Good afternoon, and welcome to the IUPI Center for Translating Research and To Practice Scholar of the Month Conversation. My name is Steve wig. I'm the Associate Director of the Center. It's my pleasure to welcome you to this online event. We're so excited that you've chosen to come and spent some time with us here today. So if you don't know, the Center for Translating Research and to Practice is the brainchild of Professor Emeritus Sandra Parono, who is a professor of Communication Studies, and has a translational scholar herself in the area of Privacy Management. Doctor Petrono's idea was for IUPUI to identify and celebrate and promote translational research here on the IUPI campus. I think you know we're a hotbed of research that is community engaged that generates or uses generated knowledge and solves complex problems in our community. And today is no exception to that. So we're glad that you've chosen to spend some time here with us. I have a few housekeeping announcements to get us going through the day before we introduce Professor Dan Johnson, who's this month's scholar. I hope you've had a chance to view his information on our website to learn more about his work. But what better day could we do this than today, which is earth day? So we wish you a happy earth day. We hope that you'll be able to take away information from today as we as we do take care of the place where we live. And I know I UPI is a great resource for a lot of research that is applicable to help us take good care of the place that we live. So as we begin to work together, you're no stranger to Zoom, but we do ask you that you keep your microphone muted, and so it keeps background noise down, but there is a period where when we get to the discussion that we hope you will unmute and and turn on your camera so that we can see each other and have a conversation. That's another one of doctor Petrono's great ideas is that we would have a space like this where we could talk to a scholar, get some information, but then have conversation and share ideas so that we could enrich the work and in fact, come up with other ideas and connections. We are recording this presentation and conversation today in case you want to share it with others or those that couldn't make it, and you'd want them to hear about it. And of course, importantly, we do ask for your feedback afterwards. We're going to send you one of those dreaded post evaluation events form, so please take a moment to fill it out. It does help us. And it helps our speakers to know more about the work that they're doing. You want to know about updates? Please, check out our website, and you can sign up to get CEUs for these events. In case this is helpful to you. 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And if you go to the featured page and scroll down to where he is and click on his link, you'll find out more about his work. You'll see a fantastic picture about him. But also importantly, you'll be able to link to his scholarly works through something called Scholar works. And the library does a great job of making publications available easily accessible to you. So you can check it out on our webpage or go directly to the Scholar work site. You can find out more about Dan's work or any other of our scholars and find out what they're doing and have easy access in one place to their journal publications. Next month, we have our May scholar of the month, will be Lindsey Hasket, and her research helps us understand how nurses caring for COVID patients experience secondary trauma and what we might do about that. So please put that on your calendar. May 27. We're generally near the end of the month on a Friday, always at noon. And we always finish early enough for you to be at your 1:00 appointment. So we don't want to waste any more time. It's my pleasure now to welcome to the stage, Professor Dan Johnson, who is a profess associate professor of Geography in the School of Liberal Arts. And today, he's going to talk to us about interactions between social and environmental vulnerability. And so we're looking forward to his presentation. I will stop sharing and let him share his screen and come on board and give us a setup for this conversation. So welcome, Dan. Thank you, Stephen. I appreciate that. I especially appreciate being invited to talk on Earth day especially with the environmental vulnerability piece that my work involves. So as you see here, this looks like maybe a map table we might have used to have in our lab downstairs and in Kavanaugh Hall, where we had maps laid out all over the place and all that sort of thing. So I'm going to be throwing a lot of different images and maps at you, hopefully to help generate some conversation on this topic because it is so it's a very important discussion in today's environment. So it's also, as we frame this is to discuss what actually is vulnerability, and how does that fit in? So this is a model developed by a researcher back in 19 late 90s, so around 98 99, building a triangular look at risk to natural hazards. Where each side of that triangle, one side was represented by vulnerability, one side by exposure, one side by hazard, and risk is sort of in the middle of that. So these three things are necessary to, you know, understanding all of three of these things are necessary in order to understand the risk to a certain hazard. But the vulnerability piece that I specifically work in, and we could break vulnerability down to a very broad you know, incredibly broad topic. But I break it down into a social sorry, social vulnerability component and then an environmental vulnerability component. And so, what is social vulnerability, right? It's a community's inability sort of to respond to an external hazard or you know, something