Microsoft Urban Futures Summer Workshop | Data Driven Urban Transformation [Day 1]

Key Takeaways

good morning everyone and welcome to the urban future summer workshop i'm kristin lauter and on behalf of the organizers i want to thank you all for joining us for the next three days of an intensive working event including talks and working groups the world is changing fast and cities are facing many challenges from covid and public health challenges to air quality and environmental impact to economic and social equity and community engagement issues our agenda includes working groups focused on all of these topics in which we hope to create research and collaboration plans and outline opportunities for concrete impact in cities in practice i'm lucky to be the research manager for our new urban innovation initiative at microsoft research and i'd like to thank the team scott counts aston roseway paul johns and gavin janka and his central engineering team for all their hard work to make this conference happen we're lucky to be partnering with roy zimmerman and janusz zafabian and kenji takeda from microsoft research outreach team to run this virtual event and we're especially indebted to janus and kylie and the technical support team for putting all the pieces together to make it happen virtually we've had significant support for our work and this conference from our corporate and legal affairs division under brad smith and i want to especially thank michael matt miller for his help in organizing we organized this event together and have been learning as we go having fun hopefully some future event will happen in person but for now we'll have to do what we can in this virtual setting over the next three days we'll hear short talks from leading scientists and researchers representing universities institutes and cities we have more than 100 attendees joining us and leading experts in many fields you'll also hear from several relevant teams from microsoft including our urban innovation initiative in research as well as our smart energy and places team in azure which is our cloud product division as well as from our ai for earth and state and local government teams in sila microsoft has recently announced its commitment to be carbon negative by 2030 in addition to a one billion dollar climate innovation fund we hope to learn from each other and look for ways to build strong research collaborations across all sectors which can accelerate the impact we envision so after starting each day with recorded talks which will be publicly available after the workshop we're using an innovative collaboration format for the second half of each day everyone has been assigned to a working group and you should have heard from your group leaders by now these working groups are intended to be a fun way to do community building share ideas and identify opportunities for collaboration and shared agendas and coordination for impact to keep the groups focused and to create some tangible outcomes from the workshop each group will be writing a short white paper over the course of three days the white papers will be made publicly available and will be public domain no ip ownership with all authors listed and equal to the members of your group who participate and wish to be listed these are brainstorming sessions and the idea is to have a scribe for each group and capture the thoughts of the group real time to minimize post-processing i'll say a little bit more about suggestions for your focused group work at the end of the talks today so for now i just want to say thank you so much for joining us and i'll turn it over to roy zimmerman who will make a few more logistical announcements and lead us through the program for today thank you all right good morning everyone i just have a few housekeeping announcements uh just a reminder that we're recording and we'll share the recordings after the sessions we do have a few speakers who have asked not to have their talks published and we will absolutely accommodate that please bear with us as we continue to refine our own virtual experience platform for this event we may experience a few hiccups like people forgetting to unmute themselves and i thank you in advance for your patience if you are experiencing any technical difficulties during the event please email gennus by replying to any of the emails you receive from msr events and she will work with you on troubleshooting our team will make sure you are muted during the talks that's not because we don't want to hear from you all questions should be submitted through the chat tool you can submit these at any time during the talks all speakers will be invited to answer any questions not addressed during the session after the event feel free to use the hashtag urban futures wkshp workshop if you want to amplify the event on your own social media channels and i will publish that to the group uh right now hopefully you can see that in the chat window and with that we'll move on to our first speaker who is scott counts for microsoft he'll be talking about city futures and the urban innovation at microsoft terrific thank you roy and hello everybody i'm very excited for this workshop uh like roy said i'm scott counts i'm a senior principal researcher in the redmond lab of microsoft research i've got just 20 minutes or so today i want to make sure to leave time for questions so i'm going to give a fairly brief overview of urban innovation at microsoft research and then also touch on some of the so that includes things like our charter some of our projects examples from our projects some of our guiding principles and then shift gears a little bit and talk about some of the opportunities uh in this urban science and technology space at microsoft the company more broadly and so that includes things like data opportunities compute opportunities and then some of the big picture initiatives that kristen just mentioned uh that microsoft has launched so before i get into any of that though let me start by saying who we are so who who are the folks involved with the urban innovation initiative at microsoft research so this is the team of people who have come together really quite organically to work in