Big Data for Social Good: The developers' journey


Watch this live chat with the teams and participants from the #Hadoop4Good challenge to learn why they chose their projects, hear some of their challenges and delve deeper into the story behind their applications. 

See all the participant projects and join us for our group two discussion.

Don't miss the winner announcement!


0:00 do 0:05 companies are love yeah and we have been 0:08 having lotsa fun talking through and learning about the inner such a good 0:12 challenge 0:12 and we're very exciting point right now which is everybody has shared your 0:17 submission doesn't March 1st and we will be announcing the winners 0:20 on the 15th April so over the course the next two weeks will hope this session 0:23 will have another session like this next Wednesday 0:26 and then all in one they will have to make the announcement what 0:29 was what want competition and we are excited to be able to share that but 0:33 didn't been so far 0:34 let's spend a couple of minutes just talking through each all 0:38 or some of the innovations this text books that are joining us today 0:41 do with that what's your turn it over to bed gonna talk to us about his away 0:45 since that mission 0:46 and what all was motivation and inspiration from the findings 0:50 and so we couldn't you then okay so 0:54 over here tender chicago's Open Government Act makes 0:58 which is a group where technologists and civic-minded 1:01 it together it how it you see also: 1:05 that's they're all costs an area that request there s 1:09 she and %uh she does see 1:12 regularly causes the numbers a problem that 1:15 the commercial developments that are say 1:19 is trying to you actually intriguing what's ours is 1:22 neighborhoods meet their services and it looks as hell 1:27 how to our up may have more sectors 1:31 it's worth the signage is he database 1:35 it is a the hell open government portal 1:38 up this and so that got me thinking I 1:42 party here and we talk about really solution around that 1:46 an asshole looks Oasis or 1:49 so what we did look at all this is likes this is 1:53 city is she going back to 2006 and did eighty 1:57 geospatial ass census tracts and really we're trying to do this 2:01 like I'll across a over it this is 2:04 types where r as urs 'em 2:08 where what services is this supports the Syrian 2:12 up 2:12 ecstasy and then as they were clear around the began to see it was a 2:16 onset oppose a critical business so 2:19 the only one on the site service on the route population 2:23 and so those players bien sur 2:27 date more than that say issue 2:30 those and I'll I should ask followup questions but keep going about 2:36 going to do next which is up 2:39 I have context s 2:42 groups that do SAS social enterprises 2:46 and where he worked with them to see if you use 2:49 worse Israel this is much easier 2:52 but this is going star stretch 12 2:56 make sure that as this is I that it's a 3:00 and sorry deducted with this is a test 3:03 leaks are hits a say Chicago's 3:07 planning sets 3:10 as a stitch that great 3:14 so what did you can I just now four-mile development perspective 3:18 what did you think being able to leverage would you allow did you hear 3:22 that you may be couldn't have done otherwise 3:24 so yearly be able to 3:27 for sale %uh Clara schools the hat was very helpful 3:31 forehead team we all works after hours and on weekends 3:35 and so we %uh where the usual I'll the tools are you sure 3:39 the office clarissa as a group you're doing this 3:43 and it also this or throwing ideas 3:46 trying out seeing where it goes I'll and then just for 3:50 on a to a dataset 3:53 in a way that to we would have though English ace 3:56 well 4:00 well that's good I think sometimes the idea how the fact that you can to try 4:04 things out and do things a little bit more ad hoc is something we will people 4:07 can do with the new 4:08 so that the experience you have also that's that certain goodness mmm 4:12 so are there any question their books purchased many in the group hezballah 4:16 this point but are there any question before we move on to our next door 4:24 all right well we really appreciate 4:26 when your contribution we look well worded to sharing our 4:30 even more those details with boat as you guys do more work and I certainly 4:34 certainly continue to share the community here so 4:37 okay for the next presenter I'll why don't we go 4:41 to thought not when you are you can use yes 4:44 we can do. yes okay cool so I can you tell us a little bit about where are the 4:49 world s yes arm hi everybody 4:53 on who does its our locations that lack access to healthy food options 4:57 and then I'll most a lot researchers have 5:01 try to link this to help issues in those areas some 5:05 a lotta data on this issue has been one dimensional 5:08 particularly at a distance to be grocery stores are examined so what I tried to 5:12 do. 5:12 add more facets up the issue onto my map 5:16 stew basically be realized the most the tasaday 5:20 data um regarding the issue and I hope that that's useful to like 5:24 health care advocates say no public health officials and all that stuff so 5:30 that's really good 5:31 well can you tell us a little bit more about them you're finding so what did 5:34 you 5:34 what did you find out how to use the internet to find 5:38 or yep sure arm so 5:41 this was motivated because what I work in LA I felt like I was in a food other 5:45 I'll and I thought some metrics on with the US government 5:48 said that I didn't work in a few dozen so I wanted to 5:51 you know prove it wrong 5:54 up so what if I'm not was that lot on the southern regions a ballet 5:58 are in particular impoverished areas 6:02 and the distance to the grocery stores are 6:05 actually that bad but the its the density of 6:09 other food options such as fast food restaurants not set that make 6:12 um going to grocery store making you know to be less appealing I'll 6:16 and that's what down on a dip was really helpful just because 6:19 there are a lotta on readily available datasets are 6:23 grocery stores and all that stuff so what up to do was call for that data and 6:27 because I'm calling for at the data comes in all types a format and 6:30 is all tied to weird strange issues and all that stuff so 6:34 I'm I duke was really useful for 6:36 munching the data you know this