of that nature, that there's something about the community that just, has an inability to doesn't have the resources to respond to it. This is the CDC's definition, so and we could find numerous other definitions. But importantly, a lot of people jump to this conclusion, and I hope the discussion today sort gets into resiliency more. You know, a lot of assumptions are that social vulnerability is the inverse of resiliency or socially vulnerable communities are lack resilience. And sometimes that's not necessarily the case, as we can talk about later. Environmental vulnerability, this is you know, we could probably interact with hazard a little bit. You know, that's a piece of it, but it's kind of where that overlay between the hazard and vulnerability lie. This is where the environment has a tendency to produce a harmful impact on human health. So flood zone, right? To have a tendency to produce hazardous environments, but not all flood plains are flood zones are created equal, right? There's different variations within that flood zone for different levels of a flood, right? The important thing is both social vulnerability and environmental vulnerability are both spatially and often they're temporary variable. So we can map these. We can map this out through time and space and see how social vulnerability is changing over time, and how the environmental vulnerabilities are changing over time. But when these two come together, right when we have a community that is both socially vulnerable and environmentally vulnerable to a particular event, so say, in this context, we introduce high temperatures, extreme temperatures. To a community that is both socially vulnerable and environmentally vulnerable to this hazard. We end up with an extreme heat event and an extreme disaster. This is a map. This is a study I did long ago of Chicago's 1995 heat wave. And this is sort of the heat wave that brought extreme heat into the public consciousness. No, people were dying right and left in Chicago during early July of 1995. And there really wasn't a good understanding of why that was happening. You know, we ended up geocoding about 700 deaths that occurred in homes. So that's sort of I can't remember the exact number that we ended up geocoding, but it was more than the official number that was released, well over 700. And you see some of those points spread out here. A lot of those coincided with higher temperatures associated with surface temperatures of the urban heat island effect in cities. So how do we measure social vulnerability right now? So, say I'm working at a health department in Indianapolis and manager comes in and my boss comes in and says, Dan, I want you to map out the socially vulnerable communities in Indianapolis. I want to know what communities are socially vulnerable. Do it by census tract. Okay? So I find the social vulnerability index that was built in the early 2000 around 2003. Then I find the CDC's social vulnerability index that was built around 2011? At least the modeling, the idea behind it was 2011. Both of these data from the American Community surveys 2015-2018. And throwing on top of that, we have the thrive index, which is stands for tools for health and resilience in vulnerable environments. So how do I determine using all of these different social vulnerability indices, how do I determine which one, which one is the correct one? So that's where some of my research lies. So we look at d, different measures of vulnercial vulnerability. So we see here, the CDC social vulnerability index. You see the Cutters social vulnerability index, right? And you see the differences in the two. Right? You can clearly see You know, the CDC social vulnerability index really highlights this area in the Deep South. You see a lot of urban areas highlighted. With this one, not as many areas, you still see some clustering in the Deep South. You see the urban areas overlaid with this. The reason for the differences is just the different data that are used in them, and the different modeling approach that is used to come up with the index. So if we take these two and do a combination of them, this is just an additive approach to combining them. We can do it in much more sophisticated ways. But we sort of see where areas are tending to be higher in both categories, really focusing, again, we see this area in the deep South that is highly vulnerable urban areas across the country, areas out west. And here's another look at it. So this is another look just sort of seeing where we have high levels. So if you look at the purple here, that's where we have high measures of the CDC's social vulnerability index and high measures from the cutters social vulnerability index. Again, you know, we see these areas in the deep South and our urban areas across the country. So, you know, it's difficult for, you know, I've had health departments ask me, which one do we use? Which is the most appropriate? And you'll have health departments build their own, which adds to some of the confusion as well. So we sort of try to look at it in a composite sort of approach using all of the available data that are used in both indexes are used in all of the indexes we look at and see the spatial and then the temporal variability. I'm not showing any many maps today on the temporal change, but One area that I am starting to really focus much of my current work in is looking at how social vulnerability and measures of segregation overlap. So I'd be happy to talk about this at another time or with any of you that are interested. You see here, this is an older 1960s way of measuring segregation, but it's pretty well established in the literature called the Duncan Segregation Index. Some of you might be familiar with that. And we have our CDC social