precision as we try to zoom in deeper and deeper without any sort of bottom up information to validate how many people are in a given area much less where those people go under a forced migrations scenario and this is especially important when we're using building footprints as a proxy for people again at these really small fine grain processes within say an urban area or a refugee camp next slide please so this is some work my colleague Jamon Bannon Hook is leading at Oregon State University to assess again these gridded population data sets for refugee camps and the first big takeaway from this is with official refugee camp locations often are not within 10 kilometers of any identifiable like visually identifiable settlement from image from satellite images so if we're feeding that information into a complex you know satellite coupled survey system that underlying training data may be highly inaccurate and we might be dumping people into places where they're not or more importantly not identifying people who need to be counted in these scenarios next slide please so this is all two again I use gridded population data sets in my research I think they serve a really important function for measuring human movement across the planet over space and time and they're increasingly being used in climate projections so this is the World Bank's grounds well report the baseline data this report are calibrated with an initial condition in 2010 both from a population grid cell population site gridded population standpoint as well as things like crop information and baseline climatic conditions and then basically climate changes push through those grid cells and then a gravity model distributes rural populations towards urban areas next slide please and with this they come up with a likelihood of climate in migration across the different I believe this was based on the cement five models but it might be cement six and they estimate across six major world regions the flow of about two hundred million people displaced because of climate change into urban areas or the caveat is that you know the initial gridded population data set they use is just a single one that as I've said differs from some of the other ones and as Miguel kind of alluded to they're under the assumption of a rural to urban migration framework but that doesn't really account for people bouncing around or you know urban to rural migration urban to urban migration patterns that these really acute climatic events may lead to the greatest movement of people very rapidly and then those people move to an area that's climate vulnerable and then they get hit with another climate event so I think really distilling out you know slow onset versus acute and just recognizing that at the end of the day we may not have all the information that we would like to have and being really forthright with you know what can we actually glean in a chaotic and compoundingly complex world we're living in next slide please so just some quick key takeaways and I'll pass this off if you want to learn more I really suggest going to this website www.popgrid.org there's an interactive feature where you can look at different grid cells from different areas it also just provides updated information on the different gridded population products as they're produced again I think these products are extremely important but the caveats of the uncertainty needs to be conveyed the easiest way to do that is just to read up on the given product that you're going to use and again with these increasingly high resolution products we need to understand we may be trading off you know this better precision for lower accuracy information and whether that actually aids us in making better decision I think we'll have to figure that out and then the last thing in terms of human population movement from my own research is that clouds are always a conundrum when we're using earth observational data to measure human processes so especially in the tropics in terms of real-time information we might not always have that even as these modeling frameworks get better so thank you so much for your time really appreciate it thank you casket that was wonderful that was terrific I'm sure that a lot of people are you know eager to ask questions but before we get to the qs session let me introduce our third and last speaker of this panel Caleb Litaro representing the GDL project so Caleb it's the stage is yours thank you so much for having me it's great to be here can we get my slides up well while the slides get loaded so I'm codley taro so the the the vision really of the GDL project is essentially this you know this essentially this this dream of scooping up the world's information and there we go all right thank you perfect so the vision of the GDL project is really this idea of scooping up the world's information and trying to catalog global events in real time and GDL today actually powers a large fraction of of actually of actually a very very large fraction of all global risk work done globally either directly through GDL data sets itself or indirectly through all of the myriad risk data sets around the world that consume GDL today next slide so the idea of GDL is really about how do we take news and other open information and transform it into data that allows us to really understand the world around us especially essentially taking if you think about news and all this other news academic literature social media etc is designed for human consumption uh it's it's the spoken word it's imagery it's moving imagery it's text but at the end of the day it's not codified it was never intended for machine consumption so how do we use machines to process that information make it codified but then how do we develop these analytic workflows and allow us to really start understanding the world from it next slide um so it all starts with news and this is actually a map of a couple of months of all the locations that GDL has gathered news from or about and you can pretty much it matches global population that essentially where people live on earth we're gathering information from and this is very unique because GDL today we have an enormous emphasis on local media print broadcast web in local local sources so this differs you know if you think about political science and the social sciences you know for the longest time it was if it's not in the new york times it didn't happen and still today so so much of that of the work is done in english or a handful of european languages and western sources in fact one of the major counterterrorism data or one of the major academic terrorism data sets that has sort of become the standard and used heavily by government which also is oftentimes used to understand kind of migration flows around that is based almost exclusively on english language american news outlets um which you know again really kind of shapes your really blinds you essentially to what's happening around the world next slide uh so in the aftermath of the 2004 ebola sorry 2014 ebola outbreak we looked back and we actually discovered we had actually seen the earliest of glimmers of that through local radio broadcasts in guinea unfortunately it was in french the time period we were only translating a fraction of that so by the end of 2014 uh we launched this mass machine translation infrastructure so you can imagine GDL today we're scooping up print broadcast web and broadcast at both radio and television uh from around the world uh we're we're scooping all that in we monitor today over 150 languages we know we actually monitor more uh but language detection tools kind of break down once you pass 150 and we're actually about to launch a 400 language detector for that we live translate absolutely everything we monitor in 65 of those languages soon to be more than 100 which allows us to reach really really locally so in the aftermath of ebola outbreak we launched this translation infrastructure fast forward to 10 p.m eastern time december 30th 2019 uh we picked up uh the a surge of local chinese language coverage in Wuhan china about a SARS-like viral pneumonia of unknown origin the following morning a company in canada called blue dots global which uh the vile surveillance firm um that uses our data they have these machine learning models built in our data they sent out a worldwide alert one of the very first worldwide alerts saying based on this data and based on these other models we expect the global pandemic will be coming by next month and of course uh you know so it was it's a really powerful kind of statement about why it's so so important if we want to understand the world to monitor local sources and local languages uh next slide we also have academic literature as part of this so a collaboration with the u.s. army a few years ago we want to look we basically from j story looked at all the academic literature they've collected on african middle east we looked at so if you're familiar with dt ic uh so u.s. government unclassed and declassed output um and then a lot of other uh sources there to look at context and and through this we actually codify out of so you can imagine taking all this literature and and extracting out computationally topics geographies bibliographics all kinds of metadata so you can go in and you can say um what are the food insecure take the food insecurity issues between these two groups that tend to involve mass migration events and who are sort of the academic uh experts that tend to be cited the most in that specific area um and how is that changing over time so this is actually deployed as a production tool was it was was the development of this really so that idea of let's say that we're going and we're actually trying to intervene who are kind of those those find experts globally but more importantly you can start looking at comparisons of what is the government and academic institutions like the national academies where they focusing versus what's the reality on the ground and there's a huge piece that a huge paper came behind that we actually showed this immense immense golf between what academia and the scientific institutions like nas study which tends to be where sort of funding priorities it really has to do with western policy um and sort of the the funding uh sort of funding trends at the moment instead of reality of where where events actually occurring on earth and typically what we find is the academic environment um and the policy community is about 10 years behind reality so this is huge huge implications that we try to understand events at the ground next slide um so you can imagine you know we're scooping up all this information from from around the world um you know what can you do with that so um with respect to the area of my great now again g-dolls deployed in in any topic on earth there there's a there's g-dolls deployed in some way shape or form but looking specifically at migration um typically one major use case is now casting and forecasting they're trying to understand global risk they're trying to understand you know where might uh you know where might instability come out um how might that manifest to what degree might migration uh play a role or be an outcome of that and where might uh where might those patterns go what are the stressors that they involve that they might cause as they migrate and that might affect their own migration patterns in as most specifically how are societies likely to react to that so looking at sort of the the global risk environment uh with respect to this and that can be sudden exhaust and a shock so that can be things like even natural disasters um you know say an earthquake which might not be immediately predictable you can however develop these forecasting models for if that does occur what are the likely outcomes within the structures of this particular society next slide um and more specifically so this is actually a fascinating model that esa that esa easo has come up with which looks specifically at risk forecasting uh for actually forced migration itself uh so a lot of sort of specialty migration uh models and specific models around the specific risk factors whether that's food and water access and so on next slide um but of course once migration occurs like once an event begins occurring um ghettos oftentimes used to map that in real time so there's actually a fascinating map that bbva came up with uh during the 2015 uh uh exodus and what's fascinating about this is he actually drew and actually matched um it actually matched eventually government statistics sort of caught up because again government statistics are so so slow um and so what this bright was a real-time look at where flows were occurring sort of inflows and outflows local stories and specifically local reaction to that that for example a small town somewhere a group of refugees showed up how are they reacting to that and how is that reaction changing because again if we look at say the ukraine crisis right now um you know looking at say local polish newspapers gives you a lot of interesting insights to kind of those early tale warnings of sort of the the european reaction to that next slide and we can also look at the mapping of