cleaning it up making sure all the college 6:39 have the right stuff in I was able to join tables make sure 6:42 I'm the data all talk to each other so that I could present and one 6:47 nice format now that's good 6:52 so I do best accelerated that process for you complex you were able to sleep a 6:56 little update is that you wouldn't have otherwise been able to consider because 6:59 you had 6:59 the crop you want call call a place to get there may be appropriate in a 7:04 different type to date at different structures 7:06 definitely owe so much to hope for that okay great 7:12 saw it interesting so well what you end up finding out is that it wasn't at the 7:16 location 7:17 their ability to get to a grocery store was really a problem right 7:21 right right its its the lock-up options arm 7:24 back come from um well one example I have 7:28 is arm cock start 7:31 out Long Beach arm without area down in town 7:35 Los Angeles arm for example that area 7:38 has a very stratified on um 7:41 divers income group there's 7:44 it a lot of people in the impoverished side in this some on this a good 7:48 population 7:48 a I'll very well of folks are 7:52 so in a particular zip code there were 7:55 only one grocer there's only one grocery store I'll 7:58 and on Yelp that the to does a two dollar sign grocery store 8:02 I'll my dataset contains I'm I'm you'll on 8:06 reporter the dollar sign ratings the grocery stores 8:09 and just to give you like a a good metric 8:13 up hope which would be a three dollar sign um 8:16 kinda grocery store and thats you know were Whole Foods is known for being a 8:19 little bit upscale 8:21 so wherein as the code I'm has a 8:24 grocery store doesn't mean that everyone has access to it I'm could be it 8:27 a grocery store that's you know much more I'm expensive or something it 8:31 serves 8:32 its targeted towards one sector up the income group 8:35 so arm I noticed that not it's not necessary if there's a grocery store not 8:40 but this a grocery store 8:42 if is it affordable to the residence in that zip code something like that so 8:47 the question you commit to Russia I think it's gonna cost anyway but how do 8:50 you see this app being used in the real world you know how could you see this 8:53 really evolving into hopes 8:55 yeah I think that consumers and residents in the city of a million other 8:58 cities could take advantage of sure 9:00 on a great question Tasha um I know that um 9:04 recently um there'd been some 9:07 on stock do it done in City Hall where 9:10 the people are trying to pass rules where I'm 9:14 the saying area um the word 9:17 they would not allow AR fast food restaurant to be opened or something and 9:21 they're trying to make 9:22 incentives for grocery stores to be open their 9:25 I'm so any sense I want my apt to be used in that way like I what public 9:30 officials 9:30 to use it to identify places where they should you know 9:34 introduced some sort and sent the producer to be opened their or 9:38 just call because a new fast food restaurants being open their 9:42 on so basically just something to motivate some political 9:46 on development and laws and 9:49 all that stuff right just thought you know providing better access to better 9:53 boot everybody exactly exactly hopefully somebody will 9:57 use it to be there 00 identify alright this is this place is at risk 10:01 and then using the app will be like all-white is there a large 10:04 impoverished population is just a distance issue 10:08 arts something like that what's really interesting you can see how many 10:14 know a lot of metropolitan areas this could be something that's really 10:17 applicable and useful hopes to be all 10:19 to look at so we appreciate you doing that research and look forward to seeing 10:22 wells 10:23 lies ahead thank you okay great 10:27 are things that so I hold my hand right you going make sure you have any other 10:32 questions were yeah yeah I think Jason 10:39 pet alluded to what you're suggesting so would be interesting to see if 10:42 will desert along with poverty areas are lowering heart problems or something 10:46 and what are those correlations that it seemed like that 10:50 that's also what you're trying to get almost like this point if that still new 10:54 that's already indicator what areas are these guys 10:57 you must like most prepared watchin alright 11:04 let's see I think that's good I will not thanks very much we appreciate it and we 11:10 look forward to hearing more about your food desert 11:12 discoveries thanks very much for your participation 11:15 thank you alright cool so again anybody ask questions we will take questions at 11:20 the end as well so we'll create hello 11:22 I'm at any point in the queue name my every move over to Matt Matt I see that 11:26 you're on a 11:27 I'm you that's a great thing and you have 11:30 watching a loose Brett at saw as I'm here inside and I we can't wait to hear 11:34 about that little 11:36 obviously this year was crazy Sol Trujillo welcome all 11:40 and I've included arm someone to the developers that worked on this project 11:44 with me 11:45 out regale are so he can kind of get a little bit more 11:48 technical insight as far as what this project was and how we went about it 11:52 you know first others read motivation so 11:56 up there was a big problem tens of thousands people die every year 12:00 grade CC and there's really no 12:03 I did I guess consumer endpoint with this data 12:07 it's really messy right now and it's it on 12:10 the all Assoc we our goal here 12:13 in watching the spread was to bring this down to a county-level on 12:17 so that people to get more granular insight into where the flu is 12:20 up and really it was it was a look towards 12:24 possibly step extending this to other disease states 12:28 and on to get experts involved 12:31 eventually but 12:36 so really kind of how we how we develop this 12:41 on project through a series of kinda brainstorming exercises and and part of 12:45 our team worker 12:46 by students from Purdue University on 12:50 me we can be ingested a lot different data 12:54 based around the flu armed CDC down a 12:57 on a train air transportation down to ground transportation data population 13:02 density 13:02 I N and social that as well are from Twitter 13:06 to kinda 13:07 those by get that granular look at where the flu currently is 13:11 