vulnerability composite. These darker purple areas are where we have high levels of segregation, high degrees of social vulnerability. I'm doing this now. I have a paper I'm getting ready to put out that looks at the top 200 cities across the country. And it's looking at how social vulnerability and segregation are related, but more importantly, it's also looking at the temperature differences from different racial minority groups. Within the country. And we're seeing that our non white populations, in many cases are living in communities that are seven to 12 degrees Celsius hotter than the surrounding temperatures of that city. So I'm kind of getting off track there, but that's a study. I'm getting ready to put out. But getting into environmental vulnerability now, Here's a look at land surface temperatures in Indiana. This is a composite from 2017, and you can clearly see the urban heat island effects across the entire the entire state, clearly see Indianapolis and the development in Fishers, Hamilton County, up there. Anderson, Muncie, down here in New Albany, Evansville. These are all pocketed across the state, highly visible and thermal imagery. Here's a look at just surface urban heat islands from cities, and the red areas, of course, are showing hotter location than the green areas, and we'll look at Indianapolis here. And the red areas are seven to ten degrees hotter, and this is in Celsius than the surrounding countryside. And then it sort of breaks down from there. So if we look at how urban heat, extreme heat event might affect Indianapolis. We can look at how the surface bone effect adds to that. And in fact, there was a study done years ago of what's the most likely natural disaster to strike Indianapolis. And a lot of people think, big tornado, right? We're going to have a large EF five tornado ripped through the city or the earthquake, right, the new Madri earthquake. Most likely is an extreme heat event that will settle in the city for several days, causing not just heightened deaths, but taxing of the you know, the emergency management and health care system. And that was a big concern during the first summer of the pandemic where we thought we would have. In some cases cities did experience both people coming in for COVID 19 and people coming in with extreme heat. So both were a big problem. Again, land surface temperatures around Indianapolis. This is a 2016 look at it, right. This kind of showing how temperature differences are, the different what we call sort of a micro urban heat islands around the city. Sorry I skipped one there. And then just looking at through time. So this is one I'm showing you. This is one of the ones I'm showing you through time here. We've got a model that I developed the extreme heat vulnerability index that looks, we've got Indianapolis from 1990. We've got Indianapolis in 2000, and we've got Indianapolis from 2010. You can kind of see how it's changing through time, right? These temperatures due to development are changing, but also populations are moving. So, interestingly, it became more clustered in 2000 and then became a little more dispersed again in 2010. And hopefully in the next few months, we'll have enough data released from the Census Bureau that we'll be able to create the 2020 version of this. So looking at it from an emergency management standpoint, there's a possibility of what we refer to academically as a convolutional environmental vulnerability, where we can have multiple environmental stressors impacting a community at the same time. And so what I'm showing here is, and this is a little surprising for some people, is the riverine flooding risk, but also earthquake risk. And what sort of pops out to a lot of people is Yeah, this, Indian Haps are, you know, Marion County is in the zone of what we call highest risk, at least comparing across the country to both riverine flooding and earthquakes due to the new Madri fault line. So looking at riverine flood risk and the CDC social vulnerability index, and I've highlighted these red areas to show these are the areas of highest risk, you know, due to extreme flooding from rivers, and the social vulnerability included in it. All right. So we see obviously a lot of places along the Mississippi, would you expect those of seeing those earlier maps of heightened social vulnerability in the Deep South. But Marion County pops out of this, right? Marion County is in it, Madison County with Anderson. So a study right now that I have, it's in minor revisions was looking at building a dynamic, what we call a dynamic Bayesian network, and I'm going to try not to get too technical with this, so I apologize if I get too technical. But this is an artificial intelligence approach to predicting COVID 19 relative risk or community level relative risk with this computational method. So On the left, we have what we'd call a simple basin network. And with this, and I'm just showing it in the context of an extreme heat. We have temperature, we have heat related illness, and we have humidity. Temperature can cause heat related illness. Heightened temperatures can also increase the ability of the atmosphere to hold more water moisture. So we build with these connections, you see, we build conditional probability tables that based on different data that are present in these circles, you see. The circles represent different data sources. So we could say with this. If the temperature in this location reaches 95 degrees and the humidity is 90%, what's our risk for heat related illnesses across space. And that's what is limiting with this simple approach, is it's just giving us sort of a cross sectional view a snapshot in time. But we can make this a dynamic network, and that's what, you know, my approach with this is as we take