06:03. | :06:05. | |
incidence of violent crime, mainly gun and drug related. | :06:06. | :06:10. | |
To see how all of this different kit works, | :06:11. | :06:12. | |
I'm on my way to a police station which acts as a command | :06:13. | :06:15. | |
centre, bringing all of the technologies together. | :06:16. | :06:19. | |
Heading up the project is Deputy Chief Jonathan Lewen | :06:20. | :06:21. | |
So this is our Strategic Decision Support Center. | :06:22. | :06:29. | |
So this is where you bring all of your different | :06:30. | :06:31. | |
This is the first time that this level of technology | :06:32. | :06:35. | |
integration has been done, not only here, I think, | :06:36. | :06:38. | |
So what can we see on the screens we have got around us? | :06:39. | :06:42. | |
So, all around us are various sensor inputs, cameras, gunshot detection. | :06:43. | :06:44. | |
The screen behind you is something called Hunch Lab, | :06:45. | :06:47. | |
which is a geographic prediction tool that brings a lot of data | :06:48. | :06:51. | |
into a model to predict risk for future violence. | :06:52. | :06:55. | |
So what you are seeing on these little boxes here are areas | :06:56. | :06:58. | |
where the model is recommending that we deploy resources | :06:59. | :07:01. | |
and implement strategies to fight some of the violence | :07:02. | :07:04. | |
And then it is telling us that we should deploy resources, | :07:05. | :07:08. | |
visit businesses, do foot patrol, various tactics. | :07:09. | :07:11. | |
Shot Spotter just very quickly triangulates possible gunshot events | :07:12. | :07:16. | |
using acoustic sensors that are located throughout the district, | :07:17. | :07:19. | |
and it shows the officer exactly where, accurate to within 25 yards, | :07:20. | :07:23. | |
And you can actually play the audio of the gunshot event, | :07:24. | :07:28. | |
So here's an event with nine rounds fired. | :07:29. | :07:31. | |
And in this case, you can see the location is actually | :07:32. | :07:39. | |
the back yard of a house, so that's going to be very accurate. | :07:40. | :07:43. | |
So this is the decision support system and this | :07:44. | :07:45. | |
is where everything comes together in one place. | :07:46. | :07:48. | |
It will soon be available in the hands of officers on smartphones. | :07:49. | :07:51. | |
So in this case, we are looking at a 911 call of a robbery that just | :07:52. | :07:55. | |
There are four cameras within a 300 foot radius of that call. | :07:56. | :08:00. | |
Here is the real-time video from those cameras. | :08:01. | :08:01. | |
These guys here, these are possible suspects, or... | :08:02. | :08:03. | |
These are people that might possibly be involved? | :08:04. | :08:05. | |
How do we know that this is identifying the right people? | :08:06. | :08:09. | |
We find when we test and measure them, that the model's | :08:10. | :08:13. | |
recommendations, because we can backdate it, we can look | :08:14. | :08:16. | |
at a known outcome period and see how it performs. | :08:17. | :08:18. | |
And we know that it's picking the right people because we know | :08:19. | :08:21. | |
But some of this technology is proving to be controversial, | :08:22. | :08:30. | |
It's called the Strategic Subjects List. | :08:31. | :08:33. | |
and locations, this list is concerned with predicting crimes | :08:34. | :08:38. | |
Just like Hunch Lab is a place-based risk model, this is a person-based | :08:39. | :08:50. | |
risk model that is looking at variables such as arrest | :08:51. | :08:54. | |
activity, so have you been arrested for a gun offence in the past? | :08:55. | :08:57. | |
So it's using some crime victim data. | :08:58. | :09:03. | |
Is your trend line in criminal activity increasing or decreasing? | :09:04. | :09:05. | |
What was your age at the time you were last arrested? | :09:06. | :09:08. | |
Nothing about race, nothing about gender, | :09:09. | :09:11. | |
It is using objective measures to determine risk | :09:12. | :09:14. | |
It's basically telling us that this person is 500 times more likely | :09:15. | :09:20. | |
than a member of the general population to be involved | :09:21. | :09:23. | |
in a shooting, either as a victim or an offender. | :09:24. | :09:26. | |
So in here, we can see his affiliations, his gang affiliations. | :09:27. | :09:30. | |
We can see also his, is this his arrest record | :09:31. | :09:33. | |
You can see that he has a weapons arrest. | :09:34. | :09:38. | |
He was arrested here for aggravated battery. | :09:39. | :09:40. | |
So here's a first-degree murder charge. | :09:41. | :09:42. | |
Here's another arrest, this is a narcotics arrest. | :09:43. | :09:46. | |