but then running it through gonna be regression model on 13:15 to figure out where it will be over the next five days 13:19 so 13:23 armenian Font perspective can you tell us a little bit more just pull up here 13:27 that might be new or two what you guys actually I'm what did you learn 13:31 and what did you really find out as you start to sense at this present for that 13:35 period time 13:36 yet so we spent most of our time 13:40 a as opposed to developing the app we actually spent most part I'm trying to 13:44 figure out what was important in this model 13:47 on and one of the hardest things about this this project was there are 13:52 Armenian the main correlation we thought would exist 13:55 and the main important factor which was temperature that we thought would be 13:58 important in predicting the flu actually was not important 14:01 and it wasn't something that we ended up including in our final model 14:05 on soaps we went on a lot of different has 14:08 population density is a big factor in how the flu spreads obviously 14:13 um one's looking index or how the slough the 14:16 physical mechanism I'll spreads which is it somebody costs 14:21 or sneezes with any a six-week radius of another person 14:24 %uh that gives insight into why that's true 14:27 also travel data and talking about 14:31 specifically round trip today being important 14:34 and that's how how counties in Iraq and other solution would spread between 14:39 county's 14:40 and in the final model om which and it was really interesting us the 14:44 these factors became the most important well that's really interesting still 14:49 well-protected perspective as you if you were to take this forward 14:52 you think you have some other right variables in play it would actually 14:55 allow you to settle 14:57 here the variables that are actually matter when you think was gonna spread 15:01 and you can make britain-based 15:02 former well thanks now 15:05 also but I think some further research 15:09 into you can have the variations on the flu and how they interact 15:12 on kinda geographical level would-be arm 15:16 kinda the driving force in in the advancement of this model 15:22 okay and idk I just 15:24 his guy Edgar all I 15:28 yeah as it stands now are our model is admittedly simple as we learned more 15:31 about the flu and about the fact that there are several strains 15:34 strains the clue and every year a the 15:37 it's a completely new strand that's coming out om 15:41 and and and a lot of other facts we had some people from Purdue that were able 15:45 to inform us on 15:46 on some these facts were looks as we learned about this week and realize that 15:49 we were in a bit over our heads in terms ok getting a really good prediction 15:53 model for the flu 15:54 %uh what we produced right now will 15:58 in on some level predict the clue and that's that's 16:01 true so when the flu season starts it'll be able to show where it's going to hit 16:05 most 16:06 and a what should be happening who 16:09 over the next five days our ambitions are to bring in 16:14 some more qualified individuals to help us build out really good 16:17 model with this as a first step 16:20 so that's that's so that was my next question in turns out 16:23 your plans for taking this research forward and although obviously it was 16:27 for the competition 16:27 whole scene is putting out as you get ready for having this information and 16:32 net while gmail will connect your cynicism already doesn't look or or 16:36 where you know your in and we both worked for 16:40 a big data consultancy and then we're up as we as we mentioned 16:45 involved with a bunch of students from Purdue University and one of our primary 16:49 focus is 16:50 at at Percy oh the organization we work in is 16:53 in I income medical prediction and in dealing with medical organizations 16:58 so arm connecting to those kinda resources that we have on a client bases 17:04 will provide further insight as far as how we 17:07 how we properly to in this model in 17:10 in provide further insight to to the end users 17:13 on locally we've seen by a 17:17 a fairly a pleasant on 17:20 kinda reception on this model by 17:23 we were on kinda the local news in and they actually had it on a big TV behind 17:27 him 17:28 I talking about the model is as it was kind of like the weather 17:32 map non so I think there's 17:35 I think there's a significant number of people that are interested 17:38 in this research and and we can can it continue the conversation moving forward 17:43 okay great yeah we saw a news clip that was so totally great to see it was 17:48 welcome news to sell 17:49 that dole will miss you all the best as you continue that work in research and 17:53 look forward to hearing more about it 17:55 right thanks opened or what right any questions feel free to chat a man 17:59 when we can take them here at the end the call alright so why don't we move 18:04 over to 18:05 David I David do you wanna tell us 18:08 you know you should be on me screw your there you go 18:12 you want to hold water Brady Bunch work what million 18:16 be found lol will be great okay sure 18:19 so Brady Bunch Tom gonna be really catchy am 18:23 year some memories on the show as well but basically 18:27 to focus our analysis was renewed that 18:31 his focus on New York City renewed that classes the register buses 18:36 was decreasing and from services that we've seen most of it was due to long 18:41 wait times 18:42 so we 2010 their drivers a long wait times was bus punching 18:46 and that's what we wanted to study but to kind of to gonna take a step back 18:50 and to kind of go back 18:53 into demotivational wiry you want to look at this 18:57 this problem was that we notice in New York City was that the 19:02 the head there was a transportation 19:05 issue happening so there's a lot of people in New York City 19:08 mo said and did not oncars over 50 percent in our own cars 19:14 and also that rely on public transportation simeon motor 19:17 transportation in New York City's subways 19:20 and more and more each year more and more people 19:24 taking subway is and it's happening so fast that 19:27 the reader may increase in ridership was some ways is outpacing the population 19:31 growth in New York City 19:32 so business ever Impex really 19:35 being established date1 is that commute is a scene like over crowding