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Channel / Source:
TEDx Talks
Published: 2015-04-20
Source: https://www.youtube.com/watch?v=6xsvGYIxJok
I'm both I run a that says I'm a I'm than welcome I'm a do data's story teller I did a story teller if you'd asked me a year ago what a data storyteller was I would probably say I I have no idea what it is or talked to today I am to tell you about my journey that over the last year wet accidentally became a data
stored I thought about what I learned along the way and maybe convince some of you that you too can be data storytellers if if you're curious and want to and so that my story a little about my background so first I work at a at investment in tech company called to say my where I do data science that's sort of one part of my world but
also married Anna an urban planner %HESITATION so I got my computer science in urban planning world and for most you know the time these two sections of my life have been pretty separate and that was just the way it was Intelsat nursing happen here in New York City in two thousand eleven %HESITATION then mayor Bloomberg sign this legislation called the open data laws in New York
nobody laws are really exciting for people like me because it takes data that's sort of inside city government suddenly allows anyone to look at it so whereas before the governor what am I something and tell us Hey this neighborhood has this has this many accidents now we can see data point by data point what's happening in a very very local level and so when these two
things %HESITATION came together in that up on by the way there's an open data portal to point out that knew anyone can go to to New York City open data portal and there's data sets up all sorts of things the fact there's one on the %HESITATION the size of the televisions in Times Square and their locations seek if that's gonna do it that was really cool
%HESITATION so these data sets of all different types in the pac twelve over twelve her data set so far and it's growing all the time so I kind of took this data science work and my interest in urban planning from discussion with my wife put together in this blog called I quant New York I'm Noah who yes somebody knows that right awesome thank you but the
first thing one of first things I did was I this map and this is a map of cycling injuries in New York City %HESITATION so red areas hilarious cinders none of it is it is in the %HESITATION let %HESITATION red areas are areas where people are getting more cyclists accidents and I found this are some public data and %HESITATION and and not at all I I
noticed a few things one on the east side of Manhattan was where there's a lot more cycling accidents or injuries that's because that's where they're more cyclists coming off the bridge's right and but also there's some other hot spots like Williamsburg and %HESITATION in Brooklyn or %HESITATION Roosevelt Avenue in queens and so I I I wrote about that and and puts it on the blog is
more just for me to learn how to do mapping I was as open source software code Q. GIS I want to learn it I posted it and when I did something actually happened I started to write about it so I got the mist covered and %HESITATION Brooklyn actually claim that it was it that I'd claimed it was a death trap which is not exactly what I
said %HESITATION %HESITATION streets blah guy and then and then even in the Atlantic %HESITATION and this is just sort of from a blog that I just put on tumblr and I had no fallers and I well that was really interesting so %HESITATION over the time from their sense of what white people right but I'm not the first person analyze cycle Saxons I'm not first person to
make maps like this what I did was not come get what was it that made it spread in and I thought about and I've worked over the next few %HESITATION I posted it to see what was kind of moving around and I realize that there's a third part to this that was really really important and and that is privacy coming improv comedy yes improper so I've
been doing improv since I was a summer camp called French woods %HESITATION yeah French with a woman in a good sign upstate New York I did in process of about thirteen years old I've been doing it ever since and I've learned a lot of things in improv that I realized I was bringing into my writing those bring into this data science to make people kind of
more interested in it %HESITATION and so I think in order to spread source site you need to go to tell the story started to talk about why improv relates to data science and and how how they can come together to tell better stories and that's why I call it data story telling what I kind of noticed that a phone into the first in stories you I
connect with people's experiences right by doing an improv scene and you learn you know if you're if you're %HESITATION brushing your teeth next to your wife and and and that that that's only we talk of people can relate to you know how they look on the bus that you know seen through that people kind of relate to because it's they have experiences there so I try
to write about things that new Yorkers experience and I figured what in your experience more than Duane Reade right so I did I thought this might be in a safe or not it was a I'm not every single building in New York to the closest pharmacy and I colored it by the pharmacy oranges when read read a CVS blues Walgreens yellows righted I map the whole
first about this neighborhood yet twin read country %HESITATION no question I also learned that CBS and writer attacking from the water good strategy CDS coming from the Hudson Duane Reade will not see it coming on and and I thought what that is when we really in New York they turned up no to Manhattan thing %HESITATION please do not even more the Bronx is right it country
and and really Brooklyn and queens there's a patchwork well this is interesting if you if you work for this company this proletarian sing for me it's a missing just wondering what our experience on how we can start to quantify them tell stories of our lives so really that's something that we can all kind of relate to and that's part of storytelling even and data analysis the
other thing I notice is you want to focus on a single idea right in them improv scene %HESITATION you can try to have seven ideas going but things can get lost pretty quickly so my work I try to put someone again when I when I look at city but data city but it is interesting that people leaving stations coming to stations a lot of data what
we take one idea and ideas gender okay here I've mapped the percent of male and female writers and city bike riders in New York City we teens in this neighborhood over eighty percent of the riders are male so this is a very male dominant city bike neighborhood and what that tells it could be better tentative incipit transportation infrastructure it could be a unit study of gender
here in the city arm and also if you're looking to meet a girl in a city by go to Brooklyn that's important I but what important here that's one ideas just gender is a lot of columns in the data to begin is that let's study just one the other thing is keep it simple not just not just one idea but one simple idea ideas ideas can