narratives so narratives again one of the most important things if we think about things like forced migration um it's not you know satellites will tell you where people are moving uh things like cdr records the cell phone data records will allow mass scale mobility um at an agris scale and those are of course deployed by almost all ngos and governments today but that gives you certain things and cdr records are very there's a lot of nuance when you look at refugee migration specifically around um certainly you know their their applications to different parts of the world different types of migration patterns but most importantly what they tell you is kind of where people are at this moment what they don't tell you is the most important thing which is what is the information flows that are reaching migrants which has an immense impact on dis and misinformation uh that can feed them so both real information but also again there are a lot of actors um both well-meaning but also malicious actors whether private sector or state uh or criminal enterprises as well that attempt to direct migrants that attempt to use them uh increasing as a tool of conflict um so feeding them force forcibly feeding them false knowingly false information um with this intent to create disruption they're sort of becoming a form of irregular warfare in many areas of the world and so how do we understand what are those information streams that are arriving to them but then most importantly the domestic reaction society and one of the things i'll show in a moment um is how those play and one thing you can see with this is mapping narratives there's ability to go through and this particular example is actually drawn from climate change um which of course has a lot of impact on migration um but that ability to so this particular example here was let's take a collection of of a few months of climate change coverage just take all the people that are mentioned in public news coverage and map the interconnections to those and what you see in all those communities around those are the narrative communities how climate change is specifically being internalized i mean around the periphery of those are the reporters the journalists the the experts in those areas so it becomes a very powerful way to take these massive complex and very fluid narrative environments we can visualize them in ways that we can that we can actually message into uh next slide but also understanding how the media is covering it so this is uh this is a kind of a global look at um coverage essentially of refugee attention to refugees since 2017 you can see the ukraine crisis caused this immense intense coverage of refugees of migration but we can see how quickly almost immediately within days of the war beginning it faded away and it's continued to decline um but also we can see afghanistan didn't really cause nearly that amount so we can see the world's attention and in return funding and policy and other support um tends to flow of course through the pattern of um you know what sort of the what societies um specifically the large donor societies kind of care about in the world which has huge implications there uh next slide we can also see that media attention to this national attention to refugees and migration tends to peak with their own impact so this was a fascinating uh chart that erwin did in 2015 and they looked at each country when did they begin their domestic press begin talking about migration and so you can see each country um as as migrations of migration flows spread across europe um countries didn't say oh look at what's happening we need to start talking about this it wasn't until it reached their doorstep was it well that's not our problem is someone else's problem it's only when they reached their own doorstep that the media started covering that and that means that that critical missing period we can start preparing a population discussing uh what's occurring here uh the needs of these populations all that messaging environment that's lacking so sort of the average the average citizen doesn't even realize that there's a crisis until all of a sudden it's on their doorstep and they don't really have a chance to kind of internalize or adapt and and that can oftentimes um see the sort of a negative reaction to refugee flows and so there's a lot of sort of missed opportunities there next slide um but then also understand how they're framed so what you're seeing in this map here is when you look at media coverage of refugees what are the countries that are mentioned together um and so again this is not the reality this is how the world's media kind of views refugee movements across the world there's a lot and again a lot of this makes sense there's a lot of telling insights there um in terms of how we kind of the lenses and specifically the western lens through which migration is viewed throughout the world next slide um and also now this is an interesting one so this is actually specifically of natural disasters um so this is looking at the media coverage of a natural disaster and specifically the essentially the exponential decay curve of coverage you can imagine um of a particular area there's very low coverage and natural disaster occurs there's a surge in coverage last 72 hours and the decay um it's almost a perfect uh decay up to 14 days now what's interesting about this graph is of course media scholars have always known this but what's fascinating about this is we can observe stories that deviate from this so we can take any emerging story overlay it onto this and deviation so it's inorganic that an external actor is inorganically shaping the narrative of that story whether it's a natural disaster whether it's refugees whether it's another story that has huge implications that tells us that this is no longer an actual story about refugees this is something else entirely um so it means that some other actors attempting to shape this whether that's as a tool of conflict to so discord um or so on and that once you see those kind of deviations that allows your analysts then to really drill and understand what is the messaging environment around this and what are what are the things that we need to do to intervene potentially next slide um now this map this is actually part of an animation which i i'm not showing here but this is actually fascinating so this is scooping up each day all take every mention of paris across the world's media that we monitor each day how positive and negative was all the coverage of paris yesterday if you do that across the world um what you get is of course not how happy or sad people are within an area but how the world's portraying that now what's fascinating about that is when we looked at europe and we zoomed in you can see the you can see positivity you can see these tendrils of intense negativity start making their way through europe that was the backlash to the 2015 refugee exodus and you can see that you could actually see in real time sort of this wave of positivity and then this turning against it as those refugees became sort of the the outlet essentially the scapegoat for anything that was that was happening in society inflation crime etc suddenly it's all their fault and so you can see that um remotely through the data next slide um and we can also look at things globally so this is actually a fascinating one of sort of global positivity and negativity of worldwide media uh since 1979 prim broadcast web over a hundred languages and what we can see is you look here you look at the air of the web you can see at the web that sort of tone kind of moved around but it was the web era as online news florist that we've plunged um ever more steadily towards negativity and that has a lot to do with media and how you know papers and outlets now are competing globally and social media and virility but what it means at the end of the day is as steven pinker actually used this this graph in his book i mean if you look if you look more into this you see that the world isn't necessarily becoming a more negative place but the media is portraying it that way as they you know due to commercial pressure and what that means is the lens through which we see like the the lens through which we see say migration is no longer going to be as it might have been 30 or 40 years ago here these are real life human beings they're migrating here's their challenges here's what they need instead it now becomes an opportunity for essentially negativity of here's another bad story um and and again that back that sort of that backdrop of negativity more broadly this has a again more implications than information environment next slide um we can look at simple things like pronouns within media coverage and we can start looking at are the our refugees portrayed as us you know as as human beings like we are or is it those and they and them you can see these fracture points in the media at key points next slide um we can look at morality call out so we can look at how are they being portrayed are they being portrayed as um you know are they um is this a moral imperative to address them is that patriotic duty is this selfish duty to assist them um what are their driving factors and again these are not the reality but this is how it's portrayed to societies which again has immense impact um to the sustainment of policy and the the facilitation of resources next slide um and we can see this very clearly in things like ebola when the ebola outbreak occurred it got very little coverage until the first americans got it and if you look at the bottom graph there you can see that the tone of media coverage of covid is very negative to the first americans got and it became more and more positive um and that again that was the the sort of the westernest savior that oh don't worry now that westerners americans have it we're going to go and save the world now and that again has a huge implication to the lenses to which societies and in term policy decisions of funding resources um you know all these different factors play a huge role through that lens next slide we can also see the impact to which major events um so for example after after kurti's death um you can see the tone of media coverage towards refugees is very negative after his death we saw this wave of sympathy there um then that of course faded away so the ability to kind of measure that in real time of of of these events next slide um and this is a fascinating visualization uh that kathiebus did was this kind of looking at media coverage of refugees and putting a dot on the map when there was a positive mention there um so how are refugees being portrayed next slide um this is another fascinating researcher um and what she did was a really really fascinating map is she took this media because she basically took our live feed and looked for any mention of refugees are and then looked at what came after that so what is a refugee is it uh is a refugee is a bad person is it a stateless person is it someone that's that suffering is it someone that's created that's a criminal how is it being described and how does that vary across the world next slide um so she created these these fascinating maps to kind of show the main you know are they communities you know are they claimants are they basically a burden to society or are they part of our community there next slide and she did these these really fascinating maps we can kind of see word by word by word how are they described so understanding those narratives in real time next slide and then finally looking at um so this is actually this is specifically about covid but this ability to create these large narrative maps so this was actually an example done with covid where the question was um it was essentially a group of of allied governors where we know the major narratives around covid what are all the narratives that are coming out that we don't know right now so the ability to sort of map those out in real time next slide um and then finally looking at so this was actually the the map of covid actually as it first occurred as the earliest glimmers um restart data sets um this was actually those early early glimmers you can see that first initial pickup um that's when we saw that final wave there was two weeks later when the west of the world finally started coming to terms that maybe there's a problem here so again that importance of looking locally next slide um and so finally if you think about the gdo project it's really this idea of scooping up the world's information trying to process what's happening there is specifically with respect to um to migration it's really about this idea of understanding the world's risk factors of seeing both what might be happening here but then eventually you know sort of looking at both what are the immediate risk factors but then other things like exhaustion is shocks what are potential scenarios there as things come to a head measuring that in real time um and giving sort of those those prediction those those essentially those forecasts once things actually turn to action and and events begin to occur mapping those in real times as those turn eventually into migration mapping those out and then as those