on the 19:39 subways 19:40 you know it's it's so packed to the brim inspected almost a digital platform 19:44 and you're thinking okay like a few years from now has a population 19:48 increases 19:49 you know this can be capacity issue with the subways so we thought was they 19:54 okay we probably rely on alternative 19:57 motor transportations and what we saw was that those opportunities 20:02 and buses I your bass is the networks are pretty extensively covers all the 20:07 five boroughs in New York City 20:08 however to write it the ridership has been decreasing sweet 20:12 Syria seeing that it's being and the utilize 20:15 and where we when we 20:18 dug a little bit deeper we tried to look into it you were people saying oh well 20:23 passes 20:23 the main thing was that the buses were unreliable 20:27 and peeling the onion a little bit deeper 20:30 Reese we saw a lot of people saying that you know is mostly due to long wait 20:34 times 20:34 I'm so with a lot quicker buses yet wait a long time 20:38 so what are the drivers every thought and what are the common issues have any 20:42 bus transportation is I'm buses coming in punching 20:46 passes Cummins coming in bunches the two buses arriving at the same time 20:51 so that's when announces focus on Lake 20:54 if you want to do whatever we times combat 20:58 don't let em in New York City where would be to wait times that I would 21:01 expect 21:02 taking a bus how often does bus benching happen 21:06 and wind as when best bunching happens 21:09 how does that impact wait time so that was kinda what was wrong 21:13 om thought about how 21:16 announces the products or even a bit long winded but coffee that got 21:20 that's the question know that's good so in your case 21:24 so obviously reading transportation dinner hosted in your what other pieces 21:28 of information that will let you go 21:29 allow you to be you bring your and use as your 21:33 I'll you know baseline for driving us or insight and analysis are all times in 21:38 what could be done with want to help people get their you want but the storm 21:41 China so worried we mostly focused on transportation dear 21:46 just because com 21:49 we were measuring did because it was pretty extensive like the way to MTA 21:54 is checking the bus as they caught two passes in the five boroughs are being 21:58 tracked 21:58 Micek every 30 seconds its route idea I'm so 22:02 show try now says is pretty focused like he was 22:05 measuring we time so the buses I am 22:08 but in that's in in itself is pretty complex 22:11 because we needed to estimate when the buses would arrive at each stop 22:16 the only information that we got from the data itself 22:20 was I'm where the buses were 22:23 each interval each 30-second though and how close they were to each bust-up 22:27 but not giving II direct information when they actually write a bust-up 22:33 so we had to do was will use who was that this is the message Saturday this 22:36 is a massive data set 22:38 because every 30 seconds for the buses combo what how do you 22:42 on Alex Ryan Dube was he worked to help us with was 22:45 newly created this big secret tables and I am 22:50 you're using seizing how only jurisdiction everything we able to 22:54 figure out what you when buses 22:56 on estimate when the buses were able to reach to bus stops 22:59 com so you transportation it we only use transportation data but it's pretty 23:03 massive 23:04 but I do but he will work to give us the platform 23:07 order to SB were two I'll we find the data 23:12 lot quicker okay I'll writes a similar with Bennett also talked about 23:16 think it was Ben beginning but he was able to sort you know a rate on 23:19 something that 23:20 possible questions that he was asking Arsenal salts or rated 23:24 well that's great okay also hold on 23:27 I did we really go to keep their any other questions you want to ask before 23:31 we get to the 23:32 next presenter and I think we're going to ask you the same question that we've 23:39 been kinda going thru 23:40 with everybody so how do you see this going well was network that you intend 23:44 to 23:44 to take ordered and see how it might actually apply 23:48 my way that happen ok scanned using a city or how do you see it 23:51 14 someone there i think is a few potential users but I think the 23:55 immediate potential uses for 23:58 anti-aids 28 itself from a management perspective 24:02 because what we saw was interesting was that we pick the 24:05 is this round in Brooklyn he found out that 24:08 I am you know best bench was pretty prevail in late one out of five buses 24:13 came in in and bunches and what would happen is that 24:17 every time a bus would come in bunches it would double to wait time 24:20 so I can imagine myself as a commuter you know it's you know waiting a long 24:24 time and I see two buses come at the same time 24:27 it could be I'll it could get a new nose a little bit 24:30 Creek but where we see it happening is that you know this is 24:34 real time information every 30 seconds this is being produced 24:37 and your you can basically 24:40 see ahead of time when buses are studying a bunch together 24:44 seeing kinda managed at headway so mama control their 24:48 homer manager Nancy a I can see went to Best deciding to get closer to 24:54 together it and I can say no to the bus travel to say he know maybe 24:58 my once stood on a little bit if that give that bass ahead 25:02 a bit more headway in that way they can kinda distribute 25:06 the kinda medicine headway 25:09 the second use cases in more strategically 25:13 they can start freaking out the key which routes have more bass budgeting 25:17 enriched bus stops impact bass punching you know it could be major intersections 25:22 where does 25:22 have slower traffic are more trash I'm on traffic going on 25:27 so then they can start break and that route into %um 25:31 I think the big into select passos's so 25:34 consider stopping at every stop you can stop at Limited stops you stop and mark 25:38 the major bus stops 25:40 net weight is less home delays on the way 25:43 so does this mean immediate impact so we can see straight away okay great let 25:49 super interesting and he had absolutely has applicable Indian 25:52 and your hopefully drives efficiency should you take it forward 25:56 with the MTA New York and other cities so also 25:59 didn't thank you so