be very complex and you find yourself in improv the glow of the sky because here in this in this and you're going to lose everybody very quickly so I also try to keep it simple so when people hear that I do math they often think like I do this but it's more like this I mean I I discount things I do some maybe I do a
percentage %HESITATION and this is all just sort of high school math that's not you know it's not greasy match the people can really do this if they stop and start to ask the questions so a an example I look at the percent of parking tickets from out of state plates in every precinct in New York and we see it in this neighborhood there's a higher percent
of people coming in from out of state getting tickets which is telling right that's people driving into midtown more likely if you get farther out in our burrows is less travelers also makes sense but I also want to do this per state superstars like Jersey midtown yes it shows in the data that new people hunters are driving in the mid town you'll get Connecticut completed picture
coming in from the north you can actually see the going to botanical gardens %HESITATION in the Bronx and my absolute favorite Californians we're getting the hang out the hippest parts of Brooklyn Williamsburg Bushwick green point that's where people from California get parking tickets can tell us so much about our city by looking at our data %HESITATION and also explore the thing that you know best you're
all from your own your competition fields you know the things that you know you know the area that you study very well that where you work and I kind of a morning New York I've lived here for over a decade so I I focus on the York when I went in an improv if your lawyer and you go to a scene as a lawyer that seems
to be good because you know all of attack there you can you can play it to the very top of your intelligence just hit bottom I try I try to do work in other cities but it's hard I don't any context so for today I did analysis I'm of of Times Square and I I thought well look we all relate to what may be catching a
cab I was curious where a people catch cabs around Times Square so that's it Avenue over here you can see that people in general are catching tabs that eighth Avenue when they're leaving this district a less so heading heading south on that on Seventh Avenue you can see that the big yellow blurb %HESITATION you that in the lower left is %HESITATION is the port authorities that
makes sense but really people heading towards nominees what's interesting here is that that's what people catch cabs we the people get out of cabs they actually get out more on cross streets it's much more of a grid this makes a lot of sense right if your catch a cab somewhere especially if you're maybe a tourist you give the person the address may be to the address
not dropping off and on a corner to good luck buddy a buzzer that happen and what I really really like about this with a notice that depending on the rest of the street people get out of cabs differently so it's there so they're seventhavenue you're coming in a forty six are going west east you owe it you seem to get off on the west side of
the Avenue why he never got a cab stuck in traffic just get out you can actually see it in the data because look if you're coming in the other side if the people are getting out a on that side of the Avenue so really people are getting out of a depending on the direction of the street people are getting out of the taxes go the probably
waiting at lights and this is interesting reading your time it %HESITATION do advertising in the district in Illinois where where to welcome people where people getting up most often we can start to study this with that with the public data and if this you want to try to make an impact I've tried making impact in %HESITATION in city government by doing some of this work %HESITATION
each of you has your own ways you can make impacts I'm in particular I I did one proof a mathematical proof that no matter how many times you ride the subway and refill using their buttons you can't get a zero balance like literally it's not just you are you are you you literally can't get is your balance if you use the bow and say the trick
you can type nineteen oh five and get a get a get about that when I wrote about this the MTA responded Sarah it maybe doesn't impact and they said these machines do not hold an infinite amount of change and the denomination that suggested to ensure there's ample chance to comedy customers to pay with cash but that being said we will certainly look at this as part
of the process involved in rolling out the next very increase so the very pieces coming %HESITATION will see that imagine a world in March where you're like I want twenty dollars the metro card and you get to know the metric card like how much would that be in this eighteen forty three and you pay as opposed to now I had to pay twenty dollars we're gonna
give you a random amount above that %HESITATION the magic we switch that we can run our city better right so we'll see if the NCA poster I'd love to make an impact there I also found an extreme which is that half the city cabs tipping is based on just a though fair in the surcharge so if you get into a verifone tab and you get a
twenty percent button you're actually paying twenty percent on top of the taxi fare and a little bit of a surcharge but its crew it is run by creative mobile technologies the other half the cab and you the twenty percent button you're paying of tax you're paying a Cup of taxes and tolls so for two different computers you're paying more tips in one of the computer set
up than the other because that it's calculating tip on top of tolls and this is a big deal well does drivers are making two hundred fifty dollars more a year in tips by this little bit of rounding so we have half our cabs what we're all paying a little more and the drivers being paid which isn't a bad thing but it's kind of an equitable when
I pointed this out to the %HESITATION to the TLC they said we appreciate the work wonders analysis we're giving a thorough read impacts alright so I'm working on an end might my favorite was a was this I maps fire hydrants in the York city did not just any fire hydrants these the fire hydrants mapped by the amount of parking ticket revenue they're creating so these are
the top twenty fifty culprits in New York City first on the Upper East Side watch out of the nineteenth precinct will take you know matter where you park for hide and apparently %HESITATION but more dissing where these two hydrants that were down the Lower East Side and they were generating fifty five thousand dollars a year and hide in tickets to widen fifty five thousand dollars for
like five or six years so finally today's public I had a look and when I went to pick up was happening it turns out that there's basically a hydrant and then we'll look come like a bike lane and then a parking spot so you go in and you're like I'm on front of the curb the hive and there's a bike lane between well it turns out
that while the DOT pin a parking spot the NYPD disagreed and so they would take the spot for years and years and this is actually shot from the Google street your car going by and caught the ticket which which I really appreciated really appreciate it so I wrote about that I heard from the city again while the DOT have not received any complaints about this location