this space um and i don't have time to injuries everybody uh but you will hear talks from gavin janke tomorrow and osta roseway the following day and just about everybody here is involved in participating in the workshop uh so please feel free to reach out i will say this is just an incredibly multi-disciplinary group of folks we have backgrounds in everything from electrical engineering to hardware design chemistry industrial engineering social science computer science uh user experience and design um so i do think actually that breadth uh is been quite fun and also served us quite well as we engage with cities which as we all know are very complex very multifaceted entities so those are just the full-time folks we're also extremely fortunate this summer to have an amazing group of interns working with us also bringing a very multi-disciplinary set of skills so we have people coming from atmospheric chemistry from various parts of computer science from ai and ml to human computer interaction also human development family science so extremely uh fun group of interns this summer and the interns will be giving lightning talks in their summer projects two per day later on in each day so please look for those okay so turning to uh our urban innovation initiative at microsoft research um i want to give an overview of kind of our charter or areas of focus we break our research projects down into three areas so the first one is the economic environmental impact of urbanization and there you see a screen shot of our air quality sensor which i know a bunch of you are familiar with um but we also have other projects in this environmental impact space in particular lines of research looking at next generation sensing technologies um on this kind of march toward you know low ultra low or even no power sensing technologies um in the economic development space um camera third a second area there we have mostly these are data analytics projects and i'm actually going to give a couple examples in a few slides of those and then our third area is we call community and this is a bit of a catch-all category but it includes projects in areas like public health social equity in fact we have a great summer project with an intern in the social equity space looking at disparities in employment rebound hopefully rebound from the unemployment shock due to covid now project eclipse which is our air quality sensing project is our big project our marquee project and it's been a great one for us because it really does touch on all three of these areas of focus so let me use that to illustrate um some of our sort of guiding principles for how we're going about doing research in this space so the first one is about really kind of democratizing data and so when we think about air quality sensing and doing this what we're calling hyper local air quality sensing one of the phrases we use is democratizing air quality sensing or data and what do we mean by that well what we mean is that air quality sensing and data should be available and useful and reliable for everybody and so how do you enable that and that's one of the big challenges that we've been going after and so one of the ways that you do that is uh by having lots of sensors if you wanted to be hyper local you have to have lots of sensors so how do you enable that well one thing is you try and bring down the cost of the devices themselves and so we've been working hard on that and then the other way really is that you know we're trying to make this thing very dead simple to use and so we have this metaphor of the zip tie uh this air quality sensor should be zip tie easy to use and it's not just a metaphor actually uh we in fact have an affordance on the back of the device to just zip tie uh one of these devices anywhere really but a street light a bus shelter um and there's no um power required it's battery power there's a cellular connection and so literally within minutes of hanging one of these devices you have data going right up into a cloud store so what we hope that enables really is a kind of a full sensors to analytics solution and so we're really and what you see there is a picture of the device on the left and then data in a power bi visualization on the right and so really in the end it's all about the data and making the data available to relevant uh community members and constituents and so we have a number of ways for accessing data once they're in the cloud store through an api through power bi as you're seeing there we've partnered with uh cities to make data live in excel there's map visualizations for for mobile devices and so really the the goal here is to make this this full all the way through the data and analytics solution and then as we do that uh we're you know wanting to get out and deploy in the real world test in the real world and we've done a few deployments now so we started in boston last fall in a pilot deployment you see some pictures there and i know some of you attending were involved with that so thank you for your collaboration and that ran for a few months we're now currently deployed with this project in kenmore washington which is near seattle or part of the seattle metro also in miami uh have our initial deployment there now tacoma washington has been just a terrific partner for us unfortunately our deployment there got disrupted due to covid um but so that's kind of in tbd status and then as we go um through these different deployments we're really trying to scale up just in terms of number of devices out in the communities and so we're penciling in a chicago deployment again i know some of you are involved with that project for this fall and so we're really looking forward to kind of the next wave of deployments this fall and ultimately when we're doing these deployments you know we're really trying to listen to community members uh city leaders civic tech group folks to understand what are the important policy and resource allocation questions that we can answer with this technology so what you're seeing there is a bullet point list of actual uh project titles that we have collaboratively authored with our some of our city partners um and local civic tech groups in miami in