So the score estimates how much more likely an individual is to be | :09:47. | :09:49. | |
the victim or the perpetrator of a violent crime. | :09:50. | :09:53. | |
The police use this score to inform what they call | :09:54. | :09:55. | |
This is not designed to be a punitive tool. | :09:56. | :10:02. | |
This is used to drive what we call a custom notification process, | :10:03. | :10:05. | |
which is literally a site visit to this subject, to say, | :10:06. | :10:07. | |
"You've come to our attention for these reasons. | :10:08. | :10:09. | |
We want to get you out of the cycle of violence. | :10:10. | :10:12. | |
We can offer you the following social services". | :10:13. | :10:13. | |
Maybe if they have children at home, it would be childcare services. | :10:14. | :10:18. | |
"But also, if you don't leave the cycle of violence | :10:19. | :10:22. | |
and you keep committing crimes, you're going to be subject | :10:23. | :10:25. | |
to enhanced criminal penalties", because you're a repeat gun | :10:26. | :10:27. | |
And can you see why, if police officers go and visit | :10:28. | :10:31. | |
somebody out of the blue, it might seem like they are being | :10:32. | :10:34. | |
Everybody who has a risk score has committed a crime in the past. | :10:35. | :10:38. | |
Otherwise they wouldn't even be in the model. | :10:39. | :10:40. | |
Groups like the American Civil Liberties Union, though, disagree. | :10:41. | :10:42. | |
They aren't happy about the use of some of these technologies. | :10:43. | :10:45. | |
The police showed us a database of people who have been involved | :10:46. | :10:48. | |
in violent crime in the past, and an algorithm which suggests | :10:49. | :10:51. | |
if and when they might again be involved in a violent crime. | :10:52. | :10:55. | |
Oftentimes in large numbers, along with a number | :10:56. | :11:03. | |
But what they won't say is what social services are offering. | :11:04. | :11:09. | |
Is it just them or is it their entire family? | :11:10. | :11:15. | |
What is the success rate once that occurs? | :11:16. | :11:17. | |
The fact is, is that most of the people who are charged for... | :11:18. | :11:21. | |
You know, if you take two people who are arrested | :11:22. | :11:23. | |
for a simple drug possession, if one is white and one | :11:24. | :11:25. | |
is African-American, the African-American is far more | :11:26. | :11:27. | |
likely to be charged, maybe even convicted. | :11:28. | :11:29. | |
We have seen that there has been, you know, in essence, | :11:30. | :11:34. | |
a "once convicted, always guilty" sort of theme that | :11:35. | :11:36. | |
While there might be disagreements about the use of this technology, | :11:37. | :11:44. | |
everybody I spoke to had similar ideas about an ultimate | :11:45. | :11:46. | |
solution to tackling violent crime in Chicago. | :11:47. | :11:52. | |
It's got to be every, everybody that's a stakeholder | :11:53. | :11:55. | |
in this coming together to solve the problem. | :11:56. | :12:02. | |
What is really needed across this city is a commitment | :12:03. | :12:05. | |
I think a lot of it has to do with preventing, with healing, | :12:06. | :12:09. | |
and creating a space where individuals can civically | :12:10. | :12:11. | |
Well, that was Marc and this is Marc. | :12:12. | :12:18. | |
The police said that the list is composed from people that have | :12:19. | :12:26. | |
committed violent crimes in the entire State of Illinois. | :12:27. | :12:30. | |
That is the prerequisite for getting on? | :12:31. | :12:32. | |
They only consider people who have previously committed crimes? | :12:33. | :12:35. | |
Yeah, if you've already committed a crime, especially a violent | :12:36. | :12:38. | |
crime, you might end up on the Strategic Subjects List. | :12:39. | :12:41. | |
Well, interestingly, earlier this week I spoke to DJ Patil. | :12:42. | :12:45. | |
Now, until recently, he was President Obama's | :12:46. | :12:46. | |
I asked him about this and this is what he said. | :12:47. | :12:52. | |
Many, many deep concerns about the presence of these things. | :12:53. | :12:56. | |
The fundamental one is the transparency of the algorithm. | :12:57. | :13:01. | |
Very recently in the US, we had a case that was | :13:02. | :13:04. | |
What was being used was a number of variables that | :13:05. | :13:08. | |
And specifically, your race, your background, | :13:09. | :13:11. | |
You know, these datasets of offenders, we also know, | :13:12. | :13:16. | |
have oftentimes have an increased bias because of the way police | :13:17. | :13:18. | |
enforcement happens, or is it happening in one | :13:19. | :13:24. | |
neighbourhood versus another neighbourhood? | :13:25. | :13:25. | |