much for that update and I we really appreciate the 26:02 contributions that to the challenge 26:04 thank you hurried there any other questions but did you guys 26:08 at Chatham and will take maybe 26:09 and we do have another question for the blue balled will do that at the end 26:12 why don't we go over to Tyler on on Tyler 26:16 I there you go and Tyler is going to Pauls 26:20 or husband sure all happen research what you did there 26:24 why would you want to hear about its already at all okay thanks good to see 26:28 you again 26:29 a white walls so 26:32 with with future per week and we started with a simple idea 26:35 you know everybody in the team we go to you go to restaurants 26:38 it's really easy to see what's going on in the dining room in going out there is 26:42 a letter 26:42 view this you know there's maybe TripAdvisor but 26:46 it's really hard to know what's going on in the kitchen a 26:50 and so you know starting with that premise for the well you know why is it 26:54 so difficult why 26:55 there's so much information and it out there why I is that really a problem 26:59 and then so use in boston's Open Data Portal 27:02 we're able to access that data bring it into 27:06 you know IBM clinics in to do and 27:09 we're really fortunate for the Boston tote because they have this one call on 27:12 this one that you called parity 27:14 and that allowed us to link up all like you know five different datasets and 27:18 really give us this kinda hopin' sure 27:20 %uh what's going on you know in the kitchen at these restaurants 27:24 and then a so we you know in just in use in pink sheets: 27:28 a which is be release easy cut down budget I'm 27:32 and then in terms of and I just jamming on the data and figured out what was 27:36 going on without actually are 27:37 exit both be really helpful no you people the team in the sequel background 27:42 that kinda 27:43 you know here's my vacations here's what's going on record as saying 27:46 and then that allowed us to get to can avail just a minimal number columns that 27:52 we could use for our future for application 27:55 %uh and so once we had that you know minimal at you it's a 27:58 if you up then we were able to generate these grades 28:02 for each restaurant based on their 28:05 you know five-year food inspection history I 28:09 and then kinda but I'm too nice visualization based on something that we 28:12 thought 28:13 most people could relate to so since most people had a report card 28:17 that's very relatable they know how that feels 28:19 let's give this restaurant the report card and then 28:22 you know I showed my mom she likes the a so 28:25 a yeah is a 28:29 let me answer question also legal 28:32 well and it's good it's nice to know that you had yeltsin's itself bringing 28:36 what we think will be cheats for you any information you're looking 28:40 were and I think the report card thing not exactly a very small minority 28:44 all what you might get right place there my understanding 28:47 or your Google maybe one somewhere 28:50 so how do you see that going our 28:54 so he is this something that you you plan on taking power is it something 28:57 that might use in Boston 28:59 what what do you think yes so I mean we've actually we've already had some 29:02 users initially we've already been able to 29:05 are get some feedback we've water in Starbucks in just ask people what they 29:09 saw 29:09 are and a so the city bus into spending 29:14 so you know excommunicated we have a meeting 29:18 on Friday morning with the Commissioner who is overseeing these like 29:21 inspectional services 29:23 and a you know he's really kinda put himself out there is a resource for this 29:27 team to 29:28 take a look at our rupert and kinda 29:31 you know is this the best way to greatness restaurants because you know 29:34 the serviceable 29:35 people report cards and you know when designing the end up we really wanted 29:40 to we note we know our consumer smirk and so we want to 29:44 show this is a great which isn't here yet but we also want them to be able to 29:47 go to individual level data 29:49 economic decisions for themselves and so speaking with miss your own 29:52 on Friday will definitely help a and it's a fantastic he's made himself 29:57 available 29:57 in the same a but also you know kinda balancing that with 30:01 that's where consumer and change to empower that consumers so they can make 30:05 good decisions for themselves 30:07 okay great well that's really interesting and certainly something you 30:11 can see what the water or click really sell 30:14 that saw that interestingly with you the best what the most conversations you 30:17 have to keep us posted 30:18 yeah if I'm officer thing everybody wants nor all traveling around the 30:22 router and use it easy one 30:23 holtz's this but i wanna go or don't they are while holding a reason why 30:27 yet so there's been success and I believe New York and San Francisco so 30:31 hopefully Boston sex 30:32 what would be a very good thing your 30:36 okay cool hang on Tyler where you go much do you have questions for you and 30:41 again will take questions at the end for folks that have them 30:44 well what's year more 30:47 won't who are or cool and 30:50 questions but again if you have any others apparently chatty man 30:54 and then let's go on to our last up presenter 30:58 nets and morning and we were already yeltsin you thank you Tyler 31:03 and let fault let me talk about the city bike 31:06 happen but what you're looking to discern what you learned yeah 31:10 all hi Ruth are the one and thanks for going to support him to get this angle 31:15 to handle 31:17 lot of the month fling that on with the phone calls handled 31:20 so fine and I could make it to the Hankel on 31:23 thank you very much for providing this so IBM blue mix black homeless it 31:28 basically or not does too 31:29 get some hands-on onto the technology I'm 31:32 I I work well in a big dick a project but 31:36 I have primary youths cascading to crunch the paper 31:39 I'm big a budget be quarter or not I don't bet on it this is 31:43 I'm be used the word is clouded artist which one of her top so we don't you 31:47 woods 31:47 IBM mom no Globex platform basically for 31:51 process in the paper so disco all dis janice was a colleague will give me an 31:57 opportunity to not who basically 31:59 good other hand sewn