kenmore in tacoma and so forth and i've just kind of categorized them a little bit but you know so there's a bunch of urban renewal related projects lots of greenification projects uh development so sort of monitoring um you know commercial and housing developments in urban areas lots of focus on social equity related questions so the map you're seeing there is actually of tacoma washington and the color coding corresponds to a neighborhood level equity index that city of tacoma has put together where equity refers to a kind of a constellation of factors like access to good food sources access to transportation housing prices and so forth and the plan which although i mentioned got disrupted due to covid but the plan at some point is to put our air quality sensors in these different neighborhoods based on this equity index and to see how air quality correlates uh with this equity index from city of tacoma and then of course lots of mobility related uh project ideas as well so that was just a couple of slides a fairly high level really about project eclipse again our air quality sensing project that i know some of you are familiar with tomorrow gavin jenke will be talking about the hardware the sensing device itself and then thursday asta roseway will be talking about ways to engage community members and other stakeholders with the air quality data so i'm going to uh shift gears a little bit here and talk about some of our other projects that leverage a really a pretty unique data source which is search query data from bing so search query data are really just an amazing data set for starters as you can imagine people search for just about everything under the sun and then some um but but from a social scientific perspective um you have really uh some incredible sampling properties so the coverage is just terrific i mean bing bing doesn't have as much usage as google of course but it does have really significant usage particularly in the us so for desktop web searching uh bing power is actually a third of all queries in the u.s so the sampling properties are just terrific and you know i think from a from a modeling and forecasting perspective search queries are what we think about or sometimes called aspirational that is they're forward-looking so people are looking for information about a decision that or behavior that they're going to take in the future so that has really nice properties from a forecasting standpoint and at this point working with bing queries we have a really nice process in place for um you know in utilizing the data in a privacy friendly irb approved uh manner uh where everything's anonymized and aggregated and we even have a track record now of sharing extracted signals with research partners outside of microsoft so let me jump into these projects so the first one is in the economics base and this is about measuring demand for employment in other words people searching for jobs and so what we've done here is take search queries identify those queries that are job searches and then from those we classify them into different employment categories so these are things like technology leisure and hospitality retail art architecture and engineering and so forth so these different employment sectors and then what you're seeing on the left there is the scatter plot on the left uh on the x-axis we have a population of count u.s counties so every dot is u.s county and on the right or sorry on the y-axis is the diversity across those different employment sectors of the job searches so in short this is a way to relate the diversity of job searching to in this case population of u.s counties and so what you see there is that as population increases uh job search diversity also increases so lower is actually more diverse on the genie coefficient scale there until you hit about 50 000 people and when you hit population of 50 000 um of course that's not causal it's correlational but the diversity of job searching tends to sort of level out if you will so this is a metric and how might you use this job search diversity metric well i mean a number of ways really so for instance if you're a smaller municipality who's growing uh you're hoping that you're growing economically in a way that's robust and diverse and so this would be one way to track that or if let's say from a larger metropolitan area standpoint you know you're making transportation decisions and you're thinking about what's our you know how should we connect to some of the growing outlying areas in a way that helps them most increase economic or employment diversity or even just related to coming out of covid you know this is a way to see if as we hopefully anyway rebound economically that people are searching for jobs in a way again that's diverse and robust now the map on the right that's a screenshot from a tool that we built there's the url there you can go and click around and play with some of these data and so what that interactive web experience shows is again county scale and we allow we show how again how these job search uh metrics correspond to different population demographics so in this case the green and the red are highlighting education level so high and low education counties in the u.s and then looking at the percent of searches that are in the leisure and hospitality industry so for instance you see in nevada a number of counties that are lower in education but high in terms of leisure and hospitality job searches so please feel free to play around with that another example using query data this one connects to our air quality sensing project so for this one we were trying to model respiratory illness rates at quite fine geographic scale so this is a census tract uh scale approximately neighborhood scale uh this is chicago by the way i know we have a big chicago contingent but for the rest of us that's chicago um and so here on the left we see kind of our ground truth which is estimates from cdc about asthma rates in the different census tracts in chicago the middle image there shows what we did using just query data alone and then on the right we see our kind of overall best model which combines bing query data with other data sources such as land cover data