different time slices and we build this recurrent structure of these different simple networks, but they link to one another. So we have the relationships I just described in one time slice. But then those relationships impact the next one to a degree. That impacts the next one. And even relationships at time slice here can affect things at this time slice. So we have You, in the case of our network, we have literally probably hundreds of different conditional probability tables outlined. Here's the structural overview of working to predict the COVID the COVID relative risk. So included in this, we have some temperature data that's from NASA. It's a NASA model that helps utilizes surface observations as well as remote sensing observations, but included in it also level of precipitation. And we bring in the CDC social vulnerability index, the social vulnerability, the Cutters social vulnerability. This is the FMA National risk index, which I've shown you a little bit of And then another piece to it is resiliency. So this is referred to as the baseline resilience indicators for communities. All those are sort of fed into this. You see one increment here. But all of these are fed in to build predictions through time. They're looking at what actually occurred in May 2021, what actually occurred in June 2021, July 2021, August 21, to predict what took place, what will happen here. All right. And this is kind of just an increment showing how different things feed in to what we're most interested in, which is this, you know, central relative risk. Here's some outputs from this model. And with most of these basi network approaches, we have to build categorical variables. So we have to make our continuous variable of relative risk discrete. So we break that down into a high risk, low, high medium, low medium, high low, and low low. And on the left, so I probably should have switched these around. I have October 2021. This is the what was predicted based on all the data and the network that came in. This is what actually occurred on the right, and we see differences right here. Importantly, the differences are not I have some students, I think, and I know I have some students in here and they'll understand what I'm talking about when I say, these are not spatially clustered. The residuals from this are not spatially clustered, they're random in space. We look at March 2021. We also made a fairly good prediction of the predictions are approaching in the lower 90% of areas to be most likely impacted based on all the previous variables. So we are working toward using this model to you know, look at COVID in the future, but also bringing this into other, you know, extreme heat. So, you know, other health issues and diseases is what, hopefully we'll be working with this model. So one area I wanted to really kind of push our discussion today is in this topic of resiliency because that's what's really coming about now. And I have a colleague, she works in New York City Health Department, and she shared some quotes with me that people have said to her as they go into different communities and work to improve health there. And one of them, like I have here, I'm so sick and tired of hearing that our community is socially vulnerable. What are you going to do about it? You know, they don't want to hear that, you know, these residents don't really want to hear that they're you know that their neighborhood is vulnerable. They already know that or they're tired of that negative connotation with vulnerability. So as I build these social, you know, these vulnerability models, to different diseases or events, the residents, you know, and actually to make these models more useful. You know, I don't want this type of work just to sit on a well, it doesn't sit on a dusty shelf anymore, I guess. I guess it's in a computer somewhere. But you know what I'm saying? I mean, I want it to have some impact, and This resiliency issue, right? The are not issue, but this approach to it from a resiliency standpoint is kind of new to me. You know, I've not really built what I call a resiliency index yet, because it's kind of difficult for me to feel like I can quantify resiliency, you know, the way that we can vulnerability. So, you know, that's kind of all the slides I have, and I hope that's a little bit of food for some discussion here today. And I'd be happy to back up and do any, you know, show any slides that you'd want to see again. Let me get my camera on now. Well, thank you, Dan for setting the stage for what I hope is an interesting conversation. And I'll invite colleagues, our audience here to turn on your cameras so we could see who's here and invite you to raise your hand or to unmute to ask a question or to pose a comment, et cetera. And you heard the invitation from Dan, if there's a particular slide that you'd like to go back to? Yes. Happy to do that as well. I'll be happy to do that. Yeah. We'd be happy to do that. So we'll pause for a moment and see if there's anyone in our community that would like to begin the discussion. If I can chime in, this is Greg Moby in the archives or at the library. Hi, Greg. And, how much in looking at these various communities, how much of it is based on economics? Well, yeah, quite a bit of it is. If you look at the if we look at the Let me back up to a a couple of slides here. If we look at the CDC vulnerability index for Indianapolis here, literally, I mean, literally, one quarter of that, economics accounts for one quarter of that. There's a separate component that is fed into that, that is considered the economic or socioeconomic capacity of the community. So a lot of it is economic, Greg. And interestingly enough if you bring that up, some of the economic pieces of it too, also, can add to the resiliency