as those exits occur how what is the information uh environment that's reaching refugees what is the information environment that's reaching societies and how are they internalizing that so thank you very much i know i've covered a huge amount of ground here but hopefully it gives you a little bit of glimpse of again this this broader idea of how we can use these non-traditional data sets that will understand global events both long term um but then also again that narrative environment which at the end of the day you can use a satellite to watch to watch migration flows um you can use cdr's observational data all kinds of different um data to kind of say well look here where people are but without understanding the narrative environment around that you can't really understand how societies are going to react and most importantly what is the sustainment and the range of policy options that are available obviously in ukraine governments around the world are stepping up in a way that afghanistan they did not um and that has a lot to do with again how it's being framed how societies are being told these stories um and so again that the huge amounts of insights i think that can be gained from that thank you so much thank you kelev that was fascinating um i want to thank our three speakers miguel casket and kelev uh for your fascinating um presentations and insights um you know uh miguel did a fantastic job of laying out the background and some of the things that are associated um some of the things that stuck with me are these uh issues around food and nutrition for idps um and then the connection the whole connection to sustainability of nations and states um casket's presentation on you know measuring population especially you know this impacted and potentially displaced populations from disasters using different kinds of data you know gridded population data um that was really um very informative um i particularly liked the issue about um the community's trend of trying to be quantitative with building footprints and and he very rightfully pointed out that there is an issue about building volume which we still do not understand very well and kelev's um real um insights from how do you turn news into data and this qualitative assessments of the narratives that not only allows us to understand broad general patterns of you know idp movements but also the sentiments around those populations and and the populations surrounding them so with that i'm going to open it up for questions from the committee members um and if you have one please um raise your hand in zoom um if you would like to ask a question uh for those who are our public attendees watching the webcast please contribute your ideas into slido and we will work to bring those into our conversations um so let me go and see who would like to ask a question harvey okay um thank you very much for very interesting presentations i mean just really i have i have lots of questions but i guess i have one underlying and i think very important question so you talked to the panelists talked a lot about um nontritional data sources that they get at things that are difficult to measure um but when we make legal and policy decisions in government we have really strict standards for data authoritativeness you know for validity reliability how representative the data are how transparent the data generation processes how accountable it is i wonder if the panelists could comment on that how do we take these nontraditional data sources and make them good enough for you know authoritative enough that we can make um you know policy you know policy legal and and infrastructure investment decisions who wants to take that money i mean i can start i mean you know in the open source within say the ally government's open source intelligence has an 80 year history of driving policy decisions um you know the the original warning of pro harbor came from open sources um it was actually fibbuses or fbms is very first report um so there's a long there's a long heritage and a long history there of trying to understand media narratives trying to understand these types of open sources and understand that exact question how do we translate that to policy how do we understand the biases the the various pieces that play into that so the short answer is there's a long long history what we've lacked is the tools and technology to focus globally um strategically rather than merely tactical of kind of the the the issue of the moment so harry i'll i'll go next and i agree there is a policy resistance towards um sources um but i also think broadly if you think about policy i think about like i i put i will put a marriage sheen risk management in that in that box and let me tell you if if you give fema 60 percent accurate thing they'll take it because they don't have anything else and so there's there's some land going on in the pipeline we as humans hate uncertainty and don't like to make decisions about uncertainty but guess what that's the future the future is full of uncertainty and so we need to be more a scientist and and more open to talk about error bounds and data quality and traceability and all these other principles that would allow policy makers to understand that we've done our work to give you the best information that we can but forecasting is is as good as the data that we have and so here's some investments that you can make if you want to improve the confidence of these predictions for example night time night everybody's asleep at 1 30 a.m in the morning so guess what half of africa all these maps that you see on night time nights they're not that really useful because electricity decisions are being made such that you know turning off the lights to you know maintain energy storage and so we need to think more broadly as a science community of what investments we can make to have to have more optimal global remote sensing portfolios that are in service to the humanitarian community i'm hopeful that you know this group would help advance those causes so that we can tackle the issue of uncertainty but you know trashing trash out if you're not going to you need to spend in make investments and fund research on frameworks that allowed you to reduce those uncertainties if there if you don't do that you're not going to get good policy outcomes so it's up to the us to communicate that to the policy makers yeah i'll just add i mean uh miguel you you said very closely to what i was going to say and i i can't recall if i said this during my talk it's my brain's little fuzzy but zoom but invest in traditional i mean on the demography side of thing invest in the traditional demography methods we have to invest in our censuses we have to invest in having people on the ground and understanding um individual communities on a very human level and i i love remote sensing and i use it all the time in my research but as miguel said very well garbage in garbage out and and i really worry with machine learning uh and artificial intelligence becoming where i see funding going everywhere that those models are only going to be as good as your training data and if your training data on the human side of things isn't valid then your model's not going to give you the information you want so traditional investment in real social sciences i think is really important as we build out these technologies all right let's see if anybody has another question while people are making up their mind i wanted to ask one question which is you know in our title of our panel we use the word ethics and as we as you all discussed the different kinds of measurements and analysis techniques for data i wanted to ask all of you your perspectives on you know how ethics should be playing a role and and i can see this with with one observation which is the the work that i am involved with you know for many years is you know identifying human settlements on uh from satellite images um you can pick up these refugees and IDPs in very sensitive conflict zones and that data being if it becomes public becomes incredibly dangerous because you are putting human lives at stake of where people are taking refuge so it became an ethical debate about you know what is the right thing to do you know do you release those kinds of data sets for public good or is it something that we should protect so i'll open it up to the three of you to see you know from your perspective how would you characterize the the role of ethics i mean from a media perspective that's one of the nice things about looking at at news media is again that that openness there i will say that you know certainly with cdr's and social media data you know i've you know in our past life wrote extensively on the data ethics area um and you know it is one of those scary places where you know facebook for example has this disaster mapping program it makes it available um but there's a huge amount of concern there because a lot of ngos um you know even major ngos like the un um you know there's a lot of interaction with governments and repressive governments can get access to some of this information um you know and and there is a shift i mean you know whether it's pn you know pna s uh triple as the journals um you know the proceedings of national academy whether science magazine um now actually uh the apa as well as journals um all of you now have done shifts to say a lot of these traditional like the common rule doesn't apply to social media it doesn't apply to a lot of this sensitive data um and it's really fascinating uh when you you know kind of look at the ethical shift that's occurred and it used to be that a lot of these data sets um used to be things that were considered very sensitive um and you know now um you know it was it was kind of shocking when you know pna s said um you know the all those rules about manipulation of social media monitoring all those especially for understanding population scale we don't see those as as issues anymore so i think that landscape is changing dramatically and it's changing in a way that benefits academics looking for data sets for publications not with but ignoring the very real dangers to society and i think that is a market shift um that i've i've you know gotten statements from all the journals now of yes we've totally changed on that now we don't really see this at these ethical constraints the way we used to we have to prioritize publication we have to prioritize grant funding we have to minimize the ability of societies of citizens to protect themselves it's kind of a very interesting uh piece there that really is not getting a lot of discussion i think right big old casket so i like to frame the discussion of ethics and data in a humanitarian context in a more positive landscape because i see and i have witnessed how this data can also be used as a mechanism for transparency and accountability of those centralized systems that are supposed to be helping the forgotten when you show an image of the longest power outage in porto ripina and us history and you say where the people have to weigh the longest outages i think that's very powerful and it has ethical implications to put FEMA housing and urban development army core these agencies which us taxpayers pay to protect our communities to task the same can be said across the global south when we see all this helicopter humanitarian work going in you know feeding off of charities and then the work doesn't get done so creating mechanisms of accountability to track the effectiveness of electrification programs across africa is something is one example where there there are still some ethical dilemmas but you're you're trying to concentrate on on the so that the positive aspects absolutely true you know we have to be careful about issues of national security you know i as i join light as it's becoming a more i realize as i oh my god we've got a lot of stuff with this data but we also need to make sure that we offer solutions in terms of equity and justice great that's good uh booty i love what you said about our ability to detect populations who are vulnerable who with that information may lead to bad actions whether that's by government or other people and i it's something i think about all the time um when i contrast it with what mcgill said it from a ham humanitarian standpoint and i think i tend to be more glasses half full and a little naive as to how some of these data sets we produce are used um the second part of ethics that i've been thinking about is whether providing data human settlement data or hazard data or a climate forecast that we're not very certain about is better than providing no information at all in a humanitarian standpoint and i don't have a good answer for that but it is something i'm becoming more mindful of is as we as i produce more data sets and make more user friendly is commuting communicating that uncertainty but again whether putting in someone's hand will actually just lead to worse decisions or overconfidence in a decision-making framework and i don't have a good answer for that but it's definitely on my mind so i really appreciate this question bulu i'm sorry i would like to add the bad actors unfortunately there are more