from satellite uh from satellites and census data and the best model actually correlates with the cdc um at over 0.9.92 or so and that's across you know literally tens of thousands of census tracts and hundreds of cities around the country and then the final example i'll give of ways to leverage this data source um we have another one um on this interactive website if you want to go have a look this one's about forecasting migration human migration in the united states and so what we're seeing here on the left is just from acs so this is just net migration as percent of state population and then the map on the right is based on our data where we can estimate the inflow and outflow but also in terms of whether the intent to move is based on a housing-related query or a an employment related query and you can even go deeper than that so if it's a employment related query what type of employment are people looking for so there's effectively a graph which in this case is a state to state 50 by 50 graph where the edges are intense to migrate and then what type of intent and the map here shows the state to state graph but we also have county to county and metro to metro graphs available as well so those were a few more of our projects and some examples leveraging the query data from bing but there are other outside of microsoft research but just broader microsoft other data and compute related resources that we all might take advantage of so let me mention a few of those so the first one is ai for earth which i put the url there for you to learn more uh if you don't know already but ai for earth offers data sets offers compute grants and other grants for people working on in the environmental and climate science space um so definitely look there for for resources and we'll have uh lucas japan speaking on thursday from ai for earth the second one is open data sets from azure itself so um we've worked with azure on a few different different projects related to urban science and technology uh one was on creating a heat island index for every city in the u.s based on a decade's worth of noaa weather data that they're hosting in an open format another one was looking at cross city comparisons of 311 data based on data that they're hosting and i think really the advantage here is the availability of the data but also it's put in just a very high performance environment so it's very quick to compete with and then the third one i'll mention is in the economic space and this is from linkedin so if you work in the economic kind of modeling space you probably read linkedin's monthly workforce reports they're also offering workforce related data now so another resource to check out in the broader microsoft scope microsoft of course also uh makes technology products a number of which are very relevant to urban science and technology so let me touch on a few of those for sure for environmental and urban sensing work as kristin alluded to the azure iot folks have been just a terrific partner for us um and miriam rasum will be speaking um tomorrow and is also participating in the workshop she works on the smart city side of the azure iot team and again just terrific collaboration partner for us in the economic area i mentioned earlier that we had done this project working with being search query data around job searching well we had actually a kind of a partner or sister project where we looked at skill development using search queries and so what we've done now with the folks at bing that run the job search results is we have built a statistical model derived from the queries themselves that learn associations between skills and jobs that then enables um us to identify jobs that leverage similar skills and so we're now thinking of ways to surface that in the bing job search results and so what you're seeing there this isn't live yet but this is a kind of an early mock-up of what that could look like so when somebody searches for cashier jobs uh we can start to surface other similar jobs uh teller associate stalker in this case um and so the goal and i think this will be especially relevant as people um you know kind of return to work after covid um which is we can help people you know sort of cast a wider net if you will in their job search yeah we are nearly out of time and we've got a lot of questions for you so i'd like to at least be able to get to one of the questions yeah so that we can model some best practices here uh or yearly best practices sure um uh those of you who ask questions uh i'm sure scott will be able to follow up with you individually uh and we'll figure out a way to try and share those if possible the first question we came in that came in was from danielle aliaga from purdue i'll see if we can combine them so that you can answer both does the air quality sensor gather temperature and humidity and is this related to the chicago aot project uh the answer is yes and mostly yes uh so hopefully yes so for the first one yes it does do temperature and humidity and like i said gavin genki tomorrow will go into a fair amount of detail about the device itself but it picks up four gases a particulate matter and then temperature and humidity and yes we are partnering with the aot folks in chicago so our chicago deployment um will basically be kind of a coming together of our project in aot great thank you scott uh and thank you for the very interesting thorough comprehensive talk uh and for uh allowing a little bit of time at the end there for a question uh just a reminder to the rest of our speakers um to be mindful of the time so that we can try and keep this as interactive as possible with our um other participants um and clearly everyone's got a lot of really great information to share so um thank you for for all of that scott uh our next speaker is uh paul hodgson uh who manages city data at the greater london authority he'll be talking to us about london's digital twin for air quality and covet 19 response paul the microphone is yours hi thank you very much so i work for the greater london authority where the strategic authority for the capital and i'm part of a intelligence and research team so i manage data scientists data engineers developers