Now, do I think there is merit in the use of this data? | :13:26. | :13:28. | |
The way we saw it, and one of the reasons why we created | :13:29. | :13:32. | |
the White House Data-Driven Justice Initiative, is that we realised | :13:33. | :13:35. | |
that, hey, a huge amount of these people have other problems | :13:36. | :13:38. | |
It was the week in which we learned that Disney has filed a patent | :13:39. | :13:47. | |
Minecraft said it would allow content creators | :13:48. | :13:52. | |
And Amazon promised to refund up to $70 million to parents whose | :13:53. | :13:58. | |
children made in-app purchases without their consent. | :13:59. | :14:04. | |
It seems some hackers like waking up Texans in the middle of the night. | :14:05. | :14:10. | |
All 156 tornado warning sirens in Dallas were turned on at once. | :14:11. | :14:15. | |
Officials haven't yet tracked down the person responsible | :14:16. | :14:19. | |
for the midnight hoo-ha but say they were activated via radio | :14:20. | :14:23. | |
An oceangoing robotic snake has popped up in Southampton. | :14:24. | :14:30. | |
The Eelume has cameras and sensors so it can perform maintenance | :14:31. | :14:33. | |
Could the boys in blue be about to go green? | :14:34. | :14:39. | |
Behold, the Ford Police Responder hybrid sedan. | :14:40. | :14:41. | |
The eco-friendly car features anti-stab plates in the front seat. | :14:42. | :14:47. | |
But hang on, it's slower than the petrol model | :14:48. | :14:50. | |
And finally this week, little green people in your living room. | :14:51. | :14:58. | |
Globacore has released HoloLens, a virtual reality homage | :14:59. | :15:02. | |
Guide your green-haired friends to safety across your worldly goods. | :15:03. | :15:11. | |
Just don't expect a refund for either in-app or | :15:12. | :15:13. | |
And for three days, home to four fundraising friends. | :15:14. | :15:39. | |
They will traverse 100 kilometres over mountains and frozen lakes | :15:40. | :15:43. | |
in temperatures as low as -30 Celsius. | :15:44. | :15:47. | |
Group leader James' daughter suffers from mitochondrial disease, | :15:48. | :15:52. | |
and this trek is to raise money for a charity that helps | :15:53. | :15:55. | |
children with the condition and their families. | :15:56. | :15:59. | |
These guys are all senior tech geeks by day, so to help | :16:00. | :16:02. | |
them on their quest, we've equipped them with some | :16:03. | :16:04. | |
Here we have all our technical equipment. | :16:05. | :16:11. | |
Some people think this is a lunchbox, but it's not. | :16:12. | :16:14. | |
But which of these extreme weather gubbins will actually do the job | :16:15. | :16:21. | |
Suited, booted and sufficiently powered up, they head off | :16:22. | :16:27. | |
One of the most vital gadgets we're using is this satellite | :16:28. | :16:43. | |
So it's going to send Connor's wife, my wife, John's wife, | :16:44. | :16:49. | |
Tuka's girlfriend a text message to say everything's OK. | :16:50. | :16:52. | |
That's going to keep some of our tech kit that we've got | :16:53. | :16:57. | |
in here from freezing up, particularly a load of battery | :16:58. | :16:59. | |
We've armed ourselves with a whole load of different battery packs. | :17:00. | :17:03. | |
This one here is the RAVPower, and it's designed to be worn. | :17:04. | :17:06. | |
Here we have the ZeroLemon power bank. | :17:07. | :17:08. | |
It is a little bit heavy but then again, it packs 30,000 milliamps. | :17:09. | :17:15. | |
I'm going to hide the Nomad Tile trackable battery pack from James, | :17:16. | :17:20. | |
Wow, maybe I should have hidden it better. | :17:21. | :17:44. | |
Tuka, I think we made it just in time, my friend. | :17:45. | :17:56. | |
Very happy to be at this wilderness hut that we just got to. | :17:57. | :18:07. | |
I'm trying out the heated insoles today. | :18:08. | :18:10. | |
We've got the GoPro mounted to the skis. | :18:11. | :18:25. | |
We're headed off in that direction, about 34K, I think, today. | :18:26. | :18:29. | |
The little GoPro Hero5 Session was left out overnight. | :18:30. | :18:34. | |
I thought we'd killed it and I went and kind of scraped the ice | :18:35. | :18:37. | |
off it in the morning, pressed the button, boom, | :18:38. | :18:39. | |
So it's like, OK, that's seriously cool. | :18:40. | :18:45. | |
I've been wearing a Finnish smart watch that's been | :18:46. | :18:47. | |
As well as tracking your location, dropping a breadcrumb of GPS | :18:48. | :18:51. | |
coordinates as you move, so once you've done something, | :18:52. | :18:53. | |
I'm just going to save that up on the touchscreen. | :18:54. | :19:03. | |
The biggest thing I've found was that it gives | :19:04. | :19:05. | |