and newer bad actors who are figuring out ways to use this data in ways that impact uh the spacements and i'll say a lot because i don't care look at air bmb and the land grab that happens after disasters where they go in and say oh we're gonna get free rent to people yes but then your your pop overpopulating this air bmb in in places where land is extremely limited and now people cannot live there look what happened in barbuda where people are had to evacuate and when they came back their lands were taken away uh by commercial bad actors so i think we have to be you know if you're like i say very ethical about policing not just the traditional conflict driven actors but the the ones that are actually intensifying the inequalities in a displacement context you know and i'll add one piece of that you know our our approach has always been to work with local communities you know so much of this sort of data analysis is you know folks here in the u.s or europe using mass amounts of data and describing you know like taking side imagery and analyzing it and saying here are the refugee settlements that we're observing in this country here's what we're seeing um you know our philosophy has always been the opposite of working with communities and saying here's what we can observe about your community um what of that is useful to you what of that is sensitive to the general public and allowing them to make those informed decisions and i think that's an inversion of the academic world because again academia that is an academia but i think that's something that really has to maybe become sort of the future of when we think about this because at the end of the day these are real people's lives and if you know professor in america publishes this big paper saying hey look we found all these all these hidden settlements here and we've mapped out where all these people are going and that's an area where you know they can run right into to death i mean that that's a huge thing uh great um i don't see any other question so and we have about uh four minutes remaining so um i would like to solicit your last thoughts um on you know do you perceive any technologies that might be emerging um in in in the next you know five to ten years that would significantly you know impact or influence this particular area that's a good one um you know i honestly think that we have a technology backlog we are swimming in data and we're still like look at our nighttime nights we're still trying to make sense of new technologies and we're living you know at least on our sector in an embarrassment of riches when it comes to data the problem as you have stated and others have stated is that policy making isn't catching up to to these advances and you know like i couldn't understand i mean kyle kaleb put it perfectly you have you know the government way of doing it you know that other academic way of doing it we're so ahead of the curve in in being able to provide informed decision making and yet it hasn't scaled and i'll be very frank it hasn't even scaled in our decisions around the future of investing in science programs you look at the current science decadal survey you look at reports that i talked about i think um the cascade was talking about on the climate assessment they're they've already expired and so i think part of it is you know what do we do with what we have i i'm looking i i know dr karen sanger may say sometimes at nasa our our um our headlights are looking down they're not looking out because we have so many things happening great thank you miguel any last quick thoughts yeah very very quickly out i will say i'm most excited to use the technologies we already have them and and work with communities on the ground who may not benefit from them i mean one area that i working on more recently is just extreme heat and and population exposure and just working with local communities on proving weather forecasts because we already have those technologies and they're really good in some regions world and they're not so good in other regions world but we don't need to reinvent the wheel for a lot of these regions that don't have good weather forecasts so it's i know that's tangential from this conversation but and i'd say from from my perspective you know one of the challenges we have so much data today um the trick is what we lack is analysis um you know our government the intelligence agencies for example um you know if you look at say the cia museum um is filled with things to collect information there's not a lot of emphasis on what do we actually do with it when we have it we you know we kind of as governments we and academics we specialize in in acquiring these vast rows of data um but the end result is how do we actually what we lack is those those tools and we have actually a lot of the technologies it's the the methodological workflows to to leverage the tools and data that we have to make new insights from what we already have great so with that i wanted to thank Miguel casquette and Caleb all of you uh and pattern harvey for planning and designing this a wonderful panel this was fascinating one of the best i have um seen in recent times in terms of the depth and breadth and and the insights that you have shared so i'm going to turn it over i guess we are going into a break right now is that correct yeah so we we um we are going to go into a break um and then we reconvene in about 15 minutes at 345 for our last and not pleased and the most exciting panel on mixed methods analysis of migration displacements and human dynamics that and the panel will be connected by no other than our um wonderful colleague Elizabeth root so um see you everybody at 345 hi welcome back folks um we are about to start panel three um which is on mixed methods analysis of migration displacement and human dynamics in this next session we have three separate talks on using mixed methods to analyze migration and displacement and human dynamics and again as with previous panels each talk will be about 15 minutes and we'll hold questions from the committee and all those of you who are listening through the webcast um until we've heard from all three of our panelists today i'm fairly excited about this uh session we had a great meeting last week to sort of touch base uh with the panelists and it's clear that we have a really interesting mix of different ways in which people are measuring and visualizing and understanding human mobility um and it offers a great diversity um across a different space and time scales and whatnot so with that our first uh speaker is uh samaya dodge from the university of california santa barbara so samaya i will hand it over to you thanks elizabeth uh let me share my screen do you see my presentation next slide yes it looks great great well thank you so much for having me i'm very excited to present a portion of my work regarding to mobility and responses to cove specifically as a environmental disruption to our daily life um um it was a fantastic first um a few panels and i really enjoyed the conversations and the discussion and i think what i'm presenting today really relates to the discussion we had right after the last panel about data quality and representative mess um so starting with the pandemic um a lot of companies and organizations started to share um mobility data at different scales especially and temporally um these data often we are calculated based on cell phone traces like if you are using cell phone and we have location services on uh we've been counted so on the left lower part animation you see an animation that was created by new york times about raw cell phone traces obtained uh by cubic company and they showed that uh you can learn a lot about these people although they are anonymous traces although not always we have access to gps traces for good reasons because of privacy issues but these traces are used to calculate aggregate mobility indicators that indicate how much for example people move in general and how much or how many people are moving so a lot of companies uh like a cubic the scarlet slab safe graph facebook apple map box started sharing this data with academic community to basically explore the patterns that we see in the data and this data has been used a lot uh with uh during covid for different studies like looking at the impact of non-pharmaceutical uh interventions such as stay at home orders on the spread of covid or modeling covid itself and also it has been used to inform policy uh in terms of making um uh non-pharmaceutical intervention policies so there has been a lot of studies and publications in the area but oftentimes these studies focus on one um particular data sets or use a data set or two sources of data to make these analysis and modeling what we wanted to do here use this data to look at the quality that data represents and also uh basically uh to see whether different data sets gives us different story in terms of mobility patterns in space and time throughout the pandemic in 2020 we started collecting a lot of data set from different companies and look at the patterns so here what i'm showing you uh is just four uh sources of data obtained from place iq the scarlet slab cubic and safe graph because these were the four sources that uh basically provided their user base number of cell phones scenes that were seen in their data set so if you correlate this with the population density of these data sets well the population density of the counties because these data sets are obtained at a county level um you can see some uh some of the counties that represent large metro areas such as Los Angeles also Harrison County in Texas Texas or um Cook County in Illinois there is an under representation of actual population with this data set so we need to be mindful about what data source we use and how the population is represented in this data set next we looked at about 26 different index uh indices from nine different sources and looked at the coverage in the spatial the spatial and temporal coverage of the data so how the missingness of the data or how much a coverage is available so here the color basically represents when the data is complete you see darker colors when the data is incomplete meaning that there are missing dates that are represented in each county you see lighter colors so um some of these sources provide really complete a complete representation to look at the year however some uh free sources like google visits and uh apple data sets there are a lot of um missing data in the data set similarly for the temporal representation of the data we have counties that we don't have data for several weeks so we need to be careful about when we are averaging and using this data set uh when there is missing information so then we wanted to explore uh and compare the patterns using different data sets and for that we use spatial autocorrelation and try to cluster to find clusters of mobility patterns throughout the COVID-19 pandemic at different locations using county level data that they're obtained at the weekly basis or average at the weekly basis so using lisa we created uh visual analytic tools that has ability for the users to explore these patterns computed with lisa and the basically patterns show hot spots and cold spots hot spots represent higher values clustered together with neighboring higher values and cold spots represent lower values close to other lower values so in general without giving much information at this stage because i'm going to focus on different states later and give you more information we can see a general agreement between cubic as a data of this data source that you can buy uh in this data so you need to pay for a license fee and save graph as a free source of uh data set so um and i'm showing two different indices one is mobility index or a median distance travel which basically represent how far people moved and then the shelter in place basically represent the percentage of people that stayed at home during this timeline so if we looked at these different maps we see a general agreement between the shelter in place data set between cubic and safe graph which uh there are a lot of cold spots in the midwest and south meaning that less people shared that in place and more people moved and there are some hotter spots in the northwest and east sorry northeast and west representing less people moved and more people shared in place however we don't see this agreement in the data source that represent the the distances travel and one reason could be because of the way these uh indices are calculated the algorithms to compute distance is more complicated than just counting the number of people so the tool is available uh at this link that i provided if you want to download and explore and there is an interactive version of it that i'm going to show next uh online so the visual analysis that we created has two components the first component is a hotter spot um and called a spot recency and consistency map which represents basically how often and how recent different counties were classified as a significant hotter spot and cold spot you can zoom in and see different counties in different states and basically the size of the