and also the gis mapping folk and we've been interested in business for a while and originally it was about understanding the public realm so there are about 40 major redevelopment neighborhoods in in london with kind of large new public space being created and we wanted to understand how existing public spaces um town squares and so on are being used to help us design the new ones as well as we could but then um we started um looking in more data detail about air quality and um the pollution tends to kind of follow people around the city across the day so um domestic boilers first thing in the morning with people having showers and after heating then connected to travel um during the rush hour and then connected to um office space and factories during the the kind of the peak of the day so we started looking at novel ways of kind of trying to track that not quite in real time but but in closer to real time um then of course kovid came along and we've been trying to understand busyness uh in relation to that so i'll talk you through i guess um how we've taken our existing air quality project um which has been running with the alan turing institute for a couple of years and then really used that foundation to help us in our occur v19 response so air quality um in london come the um the causes of poor air quality or causes pollution come from quite a wide range of sources so um this is an estimate of the percentage um of the different um i guess contributors uh in a typical year and you can see they're all spread right across fairly low percentages so there's no one silver bullet where you can kind of tackle that particular cause and then kind of fix all of the problems so we've had a number of programs so in london we're looking to replace the buses with electric buses we've got a lower mission zone in the very centre of london where um we're trying to reduce kind of number of diesel cars um we're putting green walls in front of primary schools where they're next to busy roads we're working with local authorities to reconfigure high streets to make them more pedestrian friendly so there's lots of interventions going on pretty much matched against nearly all of these kind of squares on the on origin on this chart um and we've been modeling air quality for for quite some time so we've got a a reasonable network of 70 really high quality um kind of scientific instruments essentially and then we take the annual average traffic flows um from our transport authority so for every single road in london we've got an indication of the annual kind of number of cars and kind of other vehicles on those roads and then we run a quite a traditional kind of mechanistic model but because an advanced model that allows us to get a fairly good estimate of air quality right down to kind of a 20 meter grid the issue is that the the data sources that flow into that and also the modeling often means that we're two to three years um behind so we've just current recently published 2016 data and the air quality modeling from that time has been really helpful in helping us design our interventions as a city authority but what we really needed was a much kind of higher turnover of estimates and kind of air quality modelling to help us understand how effective our interventions have been so the um which bus routes should we prioritize um having electric vehicles on and then kind of very quickly understand well what's the impact that they've had as an example and a couple of years ago we're very fortunate to have the opportunity to work with the alan turing institute so in the uk that is the national data science center of excellence and really their role is to take um i guess kind of cutting edge leading research work and then apply it in um real world situation so it kind of matched up with our requirements very well and one of the things they've been helping us with is access a broader range of inputs um so whereas beforehand we relied very much just on a single type of air sensor so i guess kind of blinking into perhaps some of scott's work in the previous kind of talk um mid-range and lower range kind of air quality sensors there are now hundreds of them across london um and i think fairly soon there'll be thousands um we're also able to make use of satellite data so a lot of london's pollution doesn't actually come from london uh it blows in from industrial mainland europe or maybe sand blowing up from the sahara a new it's very difficult for a probabilistic model to um uh take that into account but you can actually track it coming across the uk kind of towards london the day before and so you can bring it into your your kind of following days models and then finally of course there's been a huge explosion in individuals using um apps um first for navigation and we can take the feeds uh from waze and tomtom and some of those other kind of devices and get a feel for kind of where there's congestion um or kind of unexpected kind of congestion and then what the turing team have done is bring that together in a probabilistic modeling approach um and what they've been able to do for us is allow us to combine data of very different resolutions from different very different sources um sort of satellite data and i think one pixel from that is is perhaps a kilometer across whereas the air quality sensors uh are taking lots and lots of readings but just at a very very local level so you've got these very different types of data and they've brought them together into kind of a single unified model i use machine learning so that they can run a model every night make a prediction and then look at what actually happened the next day and refine and kind of tweak their their model and also really importantly and present that as an open api and what that will do is look a bit more like the weather forecast so 48 hours ahead one hour intervals it gives an indication of um what the pollution levels will be and that's incredibly useful for us as a city authority but it will also hopefully enable public decision making as well so you could imagine someone planning a route perhaps they cycle to work perhaps they're visiting a park perhaps meeting up with friends and they would normally take a