of that community, right? So um You know, it's a large piece to it. You know, we found some interesting relationships in a recent study we did for COVID 19 across the US. We found some interesting relationships between people that were unemployed and employed, how the unemployed seemed to be getting COVID less than employed, but it was only the case for certain communities within certain economic patterns. So you know, that's a really important piece to it. I have a question for you, Daniel. Yeah. This is Carda Pome from Fairbanks, School of Public Health. I find it fascinating. Very nice presentation. Thank you. Let me tell you that in a former life, I should have been a cartographer because I love maps. Seriously. I love maps. It is one of wow, look at this. And to me, GPS has been kind of a setback because I always enjoy looking at maps and te how I'm going. Anyway, that's true. At a more practical level, And this is something where perhaps you have already stated that I lost perhaps I missed it. Yeah. If you have a vulnerable community because of social characteristics, and if you have a place that is environmentally vulnerable because of higher temperatures and that kind of thing. No, I think that the real crux here is where is the cosity going? Have you been able to find the costal relationship or this is associated only? I will imagine that if you have a place that is primarily made of concrete and there are no green spaces, that area should be hotter. That would be my guess. But is there really a coity and in which direction we will go? Poor people go to a place that is not all that great to leave because they can afford. How does that work? No, that's a really deep and very good question. And it's interesting because our Besian networks, you know, are developed by researchers interested in causality. And so there's some researchers that would tell you, well, if it if it shows an A Besian network that higher temperatures, you know, the higher temperatures are the cause or is it because, you know, if our Basing network showing that, then that's the cause, right? That's causing it. But it's capable as well of tweaking out some of that other pieces like you mentioned. You know, the poverty is originally what caused you know, the people to move to that community that was already at risk. It's possible to tweak those kind of things out. And my thought on it would be and obviously, those are going to be variable? You know, the patterns of that and the weights of cause or weights of impact from each variable is going to be different for every community we might look at? But yeah, I mean, I think we can kind of tweak some of that out with our approach we're using. And that's one of the strengths of it, I feel, that it can find some of that deeper, deeper pieces that cause that cause the place to be vulnerable in the first place. Yeah. Does that help does that answer a little bit? Yes. Yeah, Thank you. Well, I appreciate the questions that are coming up because it's reflective of what we hope happens in these conversations is to think about how is this generated knowledge useful in solving the problems. So you've identified some problems. I love that this slide that you state that says, Oh the confusion because Yeah. There's so much there, but to help us understand how this applies. So I love that kind of question. What other questions are coming up? Reminder invitation, please, turn on your camera so that we can see you out in the audience as we're engaging in this conversation. Other thoughts that you have I agree with the professor from Fairbanks. I think your work is incredible and amazing. And I think the maps they invite questions. My name is Shan Nama. I'm an assistant professor of Dermatology and Internal Medicine at the School of Medicine. And my question is also similar. I think these maps, they're so rich in the visual data that they bring up other possible associations and You know, when I look at these maps, I think not only of social vulnerability. When I looked up the score during your talk, it seemed like it was based on socioeconomic status and age and language. And those are very interesting. But these could also be, as you said, heat maps for the environment. They could also be food deserts. They could also be areas where there isn't much medical access or air pollution. Yeah. And that's why I think these maps are so interested. So I have two questions for you. How do you tease at those relationships? Is that through this iterative process where you say you may tweak some of these models and maybe measure and see which one of these are? Is that like a map based process? Or is that by looking at a statistical process where you can look at the different variables and their weights or the different scores and their weights? Like, I was just kind of curious. Sure. Go into it. What's your process for that? Yeah. Well, it can be both. So we can see both the weights visually and sort of tabularly in statistical form, And it sort of depends on how deep our network is. You know, if it's containing a years worth of data broken down by month, you know, we can tweak out potential relationships back several months. And that would how I would do that. I would first look at that in a tablear form, sort of like a spreadsheet that would give me those weights, which in itself, sort of these conditional probability tables. And you know even from that, what's also really interesting about this, I can do all sorts of what if analysis. So I could say, you know, in July of, you know, well, let's change that. Let's say in April of 2022, and I want to model extreme heat event in July of 2022. Let's say we have one in July of 2022. I can look back through the data and see if there's you know, something in there that