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