certain route but they can overlay their journey planner with air quality forecasts and perhaps either travel at a different time to avoid a peak or to take a slight detour to avoid a particular incident so it allows people to make decisions um to avoid pollution as well as the city authority to actually reduce the levels of pollution itself now of course um the big priority for london as it is for an awful lot of cities uh most um areas is how do we respond um to the crisis and how do we then move into kind of recovery um over the coming months and what we were able to do is to access a broader range of input data sources so so in in addition to all the work that had already been going on for the air quality modelling including road sensors cctv and so on we've been looking at kind of novel data sources for us as a city authority so aggregated anonymised mobile phone trip counts um card spend and also polling to understand um i guess get attitudes and how attitudes change across across the week and and to match that the turing team bid for some internal resources and essentially tripled the size of the team for a period of two to three months to do a really focused concentrated piece of work um making the best use of these additional sources and i guess the kind of the wider brief um and there's three main um kind of uses of this so our public health planners if they relied solely on health data they'd always be about three or four weeks behind changes in behavior so if you get an uh an increase in unsafe behavior then you get an increase in cases but that can take maybe kind of a couple of weeks for people to see the symptoms then maybe a week to be hospitalized if they're unfortunate a little while for the tests to come back so there's always that kind of sort of time lag and the public health people challenged us to say well can you come up with any kind of early warning system that cuts down on that that time at all um and then the priority at the moment certainly in london and a lot of other cities in the uk is to try and reopen high street safely so there's this idea of kind of good business or a safe level of business so you need to have a certain amount of footfall to make it worthwhile for the shops to open so they're economically viable but you don't want too much football so that people aren't able to socially distance anymore so that's a really fine balance to try and strike um and certainly in the uk there's been lots of temporary measures where pavements have been widened roads have been blocked off new cycleways have been created and what we're trying to do is build up an evidence base to help the people who manage town centers who manage high streets manage business improvement districts um to understand well have the existing arrangements made enough extra space is more extra stress perhaps needed or maybe the extra space isn't all needed so you can begin to give some space back to the traffic um or perhaps there are whole areas of the borough that are busier than expected and need these contemporary arrangements so that's a real focus across the summer as we begin to try and get the economy going again and get people out visiting their their local kind of neighborhoods and in the longer term what we're hoping is this kind of business work will help us understand perhaps as whole sectors and economy or whole neighborhoods whole areas of the capital that haven't um returned to how they were and need to reinvent themselves um and need to adapt and so to understand which ones are those what what kind of support they need so there's kind of three i guess kind of time scales we're working on so very short one for the public health people the practical support for the high street managers and in the longer term economic recovery work and in terms of outputs what we're looking towards is trying to understand how each area is against normal so that would be we're taking that to be kind of a rolling 12 months period um previous um to take into account the seasonal effects in certain areas so this kind of pre-covered figures how does today compared to that but also comparing to lockdown normals so so when we had locked down pretty much everyone who could stay at home was staying at home so that is the absolute minimum level of activity that you're ever likely to see um so what we can do is kind of benchmark that and see well how has it changed week on week since that very kind of um early lockdown and then the final thing is that you know if you've got loads and loads of sensors taking lots and lots of readings constantly and day after day hour after hour you end up just completely drowning in in the data if you're not careful so actually what you want to do is to be able to flag um what's important to you so one of the things the touring team has been doing is kind of building in that warning kind of systems as well and um in terms of geography we're doing some time tracking work at a whole city level um and there's lots and lots of different data sources um some quite noisy so what we're trying to do is kind of understand what the trends like against different types of metrics but then we're beginning to dig in now with the benefit of mobile phone data into borough level so london has 33 boroughs and you can see that certain boroughs have uh bounced back in terms of certain types of activities to kind of february figures where others have stayed you know perhaps only 30 or less of those kind of levels and then finally um for people responsible for those kind of very local responses we need to try and get data right down to the high street or this kind of the town center level and that's what we're kind of looking to do with the very kind of localized data so like um i guess kind of most digital projects there's kind of different stages to this so we've been working a lot on trying to identify the different types of input data so assessing them for suitability and cost and i guess consistency because we're looking for kind of time series um we've been doing a lot of work building the infrastructure to deal with kind of um ingesting all this data um and then i guess the really interesting stuff is the modeling so kind of making sense of