dots represent the consistency in the behavior of being a hotter spot or colder spot the size of the larger dots represent the the frequent um cluster and the color the lighter color represents the older clusters in the timeline and the darker color represents more recent hotter spots and cold spots and then the next component is when um so previous component you would could see more spatial patterns here we are looking at patterns over time how consistent these clusters were over time so if you say an angled um line at 45 angle you you will see a county basically represent the county that was consistently a hotter spot or colder spot and horizontal lines indicates that uh the county changed behavior to not being a significant colder spot or hotter spots next i will um um basically uh this um uh representation indicated we can use that to look at how different time of timelines of covid impacted mobility patterns for example we can see in california and california people started to shelter more in place like less people moved as uh when they stayed at home order started but then in california we see a changing behavior that is when the uh show social distancing um maybe orders left that there was no significant hot spot colder spot and towards the end of the year they again there was an another policy which made people stay at home more so now here i'm zooming into georgia as an example to look at how the patterns show different um uh different information uh in terms of spatial patterns and temporal patterns so looking at these two uh uh data obtained from cubing and safecraft basically in general we see the same patterns differentiating ruler versus errant counties especially for atlanta which exhibit atlanta exhibits uh basically a hotter spot behavior but we see some differences between the two the data said although they both agree that atlanta was a significant hotter spot meaning that more people stayed at home however other places maybe lost um that are not in large metro area we see a significant colder spot that oftentimes also reacting to different timelines of covid or political situation for example uh we see a uptake of people moving more after the presidential election or uh around the timesgiving um like timeline uh holidays and also we see a change uh and generating more hotter spots around the second peak of the covid um spread uh around july 28th so another uh very interesting pattern uh is observed in south cacoda although we don't see that distinct uh differentiation between ruler and large urban areas we see a very distinct geographic patterns that is separated using based on meso sapriware so we see an east river region and west river region the east river region is mainly it makes mostly corn and meat agricultural production areas and west river region predominantly are ranch and uh dry land farming and mining operations and there are some uh indian counties that represent um native american uh majority in uh basically the west um areas so if you look at these patterns we mainly see uh hotter spots in the west and colder spot in the east however the recency and consistency of these patterns changes with the different data sets we see also uh we we don't see a very significant response to uh like all through time if you look at the timeline however um but and maybe it's because south cacoda didn't have any state wise uh stay at home or their situation here so um to basically recap what i showed uh here um there is a summary of all the states that we just talked about and the percentage of number of colder spots and hotter spots created based on the data sets used using travel distance and shelter at home so if we had consistency between the across different data set we would see all these colder spots and hotter stuff hotter spots would lie now however we don't see that here so i would like to basically summarize um these points on two different aspects of what i just present one would be focusing on visual analytics and how we can basically how we leverage visualization to inform our quantitative analysis so we basically combine qualitative assessment of what we see in terms of patterns with quantitative analysis of these patterns and what the data represents so it is important to bring the mapping and a qualitative analysis into quantitative modeling as well and also it is important to look at how this basically um visualization actually inform us so we need to do some user studies to make sure that these visualizations are not misinforming us either another aspect that i would like to touch on is data quality and uncertainty that we had a discussion about so basically one important aspect one issue that might come up is because these um there is no standard on how these mobility indices are calculated different companies have different algorithms sometimes the algorithms changes through time as well so even the comparison between the same index from the same provider might be fuzzy as well there is obviously a short date there is the bias in the data and representativeness issue that we need to consider and also these data we are not sure what population they are covering because we have these data so compute these data at like aggregate level and not a specific population level so we need to perhaps inform this data with more information about socio-demographic and geographic structures of these communities for example here if you look at the timeline of uh from 2019 which is before covid like the year that was of normal lay year and then the next year 2020 when covid happened you see a change in the behavior when higher income population started to moving less than before and where uh lower income population were more impacted and their behavior fully meaning that they even move more than people with uh of higher income and with that i would like to stop here and happy to be to answer any question and i would like also to thank bias students who contributed to this research thank you so much samaya that was really interesting and i just really appreciate um a critical view and a comparison of different sources of data that is so widely used and adopted especially as we saw during the covid pandemic so just wonderful presentation thank you so i'm going yeah i'm going to uh introduce our next uh talk which is actually by a team of speakers you have kate hess and matt woodleaf who are from esri or esri who will be presenting um uh in the next round so matt and kate i wouldn't there we are thank you so much we'll turn it over yeah thank you uh it's one of the thank you uh to the um the group for inviting us here it's really exciting when we get to you share some of the stuff that we've been been working on uh so we're going to go through uh a little presentation here and on using mixed methods approaches to understanding migration with the gis so since it's founding in 1951 the united nation's high council on rubies the unhcr has been tabulating displaced persons uh in 2017 the unhcr produced the story about the uc on your screen right now and at that time more than 50 million people have been forced from their homes due to war sectarian violence uh a natural disaster so if we fast forward just five years the unhcr now estimates that there are 80 well 89 89 million people have been forcibly displaced we felt that these rod numbers however we're not really telling the whole story we wanted to find a broader story by bringing together the different patterns of data and creating a narrative that could answer questions that says where are people fleeing from where are they going and are there countries that are more desirable for those seeking asylum and lastly are we able to tie specific government policies to an increased number of displaced persons so once we had these patterns of data at hand we wanted to get into the personal aspect of the crisis so in addition to looking at the refugee numbers we needed to assess refugees at the individual level and do some qualitative analysis so once migrating do the refugees feel safe are the conditions in refugee camps adequate how do individuals feel about the future and maybe most importantly what can we do to help so the process we followed was to quantify the migration of displaced persons first we wanted to know which countries they were leaving and which countries they were turning to for asylum to do this quantitative analysis we used a flow or a link map and we plotted the country of origin and the current country of residency by first spatially enabling the unhcr data set by geocoding the countries based on their central then we calculated the total number of persons of concern and symbolize the line to connect the origin and the destination immediately we saw the scope of the crisis there is not appeared to be a single country not affected by the refugee crisis and through this simple mapping method we were able to get some key insights first by symbolizing the country's country's circles by in degree we were able to identify countries that had refugees coming from many more locations we normalized the node values here to show the comparative difference between values of this person per destination country as you can see on the map the united states canada australia down here and norway and sweden make up a large taking refugees from all over the world so their nodes are comparatively larger than the other nodes on the screen we are also able to illustrate the flow of refugees by varying line thickness based on the calculated total so let's look at the syrian era of republics for an example we wanted to know in this case the line thickness here varies depending on how many people are connecting the origin to the destination so first you see syrians fleeing to turkey we see them fleeing to neighboring lebanon at one point we actually even see irakis fleeing into syria right but the most important or the most intriguing era to us was the one that is circling back in upon itself this era represents the internally displaced people is the thickest line on our on our chart our map so we can assume that most syrians did actually not did not flee to neighboring countries but sought refuge within their home so the map provided some key insights but as i mentioned earlier the unhcr has been collecting data on refugees since 1951 and the origin of refugees since 1960 i want to show you what the raw data table looks like in this raw data table we have nearly 120 000 records representing millions of people right so as such while the map could provide key insights it was much too hard to identify patterns and and gain a significant understanding of the situation but it still pointed us in an interesting direction so we decided upon using an interactive timeline to drive the other visualization let's take a look at 2017 so as i click through this interactive timeline from 2017 to 2021 i want to pay or have you paid special attention to this summary total down here of internally displaced persons so like in 28 27 to 2018 we see a while the numbers all change we see much bigger jumps within this population including this 2.3 million up 2.3 million person jump between 2017 and 2018 and then again another 2.3 million person jump between 2020 to 2021 and so this insight here trigger another round of analysis for us we shifted our focus to analyze internally displaced persons we wanted to gain an understanding if if or how policies established by government had any influence over whether people left the country entirely or stayed within the borders of their homeland and for this we looked into the Rohingya crisis in my mr as a case study so the Rohingya have long been targets of violence both by individual groups and the government and in fact in 1982 the citizens of the law specifically left the Rohingya off the list of national races and prevented them from ever getting citizenship this is significant because the Rohingya are now classified as stateless persons i'm starting in 2000s here in the mid 2000s the government began a household registration campaign in which families of Rohingya were gathered and a photo was taken and anyone not in that photo would not be part of the family and could not legally stay in Myanmar and as you can see in the bubble chart here many were able to find refuge in neighboring Thailand however Thailand soon enacted policies to slow the admittance of Rohingya into Thailand and we see that reflected in our data set in fact many of those policies began in 2006 and we start to see a couple interesting patterns emerged here first Bangladesh it makes its first appearance as a destination and we also start seeing our first internally displaced persons let's fast forward to 2010 in 2010 the government of Myanmar began its violent crackdown on the Rohingya many were now able to flee to Bangladesh and it became the country at which most thought asylum however in 2012 the Bangladeshi government officially tried to close its borders to Rohingya and in this case we see a decrease in country asylum being Bangladesh and a massive increase of internally displaced persons in fact it's 62,000 in 2010 it becomes over 430,000 in 2012 there's also in 2012 where authorities in Myanmar forced the relocation of Rohingya