might have caused if the data is rich enough that would cause, you know, at least some of that a factor might have been a factor in that extreme heat event in what neighborhood was affected. You know, the Emerald Ashber for instance, right? I mean, when the Emerald Ashbor came through destroying tons of forest cover, right? Within the city, it made a lot of neighborhoods hotter. So because the tree canopy coverage was gone, you know, that's a variable, we could tweak out of that because we look at the forest canopy coverage. Because that's also a variable in the environmental vulnerabilities. But yeah, it's a fun process. It's a lot of fun to do. At least for me. I spent a lot of time sitting in front of of a computer. You know, I tell my students, I said, You have to love to want to sit in front of a computer all day to do this stuff, right? If you can't sit in front of a computer all day, you might want to find a different line of work, right? But that's a great question. I appreciate that one. I have a computer all day, so I don't know if I will So yeah. That's really great. I have also another practical question because I don't study COVID, but I find your measures very interesting to explore structural reasons for health. Practically, are you kind of limited by diseases that there's good temporal data on that you can maybe look at when correlate these changes in, say, the climate patterns with, you know, incidents of disease, like, what kind of limits these analyses? Yeah. Good question. And that's one of the great I mean, I hate to say great things about COVID because when the pandemic, you know, following beginnings of the pandemic, you know, seeing how that was occurring, you know, in late November, even there was mention of it, I think, late November of 2019, that this possibility of a pandemic And once it started, you know, as a geospatial data scientist, I'm like, this is it's a terrible thing, but it's a great thing to study because we're going to have plenty of data for it, right? People are collecting data for it, at least work collecting a lot of data for it every day, you know, addresses of cases. I was able to pull from the Indiana network for patient care, the COVID cases by Census Tract. And those were tabulated daily, but then I aggregated them monthly. So we are limited somewhat in that. I look at that's one of the reasons I'm using these two these two different disease processes, I guess, the COVID 19 and extreme heat because they're both at the end of two different at the end of a spectrum each at each separate ends. With COVID 19, we have all this rich data, right? It was all collected. You know, we've got amazing amounts of that. Down to the extreme heat, where we don't have so much of that, right? I mean, that's a really hard one to kind of nail down until there's a death from even then. It's like, was it extreme heat? Was it caused by something else? Was it the harvesting effect, you know? So yeah, I mean, we are very limited, I believe, and and the temporal quality, you know, the longitudinal quality of the data. And it's one reason I wanted to try to explore more of what's in the Regan stree Regan Streef electronic medical record system, because when I came to I UPI, that was kind of one of the pitches for IUPI to me is, Hey, we've got this great data. We've got this electronic medical record system at the Regan Streef, and you can mine it, and do all this great stuff with it. I was like, Hey, that's great. You know, sounds great. It's exactly what I want to do. But kind of find out it didn't seem to quite be the gold mine that was promised, you know. But there is rich data there. I don't want to take anything away from that, it's more complicated, I feel to get into some of the data other than you know, COVID 19 has been pretty easy to get ahold of. And so, yeah, I feel very limited from that. I see that Amber has her hand raised. So well, let's let her ask a question or comment. Go ahead. Am. Thank you. I'm Amber Osterhol. I'm an assistant scientist at the Indiana Clinical Translational Sciences Institute. I have I think two questions for you. Sure. The first being, is there a threshold for ambient temperature versus extreme heat at which this tends to peter off. I'm thinking places like Las Vegas where the summer heat average is in excess of 105, 110 degrees where an extreme heat event may not even exist really. Yeah. So that's a great question. It's difficult to answer, and there is a threshold. And because we adapt, right? We physically sort of adapt to temperatures. So obviously, the threshold in Phoenix, Arizona for extreme heat is going to be different than the one in Indianapolis, obviously, you know, something that we would consider a heat wave here if we put that in Las Vegas or in Las Vegas or Phoenix, they're going to say, Well, it's hot, it's a little moggy, right? But they're not going to feel the heat they typically do. One thing we get, they say, Well, it's a dry heat. Well, that's true. That's true. It's a dry heat. We get more humid type heat events, obviously. And that threshold that you mentioned is difficult to nail down because it can change from year to year, what that is. There's different ways to measure it looking at say, the past five years of temperatures and saying, well, if we're 2.5, three standard deviations above our our typical high summer summer high temperature, that's a heat event. And usually for it to be declared extreme heat event, it has to happen for several days. But then if a heat wave comes through next time and it's same temperature, it's likely that we won't have as many complications because of just people adapting to it. But one thing that's interesting about this that I like to bring up is the temperature for declaring an extreme heat