that data so what's it tell us and that's one of the areas that the turing have been leading on and then what we've been leading on as the city authority is identifying groups of end users bringing them together into kind of data sharing um groups so that we can really understand what is what are the key questions that you need to answer um and really drilling down into in the details so we can design products that do kind of one thing but do one thing well and really help them on a practical day-to-day basis and in terms of the split between the different organizations um as the gla it's probably fair to say we're working with smaller data um a more kind of traditional kind of gis kind of best fit between kind of different types of geographies and the turing have been leading on the big data pipelines and processing really kind of quite large kind of flows of data and we've been very fortunate at microsoft support touring um to retire credits and have also provided some really practical advice in setting up a robust and kind of um uh i guess kind of industrial kind of quality api uh to support access to these kind of data flows so just to give you a couple of examples there's a couple of sub teams at the turing and one's working on image recognition so edge detection has obviously been around for a while and they've been using that to kind of tag um traffic cameras so or images from traffic cameras so you can spot this is a bus this is a car this is a cyclist this is a pedestrian and not facial recognition but just recognizing humanoids and then what you can do is take these kind of dumb video feeds turn those into counts so they're building up time series um based on regular samples of kind of counts of different um types of things that are passing the cameras and then they can also overlay um a spatial grid so for people you can work out how many people are on a pavement um at certain times and then kind of how far away they are from um one another and therefore social distancing and the figure on the right is like a heat map i guess of pedestrians up and down that high street and on the right hand side there are a lot more than than on the left hand side so you can tell from the lighter colors and then the other really helpful thing they've been doing is um modeling normal for each location and what that allows you to do is to then flag up the things that are unexpected for that location and that time and the um little um chart on the right that the red areas are area clusters of unusual activity either in space or in the um the third dimension the height is is time so um you can begin to i guess kind of one-off little peaks you're not interested in but where something um significant is happening that that's where you want to kind of draw your attention to and then the final bit of work is um on routing so um back on the the air quality project what they were looking at is kind of could you as someone going out for a walk someone going for a run someone going for a cycle could as well as um you putting in your start and your destination um could you also design circular routes that avoid that minimize your um exposure to high pollution areas and that's a trickier task and you think i think the first time they ran it basically the route went straight to a clean area then ran backwards and forwards up the same road about 20 times and then went home again which isn't a particularly interesting run so they've done a lot of work on turning it into i guess kind of a compromise between the lowest um pollution but but also an interesting kind of route in its own right and if you change the pollution levels then the actual route changes as well um and then what they've done is this the final step is to take the outputs of some of the modeling um and help people um i guess kind of route themselves to the least busy bits of london so there's some basic input information here the um the orange areas in the red areas are narrow pavements so kind of focus you towards the blue pavements but also towards busier or quieter kind of times of the day so perhaps if you've got respiratory illness or you're living with someone who's shielding you can then um but you do have to go out for whatever reason it can you can help identify the kind of the best routes and the best times to take so this is what it kind of looks like brought together and it's going to be a mix of open data shared data and small amounts of paid for data and really there's three main outputs so there's the london level time series and then there's network business so business on the roads business on the pavements and so on and then destination business a business at the town centres and the high streets and in terms of open data coming out um on the data store we'll be posting commentary and blogs and obviously data visualization but the main outputs will be shared data because there's shared data and paid for data going into it we can't make all of the final outputs open data even though we'd like to so we'll be creating interactive tools governed by data sharing agreements with our users in business improvement districts and high streets and so on so just to finish off these are our kind of key research questions that we're working on so i've talked quite a lot about the town centers and the high street we're also interested in understanding um the extent to which visitors are returning to london so london's economy obviously relies a lot on its residents but we also benefit from many millions of visitors in a typical year there's a field that local high streets are actually doing reasonably well but the office districts right in the centre of london haven't really returned to anything like normal levels and then finally we want to identify vulnerable groups and really if there's any kind of spike in business or unsafe business in the areas where there's a concentration of vulnerable groups to really kind of flag that so i'm going to stop there and um very happy to answer questions over the next few minutes thanks paul um we do have our first question from helen fitzmaurice uh and it looks like someone else wants to know the answer to this question as well um the question is do you use the