onto this platform I and see how the big suits and big sick 32:04 will you know could be utilized 32:06 in phnom efficient manner basically process the paper 32:09 so to start with I mean I've been told they would be posted start 32:13 will you have provided on your talents Post website and 32:16 I feel do no more connected with those Superbike paper because 32:20 as somebody is know who just mentioned about bill 32:24 bus Brady butch a bus both application 32:28 so I think 32:30 because partition is the pero no like in the next few years 32:34 that is going to be took the basically because the pollution levels and online 32:38 but not to all the other things right so 32:41 that's the reason I mean I was at the pit was the city bike decal 32:45 I N basically I started processing this a divided by using big shoots and big 32:50 situation 32:50 initially and as as a band along Lake you're not looking good debate ok from 32:56 different though 32:57 hope fight in different situations I started but we don't look alike and blue 33:01 not cleared 33:02 abuse scandal with your licence with you with your latest on Lake 33:05 when I can compare it with the paid their deposit and after the 33:09 listening to some of the conversation in this angle no i think im like we want to 33:14 let people probably 33:15 the busted up there david was talking about so 33:19 if we can you know basically interpose these different dosage to get up early 33:24 we can have some big difference 33:31 what's really good no study break site you talked about lol 33:34 Citibank it being that the data all you know kinda resonated with you 33:38 what is it something you know you had taken the entire 33:42 up biking in your city is it something is it me 33:45 is sort of an up-and-coming motor transportation where you are 33:49 I would ever get ample me of the 33:52 musical program and people have taken to that program like crazy 33:55 you know what and 8th year people like to go and all 33:59 and use the Website I think because we don't have as much of an infrastructure 34:02 warned Oracle subways and buses is just not what your picture monolayer 34:06 worker supports like or I think that bites but I'll 34:09 reason washington where you are weird but kinda play and 34:12 in help books make their choices about transportation Yamin 34:17 I'll I'm deciding in Columbus Ohio and it is same as 34:22 both you know Denver because we don't talk a lot of public transportation 34:25 you know but they do have a bike in program called the school go 34:28 I think this column was cool bikes or something but I don't think it is as 34:32 extensive as in all city bikes platform 34:34 I and probably well you know in that case basically though 34:39 this column was biking program i think is only limited to book 34:43 always to campus or students at the campus and probably in the downtown 34:47 no I'm other distances to do is to have to travel to the car 34:50 so but basically I mean why I called president could reduce the 34:55 there does it is I got I was gonna come from OC peach 34:58 named with me in India so 35:01 Lake twenty or thirty years but in all it was the 35:04 bicycle safety 35:06 so everybody used to ride bicycle but eventually know it is I think two 35:11 wheelers thought the worker 35:12 leaders I know I'm doctors like basically just take me to do so 35:16 air quality in the CPE I know all the traffic jams and everything 35:20 so maybe you know we could probably 0 35:23 look at bill CP bike does it send learn some things from the 35:27 from you with related to the pollution levels and probably present in my 35:31 sleeping and then it'll 35:32 if let people want to learn from that don't believe somebody can come up and 35:37 then 35:37 what up such a biking but on me and the city its 35:43 okay cool so than do you have plans to go to take this but already there in 35:47 Columbus or other cities now that you've done this research 35:49 or alderman lucky playing out right I mean 35:54 I'm not sure did because you know I'm and portable diners is that I have done 35:58 or using this platform is I think go but we do want basically annoying 36:02 talks about how the genders different genders are using the biking program 36:06 late and basically wall what are the 36:10 basically not the duration of flights that users are willing to pick 36:15 so one of the out order to go to local bar 36:19 up on the plane said that are court of the running few 36:22 big group is basically people are willing to but I do not reportedly 36:26 let's say 20 minutes so if equal Glee 36:29 will put the bike incisions within 20 minutes of each other then probably a No 36:33 upbeat most of people with one won't boot but I work with the word 36:37 using bike 36:42 in okay cool we appreciate you 36:46 you provide health insurance as insights with us I think it's very useful 36:50 and you know it's really interesting obviously we call this the big game for 36:54 social challenge 36:55 and having such a basque have open 37:00 said questioned you guys are able to answer thing is for the social good 37:04 Alta decided to focus on this will open interest and from 37:07 little onto the biking to the transportation 37:11 food for two participants and then we'll hear from everybody else 37:14 right next to it but i think thats really just been 37:18 your YouTube it in and all and good for almost understand because it's 37:22 its consumer all I'll let me just do this to any 37:25 other also questions we can go ahead and take them so please I just 37:29 but yeah I suggest you check in now but or 37:32 I'll there's one more question for Matt in our 37:36 little folks okay and 37:39 that was howdyhi visualization I why 37:43 question i'm looking for. but basically what's the role 37:46 %uh that his visualization might be able to place you take this power 37:50 sure in now I can talk to this 37:54 a little bit arm but we initially kinda went the route 37:58 world I utilizing some kinda 38:01 d3 technologies and stuff too to integrate the 38:05 visualization and visualization is actually kinda one of my 38:09 main focus is rite now in the work that I'm doing 38:12 and so I think utilizing on 38:16 tools like a unity you are like a blender some other 38:20 some other platforms for for gaming technology can provide 38:24 in more rich an interactive visualization 38:27 of were kind of the