into impermanent camps and severely respected their access to travel work education and healthcare the last part I want to go look at is in 2017 in 2017 in response to an attack on police posts increased by Rohingya militants the military of Myanmar began a massive scale clearance operation and left with the choice of being killed or being forced to flee their homeland many Rohingya decided to flee to Bangladesh and we see that reflected in this part nearly one million people left the homeland to seek refuge and to this day in 2021 Bangladesh remains the country that most seek asylum into but however Bangladesh is not where our story is instead it's actually the beginning of the next phase of our analysis I'll turn it over to my colleague Kate great thanks Matt so we've spent quite a bit of time quantifying the refugee crisis but the crisis is more than just the numbers in the workbook we wanted to explore the human condition and really qualify the crisis so what do they need and do those needs have seasonality how do they feel about their situation and again how can we help so using geospatial solutions we're able to take the static data tables and tedious data tabulation and turn it into more real-time analysis this dashboard shows how survey results can be tabulated in real time the first tab here is the survey as it was received from UNHCR upon download it asks good questions but we wanted to add some additional questions that would better assess the spatial and temporal resolution of the crisis so we've added questions like how long have they spent in the camp and from which region of the country did they come we also added some semi-structured questions that would be more amenable to a qualitative methodology these allow us to use thematic coding and are less constrained than the other question types this is important because we're seeing gaps in the data versus the lived experience of refugees for example when it comes to sanitation the results from the survey show that a vast majority of people are satisfied with the sanitation conditions in the refugee camps but by conducting proximity and walk-time analysis we can see a different story each one of the rectangles here are tents that were detected using geoai methodologies from drone imagery shared by the international organization for migration after analysis it was concluded that 37 percent of the population living in these refugee camps did not have adequate allocation of washroom resources so that's nearly 12 000 people eliciting these gaps in the data set directs us to evaluate the survey instrument and can lean to refinement for the next iteration of the survey this highlights how by using a mixed methods approach we can use quantitative data to understand where a more qualitative analysis may be needed from our approach we realize there is much more we need to learn about the individual respondents to the survey and discover why their experiences were not that of the majority we've summarized this work in a story map where we can provide the background and context to the analysis and we'll share out a link to this as well the second half of the project we've shown today the goal was to evaluate satisfaction with the quality of life within the Rohingya refugee camps so this ties specifically into sustainable development goal six which is available clean water and sanitation now if we're just looking at the UNHCR survey findings we get the impression that sanitation is satisfactory in the camps and people are happy with the conditions saying quotes like that the available hygiene is improving but when we bring in the additional data sources and approaches like quantifying buildings and washrooms from aerial imagery and including more open-ended survey questions for additional qualitative input we're able to get a fuller picture of those conditions on the ground and in this case identify where there's still a lack of sanitation facilities ultimately having these more comprehensive data-driven insights enables decision makers to help people in the ways that they need it most so here we have a list of some of the changes that have been made to Rohingya refugee camps as of June 2020 including constructing new latrines and bathing facilities and increasing the number of households that have access to clean water and soap and we've included a link to the full report here where you can see additional details on the actions being taken on the ground to improve sanitation and safe water access so in this exercise we identified maybe the three main conclusions so first insights across a diversity of visualizations are necessary to tell a cohesive story rather than just the diversity of the type of data collected this allowed us to find gaps in the traditional narrative around refugees people will stay where they feel safe and in the case of Rohingya they had no other option but to leave their homes due to government policies secondly the iterative process between quantitative and qualitative was essential to understanding if the numbers reflected the sentiment of the Rohingya the iterative process allows also allowed us to refine our survey instrument and get into the why behind the where and the what and finally collaboration between the methods and data sets along with the personal collaboration allows us to dig deeper into the crisis and go beyond the numbers this collaborative analytic approach allowed us to see the story more holistically and to find questions that could help us fill in the gaps between the data and the lived experience with that let's turn to the next presenter thank you thank you so much Kate and Matt that was really really wonderful I loved the visualizations and this your description of the iterative process that really brought in qualitative understanding of what life was like on the ground in these refugee camps just really is a great exemplar of how we can do mixed methods research and integrate sort of the lived experiences of people on the ground so I'm sure you'll I'm sure the audience will have tons of questions but I thank you for your presentation and I would like to introduce our final speaker Marie Urban from Oak Ridge National Labs Marie over to you hi thank you let me share my screen oops I have to hit the share button sorry about that there we go all right looks good great thank you so thank you I appreciate the opportunity to talk to you all today and I've really enjoyed this whole discussion this afternoon as well so I'll be discussing the application of qualitative and quantitative data around IDP and refugees for population modeling so since the late 70s or NL has been involved in developing spatially refined estimates for populations at risk so initially in support of facility siding or shipment of hazardous materials and because of this research or NL develop landscape a global population data set for consequence assessment delivered in 1998 and continuous annual updates since 2000 it is the first global high resolution population distribution data set representing an unworned population so this unworn population is a more realistic spatial representation of population across activity spaces such as home work or school since the release of this global data set was nearly 25 years ago which by the way was well before geospatial data or the high resolution imagery that is now available and through leveraging our high performance computing computer vision and machine learning capabilities there is a continued refinement of the spatial fidelity of land scan with each new release our next release is land scan 2021 which will occur next month so this is a high level representation of the land scan remote sensing based global data modeling and mapping method I know cascade talked earlier about grid of population data sets and so that's kind of a nice segue into what I'm talking about today specifically about land scan this is land scan is a top-down multivariable asymmetric population modeling approach conducted by disaggregating census counts within subnational administrative boundaries so the land scan population distribution models are tailored to match the data conditions and geographic nature of each individual country and region so basically we develop a likelihood surface so where people are most likely to be found and this is performed using ancillary spatial data and high resolution imagery exploitation we then distribute the census populations to small areas within the census units based on the values in the likelihood surface the displaced population IDPs and refugees are then accounted for through data driven mixed methods so you know I don't have enough time to discuss everything about the land scan modeling but you can find more information out at landscan.orinal.gov so what causes population displacement and resettlement you know there's a lot of different events you know short-term events like conflict or persecution social and political instability natural disasters and then there's the long-term events right food insecurity water stress but then the short-term events can become the long-term events as well and then for you know kind of the typical background migration your census captures those movements so here the Kosovo refugee crisis was the first land scan update to account for IDP and refugee movements we used open source information humanitarian reports and media about locations and displaced populations to update the population distribution so by the end of April 1999 about 600,000 residents of Kosovo had become refugees another 400,000 were displaced inside Kosovo overall there were two million residents in Kosovo nearly half of the residents became refugees or internal displaced people so you can see that largest spike in the graphic below is about 375,000 Kosovars move moving south to neighboring Albania and then there's about 150,000 who moved to Macedonia and you can see those spikes throughout the graphic below and the movement of the refugees and IDPs so normally we identify new or monitor continuing events of population displacement through humanitarian or other open source resources however you know you know some events are rapidly evolving situations that require assistance immediately so if we use Ukraine as an example being the most recent continuing conflict where bordering countries and humanitarian organizations provide housing and supplies to IDPs and refugees the other example is as kind of piggybacking on Matt's discussion about Myanmar the Rohingya how in August 2017 there are nearly one million Rohingyas that refugees fleeing to Bangladesh to settle in Cox Bazaar refugee camp and then of course smaller numbers of refugees in surrounding countries for these types of situations and this has been discussed all day there there's often a shorter term need for information that can aid in estimating damage and providing relief to effective people so there isn't you know a single authoritative reporting source but rather multiple organizations and media that report the rapidly changing situations for example UNHCR which we we heard about earlier global conflict tracker you know displacement tracking matrix by IOM and you know there's even with all those that reporting there's less reporting on the resettlement of refugees especially IDPs so it's often difficult to track the resettlement and last of all aligning disparate data sources and earth observation data for population modeling is it can be very challenging and so what happens oops sorry my apologies is that we end up with a really complex challenge and trying to account for refugees and IDPs in our population modeling so for population modeling on account of refugees you know from the qualitative data side of things you know there's cultural practices of things like offers to refugees or IDPs to stay with local families and so those are hard to track but sometimes you can get information from anecdotal reporting maybe from some of the discussion within media and other organizations about displacements like kind of the location to and from there's often behavioral surveys you know they they give some insight into where refugees IDPs were maybe initially displaced and then re-displaced we can also we have the the capability to detect change in growth and decline of camps and so that change can lead to pursuit of new sources to understand the population growth or decline of those camps and then most important for population modeling is understanding the building use and so if there's any changes in that building use it's imperative to understand that we often see building use change once there's a displacement event Ukraine for example people use museums theaters and churches as shelters which then changes the building function and the expected number of people within the building and then for population modeling that's significant the the last thing is you know it's useful to identify the destroyed infrastructure you know if a building is destroyed we consider it inhabitable and no populations would be distributed to that location so for quantitative sources we regularly collect open source and media population estimates to inform our population models for an area over the longer term humanitarian