cameras to characterize surface street truck traffic uh yes so i mean london so transport for london has 900 cameras already um which essentially if there's an accident or a major event they use that to understand what's going on on the street quite a lot of boroughs sitting on maybe 100 to 800 cameras often in pedestrianized areas um more connected with community safety so we're repurposing these existing networks um and turning the dumb video feeds into counts so kind of real useful data and social distancing data so that's what the project's been about but but absolutely nothing to do with facial recognition or tracking individuals and it's been for the turing ethics committee and so on and was it problematic at all working with the city to redirect the the use of those cameras for that purpose at all so the the transport for london cameras were open source data already um so it was a relatively straightforward data sharing agreement with them uh the individual boroughs they're working their way through um they're not really set up for sharing live video feeds with external organizations so they're working their way through the logistics of that so i think there's a lot of willing and they can see the benefit of it but there's a slight time lag where i well they put in place the systems for sharing those video feeds i guess um but then obviously having done that yeah we'll have benefits for months and years to come got it thank you uh we have another question from sravya avasarala uh the question is how do you overlay this data with the data from the hospitals to warn people about the potential risks so that wouldn't be our role we would be making this available this data available to the public health authorities um because they're the experts in modeling the um the shape of the curve and so on so that isn't the city's responsibility this is the public health authorities so but but they it's our responsibility i guess to kind of feed in our best intelligence to them right i i have a question for you is the city of london working with as unique as your city is are you working with any other cities to share data or share best practices or uh align common goals to learn from each other or benefit from each other's work yeah absolutely i mean less so in this kind of very technical kind of machine learning kind of part of the project but in the wider design in the recovery we've been doing a review of lots of other cities around the world who've recovered from disasters um and they could be natural disasters they could be economic and so on kind of going back you know perhaps over the last 50 to 100 years and looking how cities have organized themselves to kind of respond to to recover and the success to which they have managed to kind of recover but also of course we're sharing data with other major cities across the uk and other networks of cities across europe we're involved in a number of pan-european projects involving kind of data and smart cities so that those networks have been really really helpful and of course some of the european cities are also mainland european cities are ahead of us in the curve so we can kind of learn from italy and spain and so on um who are kind of a month or two ahead so yeah absolutely and and it's really interesting that a lot of people have been going about this similar sort of way so it more kind of reinforces that yeah we're going about this the right way right okay we have another question from madeleine depp who says thank you for sharing this fascinating and important work i'm curious if you could talk a little more about your work getting insights and feedback from health planners and how you're connecting with your prospective audience regarding the interactive tools um so so the audience is is really tricky we've done a lot of work on that so i i think it segments in a number of ways so some of our partners will have their own analysts and actually what they want is an api they want access to the raw figures so they can do their own analysis an awful lot of people um say that particularly those responsible for some of the local neighborhoods will want a tool that will help them answer very specific questions so we're designing web-based tools with logins and you know kind of how busy was my high street this saturday compared to the saturday before sunday afterwards how busy is it compared to some of my neighbors and kind of um allows me to kind of sort of dig into the bits i want to but without overwhelming me with with data so um we're building a number of tools aimed at different audiences essentially um that that's her approach because rather than creating one monster tool that tries to do everything all right okay uh we have i think time for just two more questions uh the next question comes from varsha gopalak krishnan uh the question is you mentioned that the cameras detect humanoids and social distancing between people are you using this data to plan for the future to build streets that allow for more space between pedestrians i mean that's how we plan for the future is a really interesting question i mean i could talk for another half an hour about how it had the london plan and kind of how it might affect how london develops in the next 20 years it's it's only an estimate and what it will do is help us understand the extent to which we can promote people going back to high streets that are very still very quiet but also perhaps justify extending the space out into the road for those high streets that are getting busy um because there's always that tension where you're taking space away from the cars and the buses you need to kind of justify that so it's helping people try and find that balance in their own local areas right okay and the last question comes from alex cabral the question is i was wondering if you could describe a bit more about the approach in combining the satellite and sensor data as you mentioned they have different resolutions so i wonder how they were used together to get a sense of street level activity some my best suggestion given we've only got a minute or two is to visit the allen if you google if you um search for um alan turing london air quality you'll come up w