you so much encouragement, you know, when you're | :19:06. | :19:07. | |
wrecked and you're about to die after 12 hours. | :19:08. | :19:10. | |
The heated soles in the boot are working quite nicely, | :19:11. | :19:12. | |
So here we have the Blaze Spark infrared lens. | :19:13. | :19:19. | |
Keen to capture the Northern Lights, Connor's got a smartphone | :19:20. | :19:22. | |
You download a app called Blaze Spark. | :19:23. | :19:30. | |
Very simple to load, and once you connect the camera, | :19:31. | :19:33. | |
the app automatically starts and your phone becomes | :19:34. | :19:35. | |
OK, because we had to lug quite a lot of stuff across the Arctic, | :19:36. | :19:41. | |
there's some bits of kit we didn't take with us. | :19:42. | :19:44. | |
It's got a fan inside it, so as you light the fire, | :19:45. | :19:48. | |
it blows air through the bottom, causes it to really combust quickly. | :19:49. | :19:53. | |
It's also got an integrated battery pack. | :19:54. | :19:55. | |
And it actually converts heat into electricity and keeps | :19:56. | :19:58. | |
So this thing has got a USB slot and the phone is on charge. | :19:59. | :20:05. | |
This hand has got a heated glove on it. | :20:06. | :20:17. | |
It's quite a lot of weight you're carrying and you can only charge | :20:18. | :20:28. | |
them up from the mains, so if, like us, you're trekking | :20:29. | :20:31. | |
out in the wilderness for a few days, they are not | :20:32. | :20:33. | |
The gloves or the socks, I'll take the gloves. | :20:34. | :20:43. | |
Invent a great glove, because that would, I'd buy | :20:44. | :20:45. | |
Filling our water bucket for boiling. | :20:46. | :20:53. | |
We don't want to go and fall in there because | :20:54. | :20:56. | |
Sadly, Connor didn't manage to capture the Northern Lights | :20:57. | :21:03. | |
on his night-vision cam but he did take these beautiful | :21:04. | :21:05. | |
My activity levels, even though I've been trekking | :21:06. | :21:22. | |
for two days solidly, it only gives me 83 out of 100! | :21:23. | :21:25. | |
It keeps you on your toes, knowing how much sleep you need. | :21:26. | :21:31. | |
It tells you how much REM sleep you had, how much light sleep | :21:32. | :21:34. | |
you had and how much deep sleep you had. | :21:35. | :21:37. | |
And it records, therefore, on the basis of that | :21:38. | :21:39. | |
and the day's activities, the previous day's activities | :21:40. | :21:42. | |
When you know you've got that measurement happening all the time, | :21:43. | :21:48. | |
it reminds you to look after yourself and | :21:49. | :21:50. | |
I've been wearing thermic heated insoles now for a couple of days. | :21:51. | :21:56. | |
The cable coming out the back of the boot gave me horrendous | :21:57. | :21:59. | |
So I cut the cable off and just turned them into normal insoles. | :22:00. | :22:06. | |
This is the Snow Lizard, fully waterproof, solar | :22:07. | :22:08. | |
Even though your phone is very precious and this | :22:09. | :22:16. | |
one is to me, for sure, you can do that. | :22:17. | :22:18. | |
And they are diving around in the snow. | :22:19. | :22:26. | |
We still have 21 kilometres to go on day three. | :22:27. | :22:34. | |
So far, the crew has been really jolly and talkative. | :22:35. | :22:44. | |
For some reason, there seems to be a little less talking now | :22:45. | :22:49. | |
Blistered and bloody-toed, we approach the finishing line. | :22:50. | :22:58. | |
That was the hardest thing I've ever done. | :22:59. | :23:05. | |
You don't do this to feel warm and comfortable and cosy, actually. | :23:06. | :23:13. | |
You get out to do something like this to raise the money | :23:14. | :23:16. | |
that we have been trying to raise for the Lilly Foundation but also, | :23:17. | :23:20. | |
And tech can take you so far, but ultimately, it's your brain | :23:21. | :23:24. | |
and your endurance and so on that can take you all the way. | :23:25. | :23:28. | |
But I would still like some heated gloves. | :23:29. | :23:37. | |
Wow, what a great bunch of guys and what a great story, too, | :23:38. | :23:41. | |
especially considering they filmed that all themselves. | :23:42. | :23:44. | |
The good news is that so far, they've raised over ?17,000 | :23:45. | :23:47. | |
for the Lilly Foundation and we wish them and James' daughter, | :23:48. | :23:50. | |
For more information on their story and everything else you've seen | :23:51. | :23:54. | |
in this week's programme, check out Twitter. | :23:55. | :23:57. | |
Time to get up to date with how we will see the rest of the day | :23:58. | :24:33. | |
unfolding across the British Isles. In mixture of sunny spells and | :24:34. | :24:34. |