organizations and NGOs report registered camp populations those can include detailed camp descriptions and demographics so then we've also developed tools that exploit imagery and support of developing camp population estimates or capacities when those aren't recorded so here's an example of using overhead imagery to detect camp establishment and growth these images show the rapid construction of IDP camps due to the mass muzzle exodus between October and November of 2016 so using counting tools we can rapidly determine whether the capacity of camps supports the reported displaced population in addition to comparing with reported counts we can then pursue where the unaccounted refugees or IDPs may be found so this is also an example of how alignment of a reported population estimates in EO earth observation is important for land scan updates here's another example of using overhead imagery and reported estimates to understand population movements so border crossings are important for monitoring population movements and identifying camps this example is the Syria conflict and the opening of a former closed border into Iraq here the number of refugees crossing went from 68,000 to over 200,000 over the course of the year so what we typically see is that most refugees settled just past the border for minimum displacement but also in hopes of return so we pull information from social media as well for most more insight into refugee and IDP activities so these photos were pulled from discussion about humanitarian support this is an example of two different impromptu facility uses that result in differing population estimates over time so the facility on the left is a shelter and hosts a large population whereas the facility on the right is a distribution center so there's a large gathering of people outside the facility for the distribution center but that is only for a short time a short period of time for that day this example this is an example of our capabilities in automated change detection to determine habitable buildings so leveraging high-performance computing and machine learning capabilities so our algorithms are quickly detect buildings prior and post-conflict this is a maria pool so the damage buildings are rapidly identified to determine displaced populations and inhabitable buildings for land scan updates populations are not distributed to the destroyed buildings and so that's key for us to understand those here's another example of rapidly detecting change through building detection and counting to get us closer to the number of people affected by conflict in this case it's the Boko Haram who terrorizes city resulting in the loss of infrastructure and life so using our building detection in order in addition to accounting capability the number of sorry the number of structures remaining quickly provides insight into how many households displaced sorry let me go back so we have over 10 000 structures identified here and counted and then under 10 000 after the Boko Haram moved through the area here's a close-up where you can see the structures and the count and then afterwards really see the damaged or destroyed buildings and and your account it should be noted that this example occurred during a time when you know digital trace data wasn't readily available and it may not be available yet in many areas of the world and so being able to monitor see where people are moving isn't you know if we're looking at the global scale quite available yet however Ukraine was a great example of that happening I've seen many demonstrations of watching the the populations moving outside of Ukraine to neighboring countries so Orinel developed the capability to continuously identify areas that may be experiencing power outage using beers nighttime lights this is a dashboard the the legend on the left shows black as decreased lights white as no change yellow as increased lights and gray as cloud cover so in this image there's quite a bit of cloud cover but you can still get a glimpse of decreased and increased lights in parts of the country this capability supports rapidly detecting changes in outages especially during natural disasters and gives insight into who is affected and may need support so if communities are without power for too long they potentially begin moving where they can find power and shelter so last of all this graphic is a visualization of population dynamics during the early stages of the Syrian conflict so by now and the estimate of internally displaced people is risen to between one and a half and two million the intense fighting is in densely populated urban areas cause more and more sorry the the intense fighting has caused more and more people to flee their homes seeking refuge in schools dormitories mosques and other public buildings so many have been displaced more than once fleeing from hams to Damascus for example and then moving elsewhere in search of safety and so you can see the large movements of population since we did a a subtraction of the population to get a better understanding of the movement so understanding the losses and gains you can see many of the populations at this time and this is of course in august 2012 have moved north outside of syria and south as well and that's the end I'm happy to stop sharing thank you so much Marie again another wonderful example of how we can use sometimes called nontraditional data sources but in reality lots of different types of data that have both a qualitative and a quantitative aspect to it to help us understand really complex social problems so thank you so much for your presentation I first would like to offer the opportunity for the committee members to ask a question if anybody on the committee go ahead and raise your hand in zoom if you'd like to ask a question and I would say again remind public attendees watching the webcast that if you have an idea or a question that you would like please put it in the slido and we'll work those into our conversation at the end of this panel so committee sorry any questions harvey please again thanks for thanks for another great panel I don't know if this is a well-formed question but something I've been thinking about is that when we talk about mixed methods it seems like we're still loosely coupling quantitative and qualitative data we can see that with story maps we're basically we're telling interesting narratives but we're just basically georeferencing like media I'm wondering if you could speculate what are better ways to think about next generation of integrating qualitative and quantitative information I mean is is there a better way than just you know um measurements and exploration or using qualitative data to to inform how we do our quantity how we interpret quantitative findings is there a better way to bring these data together like I said that may not be a very well-formed question but I I just have a feeling that we're still in the first generation at least in the geospatial realm of integrating these types of data and there there are more advanced ways of doing this yeah I guess I can take a stab at answering that question um so I guess maybe one of the more um exciting things coming out of geospatial is the ability to map natural language processing so um from for example the the gdel that we saw earlier um we can take that information and put on a map right which is half the battle finding out about anything so that the context really does matter of where things are happening um so I think going forward and seeing those future developments in the natural language processing and being able to get the data faster rather than waiting or reports to come out or being able to download some data and have to explore to find our narrative like Kate and I did um I'm optimistic that can be something in the future that will help us explore data faster and understate it faster maybe provide a quicker link between the qualitative and the quantitative I don't know if that was a well-formed answer but that was my it's as good as my question thanks Matt are there other panelists Maya yeah I I would add maybe um you know using leveraging visualization and visual analytics maybe we can develop more flexible visual analytics tools that are better coupled with like both bringing both qualitative and quantitative data set together so in the background in visual analytics we have data machine learning techniques that are totally on quantitative data using quantitative data but then we map the results can we do that on the fly by integrating like information from imagery and as Matt said like natural language processing to mine a qualitative data as well as quantitative data and map it that would be interesting thank you so yeah so um Marie really quick um I'll comment I think one thing that we're experiencing and I I think it was discussed earlier is that we're in this huge you know we're we're drowning in data right we just have so much of it and I think you know at this point we really have to focus what it is that we want to to answer or we'll get out of of the data and we're we're in a position now where it will we can't just I don't know I don't know if anyone does this anymore but you know use one or two you know data sets it's going to be pulling in multiple so you know like I talked about earlier we we pull in so much different data and it's almost you know making sure that we have if it's qualitative quantitative multiple sources to to verify you know what's the quality of this data as well and so you know moving forward I mean I think we'll continue to develop amazing tools to sort and sift through and understand the data and be able to answer you know continue to answer some challenging research questions but also um hopefully take into account um you know how how certain the data is as well hopefully sorry if that was a little crazy but a little different oh that was good thank you thank you all yeah thank you so much um I have two hands Riz Riz and I think uh kirsten you were you had your hand up first yeah thank you um you you sort of answered some of the questions that I had um and harv um was touching upon them because it seems as though your session really wrapped up nicely what we started in the beginning of the day where we were talking about um you know the real-time data and Anna was talking about the you know the need of that and then Lydia got into more of a design process and then you know Steve was looking at this communication um and so I guess it's sort of a question about again back to all this data and Marie you in particular I think we're starting to bring it into what are the crises and what data is needed in each type of crisis that's happening and it seems as though all of these um approaches need different types of data and so I guess maybe a question is is anybody out there trying to organize this into here's the data that you need you know for this type of crisis because there is so much data out there flying around and I think it's it's uh almost overwhelming for organizations and policymakers to try and figure out what bucket does this fall into so is anybody doing any kind of work related to that and I don't even know if that's even a question or a comment or just thoughts about it yeah I mean so I've uh I remember as it pertains to the Ukraine crisis some of the departments in in the UN and WHO uh have been trying to build collaborative environments where they can transfer data back and forth but still maintain the integrity of their own data sets so they can still choose what to share uh maintain that control over their own data but make sure that the data transfer is faster so rather than having to to wait right it's that's a big problem we have to wait for this data well if you have it I know that you have it we have a collaboration let's just spare it um right away right sorry and these are acting up acting upon it so I think we'll see so maybe more of those really more of those collaborative environments to to share data uh faster could be could be a key in the future I can I might say can I can I add one thing so one of the things that I've observed is that there's you know while we have a lot of standards sort of metadata standards that have been created for quantitative data um and and there's there's not one metadata standard although there's a lot of effort I think that's been put into trying to create standards around how to structure data there's still a lot of sort of diverse um you know different groups are trying to develop their own so I work with the food FAO right and that world food program and they have just created this new dataset called gift which is supposed to curate and bring in all these food intake and food related datasets from around the world and the metadata that they have on their website is structured differently than metadata that I would pull down from say the iphone's effort which is looking at more population surveys so there's still this sort of diversity in what's required of a dataset to kind of create a repository or even information in an informational repository about what's out there I would say one of the great challenges in qualitative data is that those standards really don't exist right and