event is measured at the airport in a white box a meter and a half above the ground. Right. So that's the official temperature for Indianapolis is out there. And so that's the one. But we can have a temperature out there, say have 90. But in some of these areas in the city, there could be 110 already, right? So there's areas of the city where we could call an extreme heat event on a day where it wouldn't be affecting anyone in the suburb. But areas in the city, because of the heat urban heat island, you know, could be, very extreme temperature. And that's something I think health departments are just to me, it feels like health departments are just starting to understand that. Cities are just starting to understand that. I think you had a second question, do you? Yeah, great. Thank you. I hope that answers. One. Thank you for that answer. My second question is maybe kind of a little bit more less related. But thinking about social vulnerability and looking at the definition and things like that. Violence doesn't tend to be included in social vulnerability, and I'm wondering if there's a way of accounting for that in your modeling. Do you say violence? Yes. Like crime. Yeah. Well, not necessarily just crime, but other types of violence as well as far as more along the structural side of things. Could you describe that a little bit? L violence of structure? Sure. So structural violence, at least as far as the traditional definition would be things that are really kind of built within our social networks. Okay. For our society that are really designed to keep people poor, sick, and under control. Yeah, Yeah. So there's two sides to that. You know, there is no I have not yet to see a social vulnerability index include either of those. And I've wondered for a while why you know, it's something I wanted to look at and hopefully I can get one of my students someday to do so is looking at crime in particular, and we could even pull out violent crime from that and how that relates to areas of social vulnerability. And would it help? Would it improve the models to include that as a variable? I think a lot of this and that kind of feeds into my research a bit on the segregation, and how it's related. And there is no measure of segregation included either in any of these vulnerability dices. In fact, there doesn't seem to be as strong of a relationship between it as you might expect, which leads me to wonder if that needs to be included, some sort of measure of segregation that would that would show some of you know, some of the systematic racist policies that have caused that. And you can look at, like, we could pull up some maps, and I probably should have done this, you know, the historic red lining in Indianapolis, and how Most a lot of these vulnerable communities are still, you know, they're still in that area, right? They're still in those locations across the country, and there's a few studies out on that right now of not so much how social vulnerability is within those red lined areas, but just how, you know, the economic thing, the socioeconomic, the poverty, how that's so well embedded in those historic areas. So I've yet to see an index include those. I think it if that's what you're thinking, I think it would improve the models to some degree. Because obviously, they're not perfect. We'll see we pull up social vulnerability and some disease, and you think, Oh, it's going to be so well related to that, and it ends up not really being the case or it's at least not as straightforward as we think. Yeah, so that's a great question, Amber. I hope I answered that. You did. Thank you so much. Yeah, thank you. Well, I want to thank our community for asking really good questions, and I would propose, Dan that you change this slide here instead of saying the confusion opportunity. Well I'm hearing all kinds of ideas and questions about how do we use these. How do we across the different disciplines and areas of work? How could we build on this idea that you have? I hope that's one thing you're taking away, and I hope the rest. Yes, about sure. How do we contact an and explore this even further. That's one of the beauties of these kinds of conversations is finding out where the connections are and the opportunities. And so I think my sense of the conversation today is that this is incredible work. Has some great opportunity for helping us to better understand some of the complex challenges that we face as a community and how we could work together to fix them. So thank you for doing that today. And I think it's a good topic for Earth day, too, right? I E human impact on the environmental impact on humanity. So right. So we have a lot to think about today on Earth Day. And as we wrap up today, we do want to remind everybody to respond to the opportunity to give us some feedback about this event, to share this with your colleagues and friends, and to remember that there is a recording of this, and you'll be able to share this with others. We are on Facebook live, so it's another way that we try to reach everybody. So do follow us on various social media networks. And be sure to come next month in May, as we continue this opportunity to have these monthly conversations. So we'll formally end this opportunity now because we know that people need a chance to get themselves refreshed before they go to their next appointments at 1:00 or wherever you are at the hour. But we'll stay on for a few moments for that traditional after the presentation if somebody wants to say something to Dan or ask another question. We'll be here for the next few minutes to be able to do that. So we'll say goodbye to our Facebook live audience. Well thank you all for joining us today. And thank you, Dan, for giving us a lot to think about. Thank you.