end user to kinda by 38:31 glean as much inside as they can from and this project was gonna 38:35 her doubt it the visualization aspect bisbee we initially at the 8u 38:39 sat down ok do the initial idea was let's make whether 38:42 with roots and the visualization was I did before content 38:46 and the all this project and really i think is why i like it 38:50 so much is because it really instant and speech to write 38:54 all this DT to see id 38:56 and installations or you know that's good that's I mean certainly something 39:03 very powerful not 39:04 old can relate to. now for you guys and I'm gonna give everybody 39:07 that still on all our prisoners that have are here I think everybody 39:10 all her to spend share our online here 39:14 %uh but for each of these people what was the most surprising thing that you 39:19 learned as part of this process 39:20 so we'll start bankrolling they will do is let's go through everybody's answer 39:24 to that question and will wrap up we have about 10 minutes to go 39:27 I think that those interested in buying because the think their challenges right 39:30 there's no give an answer when you start the process so what was surprising 39:34 what this process I know you talk a little about temperature being 39:37 area well and maybe it wasn't what were there other things sure 39:42 I think on I think someone the main kind of surprises relating to the project 39:47 I itself we're kind of how how the variables interactive what 39:52 what had a strong correlation and what did not have a strong correlation 39:56 but as far as the challenge is a hole goes I think some of them 40:00 strong things that that were surprising or maybe 40:04 just a learning opportunity were really kind of what we 40:07 what we figured out along the way you lie seen on the IBM tools 40:11 I integrating no dread and and the 40:15 IBM on it Lumix analytics suite 40:19 was really kinda be and enriching learning experience for us 40:24 and F lee brought us some tools in our toolbox that we can utilize moving 40:28 forward do anything you want to expand on a 40:31 now tied up the crew 40:33 there is it was a huge challenge one way because we ran into an immigration 40:37 problem 40:37 actually MacBook up between old red the 40:41 IBM analytics for you that you couldn't load more than 40:45 3 kilobytes from between the two 40:48 which was a huge roadblock for a while definitely a problem 40:53 it so this who took us a while to fix it reaction pics 40:56 the that are no bread its I was pretty intense 40:59 we were able to put the Celtics didn't want small 41:03 yeah we were really lucky I'm do was it was probably the 41:07 you know a week before the actual submission date when we found that 41:11 that bug and realized wanna bigger act it was having 41:15 on the final product and luckily I p.m. was very responsive its ok like a day 41:20 for them to which the pics that I made to 41:24 all I B note read okay it 41:28 work just fine so more won't let me use let us know those to benefit 41:33 Michael and we're almost great yeah 41:37 alright cool let's go back to your first presenter bed 41:40 and then what about 30 what was the most surprising 41:43 all happy said what you were able to learn and discover a challenge 41:53 right you know what what we got let's go over to 41:56 at network what they will come back to them alright 42:00 a while 30 um 42:03 our first comment on that data mining dollars 42:07 a big challenge for me I'm I remember reading somewhere though say data 42:11 scientists have to spend around 42:13 eighty percent of their time data mining and I believe that's completely true 42:17 on one aspect of this was although this place the visuals I had to do 42:21 LA had no 42:23 appalling on for all the zip codes but they have a really high fidelity 42:27 um data set for now there's a lot of points 42:30 and then I'll my visualization to blue just couldn't handle all the points 42:35 so I have to do a lot of small stuff that didn't even really relate to the 42:39 data just so much 42:40 presentation at the data output Inc you Jess a.m. 42:44 open source some geographic time information system 42:48 comp reduce the number of points and even know I'm just doing stuff like 42:52 Americans joining datasets on geographical 42:56 locations so like I found a mcdonald's 43:00 and I wanna know what's it called a false and I put in the software and then 43:04 to say 43:05 this the court following the holding on that's great are 43:08 that is a McDonalds in that zip code on 43:11 and this is all done locally on the program but I wish I could have done was 43:15 no pick it apart right some scripts and push all those calculations the 43:19 do you job geometric operations are to head the 43:22 um ever and then in regard to the project itself 43:25 I was reminded that LA is a different city 43:28 on people do food is it's on to differ metric is a row 43:32 metric in a Metro metric um what it means is that usually 43:36 grocery store in South Ardmore further in rural areas just because they're more 43:40 spaced-out 43:41 and then when your new york they're more deaths so through dozens 43:45 have different I'm definitions sometimes but I'm for 43:49 a late a lot of people just visiting LA notices to its a pretty 43:53 big-city its its their sprawl so 43:56 what we notice is that not only is our poverty and all that stuff but 44:00 distances are very unique and LA purses 44:03 other cities like New York on band and in Chicago and so for 44:08 so about something that I i that was unique to the LA 44:11 she does its you know that makes no sense 44:16 alright cool tell what about 30 44:22 ISO I'd say there's probably two parts to it one is the the features that we 44:27 didn't implement the ones that didn't make the cut 44:29 that we were really kinda really wanted to badly but it just 44:32 wasn't quite there and then the other thing is he going to start with all 44:36 these different 44:37 you know occur almost disparate data sets and you have all this information 44:40 and then you need to 44:42 it's good you know said don't think about it and then 44:45 how do you presented an intuitive way a you know 44:48 is this really a lot of information than anyone ever tell the most important 44:52 story 44:52 and so actually walking through that process before even touch the computer 44:57 I'm does you know those