it's partially because of where this is coming from and what disciplines it's coming out of the more qualitative side of the research that's been conducted but I also just think there hasn't been sort of more of a top-down like we've got to create some standards for qualitative data so that we could more widely disseminate it to people who might be interested in integrating it and using it so I'd say that's actually a gap um that that we could um look into and and perhaps fix although if not everything's fixable but at least start to structure that data so it could be integrated and more widely used um Samaya sorry yeah I also wanted to touch on that in regarding a metadata standards and having a uniform policy or like a centralized way of like collecting a repository of mobility data that can be used for looking at you know how mobility is changing in response to disruptive events so the problem with mobility data is that they are collected by commercial companies and some organizations and there are different policies different standards the different ways of computing and movement indicators and the problem is that um a lot of these companies sell their data right so these are not free data sets and like for example like satellite imagery is land news data these are easy to be coordinated with different companies and have a centralized repository or a data source for that however with mobility data is basically the at the discretion of the data owner how they want to share it how much they want to share and how they are computing it and there is no effort to bring all these different sources of data together and that would be actually a challenge and next step to look into how we can create a standard or central repository of such data sets excellent yeah thank you so much I think this sort of idea of standards around qualitative data is one that is really important actually as there's sort of uh you know we talk about the exponential growth of data and I would say that that's no different for qualitative forms of information that we could use uh as data in our in our research in our analyses Miguel I see you have your hand up as well yes thanks Elizabeth I am very curious to hear perhaps some Kate and Matthew's opinions about sort of the proliferation of the digital twin model in in this area and I saw hey Matthew I saw you grin so you know we're all geographers I think in this room and we've been I mean my first SRE conference user conference was in Taiwan was in 2001 and I was in a meeting like 21 years later in Europe and they're talking about these amazing DRG and it's like that looks a lot like a GIS and are we repackaging you know I think they were trying to repackage a solution that is based on very fundamental geospatial analysis principles and it's partly I don't know maybe it's because industry is not aware of the state of the science what's the disconnect and what's sort of your opinion and there's a do we have do I have to now every time I wrote a proposal replace the name GIS with zero twin to get it selected I mean like I don't I don't understand what's going on so I just want to get your perspective within this context because I think there's sort of a integration gets into the issue of the integration that I spoke about earlier is that the solution or do you think what are your opinions personally you don't have to talk about you know your code you know your company's perspective but yeah I can take that one so so I would say with digital twins it is a bit of a just kind of fashionable term right now but I think it can kind of incorporate existing GIS concepts and then in some cases extends them a little bit further so I think just having digital twins exist at such a different range of scales you could have a digital twin of an entire country or of just the process is happening in one factory and I think digital twins really need GIS kind of as a basis because they're capturing the relationships between different processes and objects and that inherently is going to include how they're related in space so I think it's adding something a little bit new maybe to the existing field of GIS and what has been already kind of the standards of how people work with data but I think it is adding some new benefits to kind of how we can think about data and how things are related to each other and gives us really that ability to take things a step further from just visualizing how things are interacting now and performing kind of projections and looking at different future scenarios and how that will impact all of the different current components of our system of our digital twin and just wanted to add that I think the digital twin concept is really is about that data integration GIS is just a tool to help integrate that data but what you do with it at the end can help you build out so-called digital twins so what systems are you modeling the interplay between the natural systems in the built environment um how everything interacts and then how you can run analytics and models on those I think that's what kind of turns the digital twin from like a 2D GIS type of thing to a 3D full digital twin I guess I guess another aspect of it is the scale of digital twin like how a granule you want to have that model and how that invade the privacy of for example if you think about mobility I think that would be an issue to consider thank you I do want to allow one question from the from the webcast audience and I would like to bring that into our discussion so I actually have a very specific question about diastometric mapping and so the question was have diastometric mapping techniques been validated specifically with refugees and transient populations and the more specific question is do you use the binary method or something else so if one of our panelists has used diastometric mapping has a thought on that it'd be great to share um I think I can start I think um so the first question sorry Elizabeth is um has it been validated or was that specifically yeah validated specifically for refugee and transient transient populations um so I you know diastometric modeling is is really getting at how can you use you know how can you what how can you take this census data and disaggregate to what you know and and you're identifying if it's if it's a building um what type of building that is and then as you're talking about refugees and transient populations um those um fold in nicely because you're saying okay if we have a refugee for example if we have a refugee camp here's um how many people within this area um as I spoke before about you know pulling in different types of data to as as kind of um indicators of where people would be and um for the transient um you know and validation you know validation is hard um first of all because you know our if you were to like just look around you how many people are here we can count how many people are here at this moment like I can say I'm in my office at this moment how many people are in the building but in another minute that changes right and so validation of the population of those estimates is difficult from that perspective and when you start throwing transient populations in there that adds another layer or a dimension of um of difficulty um then your account you know how are people moving and um running simulations and understanding that movement and those patterns helps and can help with that validation um but again we get back to you know no one's staying in one place for you know um an inordinate amount of time and so so that that that makes that validation part uh a difficult thank you any other plots from our panel on dice metric mapping so we only actually have two minutes left um in our panel this afternoon um and so I did want to ask one quick question that we had discussed as a panel um and I I think that I think that one of the really interesting things about the different approaches that you've taken is that um you examine the dynamics of migration and displacement at very different spatial scale right and we're all geographers and so the space time nexus is we know is very challenging um but I might ask quickly for like two sentences or maybe maybe four sentences on how if the temporal scale of analysis changed the question uh how would how would the question you're asking your data change right so like yeah anyway so I'll leave it that maybe I'll I'll I'll go to Samaya first and then we can popcorn sure uh so um we were studying uh COVID as a you know even that we were looking at and how that relates to mobility so for that um the temporal scales that we need to be able to relate patterns of mobility to COVID was at a daily or weekly basis so if you had the data that comes in like monthly resolution then that would not be valuable valuable for the processes that you are trying to explore so I guess that it is important to understand what processes we are looking at in terms of spatial and temporal scales and whether the data actually matches and there's also temporal scale of the analysis matters that processes that we are trying to capture um I can um so for population modeling we're always moving towards a higher temporal aspect of understanding where people are and um I think you know Samaya's work she's you know getting at that you know minute by minute or you know high resolutions uh temporal scale um which is um pretty incredible and and um given where we are or what I've been talking about today and trying to capture uh refugees and IDPs you know just understanding um those at um you know maybe over a a different temporal scale which is more of months to years given where they're at and how they're moving um it's kind of puts it in a different dimension if that makes sense um and and changes the that question of temporal scale for our presentation I'll let Matt kind of talk about his section that was looking at the multi-year analysis but I think it's interesting with the survey and some of the drone imagery analysis that the section we were looking at today was really kind of a snapshot in time of just surveying people and how they're doing on the day they're surveyed and looking at the one day that the drone imagery was collected but I think it can get really interesting if you're collecting the information multiple times and then can start to do a comparison of how things are changing and maybe evaluating how the if you're looking at drone imagery that's taken on another day and doing a change comparison to just identify how many new facilities have been built and recalculating that distribution of facilities per per person and then comparing that with the more qualitative survey results and seeing how closely tied together those are with people's changing perspectives of the conditions versus what we can actually see or the conditions from looking at imagery. And looking at our temporal scale of the quantitative analysis um as unfortunately it's only it's only a year is year by year so I think it's the aggregation of that year but something that we were interested in exploring was the seasonality of migration. Do you people move more often when whatever weather is appropriate for it right? Does weather have an impact of it? Does it have any sort of relationship between what we call the fighting season and the off season? Do you people move sooner after you know holidays or religious events? Just trying to be able to find that information would have been I think would have been interesting to see how that changed the flow patterns of migration to have more granularity in the in the seasons or the or even months per year. So we're looking at our insights looks just like all right in one year all these people moved but they didn't move all at once right so people like to go back and try to understand now are they moving what's in the country are they especially the internally displaced persons are they coming from one spot to another or they're making multiple stops along the way and that wasn't information that we had that would be really great if you could find that. That's always the trick isn't it? So with that I believe that ends the third panel of our of our sessions this afternoon so I'd like to hand it back over to I believe Harvey to close us out for the afternoon. I thank you all again speakers it was a wonderful panel I really appreciate the time and effort you put in to sharing your work with us. Thank you. Yeah thanks for having us. Thank you. Yeah what a great afternoon thanks to everyone thanks to panelists thanks to our keynote speaker and thanks to members of the MSC and GSC for moderating these sessions lots of food for thought we'll be discussing this internally and thinking about what are the next steps that we can act upon all these wonderful ideas and thoughts and challenges we heard today. So on behalf of Pat McDowell the chair of the Geographical Sciences Committee and myself the chair of the Mapping Science Committee we're calling this meeting to a close I want to thank all again all the participants but also the audience for listening in I hope you learned a lot today and I can go forward and keep pushing the envelope in this very important area. Thank you all and with that we'll sign off.