a challenge what's most intuitive way to preserve 45:01 all this information and then just the features that didn't make it in 45:05 were so we had a we have this one feature where we want to hook up 45:09 a you know link to: so you have your food grade and then you have a link to 45:12 the opening and see what the people are saying 45:14 and so we're at WebCrawler and like all this stuff and you know 45:19 I saw her and twisted and just as the the you know pretty big effort and it 45:23 just 45:24 at the end of the day we just didn't have time to implement also 45:27 you know hopefully that makes certain future so mean that would be a fabulous 45:31 channel are you guys because I was there or not slow 45:34 yep I'm just getting home value at also they're using L 45:38 Yahoo to take revenge that had a DSL yeah 45:41 so yeah features that didn't make it and then just really kinda 45:45 before touching the computer like you know really thinking about how to 45:48 had a capture intuition with the data so also I'm not hopeful 45:54 now let's go back to bed and then we'll and what they can hear you this time 45:58 money cannot hear your 46:03 marina I'm in total net went into his loss these my connecting them 46:07 okay I'm is there anything else you wanna hear around 46:10 anything you can't really the discover Orange Bowl chance with this process 46:14 yeah I mean for me the most challenging part was to build you 46:19 rejoinder traditions because I not this 46:22 rowly was the first time I was like trying to visualize something ok up the 46:26 paper 46:26 iron so that was one of the most important thing that you know I 46:31 almost talent in NorCal this whole lot 46:34 challenge that's a good thing and are another surprising inside the iPhone 46:39 old phone or up to you know I blocker does some Krauts is basically during the 46:43 winter 46:44 of I'm in the last year's winter 2013 going to fork in the road approximately 46:48 500 kV riots 46:49 taken by the Bob bikers so even though the templates those 46:54 are no sub-zero so people are willing to ride the bike 46:57 though if that I lick special bike group probably be good how some more 47:02 bush remotely including broom been another 47:06 in third but I found is basically little bit early 47:09 good in this number of female drivers compared to the male drivers 47:13 which was expected but it became a breeder 47:17 once you actually plot something in a good look at it and then 47:20 basically gives you a greater impact than just telling the numbers because 47:23 you can see in the by 47:25 but the very small but a 20 per person does Oprah no criminal record 47:29 celebrating the 100 bucks 47:30 cool iron lekin other 30 only in new york city there are so many bike right 47:36 by groups 47:37 which are readily available to people under Dubai 47:40 so but basically I think that is what I know that helps 47:43 in phnom ridding the city bike program are not successful 47:47 working quite well we appreciate that 47:53 and didn't get too wet slot let's go David walkway 47:57 David the question while I what was ya for the developers for those that are 48:01 you that 48:01 udall this hard work what was you know maybe the most challenging her 48:06 surprising 48:07 MTO and a I don't steal 48:11 so I think media air must I can hear you he called me 48:15 hey sorry I had some connection issues but on but I'm back on land if you can 48:19 hear me 48:19 weekend what can your God also you what we can hear you so 48:23 will go ahead and yet the event had a final comment on that on that question 48:27 let's go ahead and do that 48:28 okay a touch briefly I am 48:31 said development so for me I think 48:35 for me it was more street for it I think lost the ball quick was doing and ask 48:41 this but once I had the analysis and 48:43 what I wanted to you 48:45 so the variables are features I want to show are basically 48:49 I E headed in a big secret able and then I was able to connect the truth tableau 48:55 to get some a Divis allies asians out so from you 48:59 mall that was in the nasa states and then for the development 49:02 use all I do a it was easy to connect from there 49:07 for tablet to the big secret tables hope you didn't answer the question 49:11 well that's good that's very helpful and where to go 49:15 three's a charm back home update 49:18 you know you wanna there you got a warm canary 49:22 OK Corral question and then what animal close-up 49:25 okay for me i i the fall asleep Acer 49:29 interesting outcomes that this is that theirs is a technology 49:33 it was created by a for large part is created by marketing firms 49:37 try to end point people the southern states we began its every bite 49:41 use this technology to help identify communities 49:45 don't know they exist so for example people that 49:48 up that for whom some 49:51 only parks or is the only thing they have and what up 49:55 they did you know that as their club this autocorrects or so healthy people 49:59 figure out their community maybe help them to band together 50:02 you interesting things up and there is a 50:06 for us in the the technical side I think be 50:09 week at whatever stage was a so lucky little 2015 50:13 that her so what is this data is available police for dole's 50:17 some framework still in this analysis and to create a Yahoo cases 50:22 so it was a we agree a serious look this together 50:26 and we some people ozzie while know that's really helpful 50:32 and I'll all realize what's at stake here you know met late 50:35 and I was started a couple minutes late but I just wanna thank 50:38 all participants join today your insight 50:42 are incredibly are all we appreciate your work and tying 50:46 and hopefully those who have had the opportunity to attend a gallon 50:49 you not yet to really hear about %uh what you consider it what you are able 50:53 to find out 50:53 so I will just leave even 2 comments here don't have to be sorry 50:57 accorded the answer is yes so we will post those instance their record or 51:00 OSA looking back in a minute but they weren't able to attend your 51:04 they were and then we'll be back again next wednesday at the same time 51:07 her me what's on the other side mirrors so thank you everybody for your time 51:11 today and I will show a great afternoon 51:13 have a great day thanks guys your YouTube 51:17 Press tomorrow do 51:22 do