Hi, Kumain Nabakayo. I'm Jessalyn. Let's dive into what's new in DevTools in Chrome 94. Great news! You can now use DevTools in your preferred language. Open Settings. Select your preferred language in the drop-down. Chrome DevTools now supports more than 80 languages. Reload DevTools and there you are. You can help us improve the translation. If you find any incorrect translation, please report via google.gov. DevTools.translate. Next, you can now simulate the dimensions of Nest Hub and Nest Hub Max in the Device Mode. Toggle the Device Mode. Select Nest Hub under the Device List. To view the Device Frame, select Show Device Frame under the More Options menu. Next, you can now get information about a site's Origin Trials in the Frame Details View. Origin Trials are a way for you to test a new or experimental web platform feature and give feedback to the web standards community before it is made available to all users. Go to this link to learn how to register for an Origin Trial. Open a page with Origin Trials. In the Application panel, select the top frame. Scroll to the Origin Trials section. Expand an Origin Trial to view the token status, expiry time, and more. Next, another new debugging feature for the CSS Container Queries. A new Container Bash is now added next to the Container Elements. Not sure what are the CSS Container Queries? Check out my last video to get a quick overview on it. In this example, I have an article element with CSS class white box. There is a paragraph in it. In the CSS, we define some alias container rules to show or hide the paragraph depending on the container size. The article element is the container here. Therefore, a container Bash is displayed next to it. Click on the Bash to toggle the display of an overlay of the chosen container and all its querying descendants. Next, use the new Invert Checkbox to invert the filters in the Network panel. For example, you can type status code 404 to view the network request with status 404. Enable the Invert Checkbox to view all the network requests which are not with status 404. Here is a bonus tip. Use the ruler while inspecting your element. Open settings under the Elements section and enable the Show Rulers checkbox. Close the settings and inspect an element now. The rulers and the extended lines help you better measure the width and height of an element. Alright, there are more new features in Chrome DevTools 94. As usual, go to the video description for the link to my blog post and other information. Thanks for watching. See you in 4 weeks for Chrome 95. Ciao!


The economy is such right now that you can make an all right living off of that. If you're single, you have no kids and you work extremely hard for five years to get to a point where that Spotify checks paying your bills. I would say it's making it harder on yourself to do so and you're incredibly unlikely to do so. And there's always the risk that Spotify craters tomorrow because all the companies like Spotify in this economic environment are cratering. So the Spotify check paying bills is is nice, right? However, it's paying your bills and then you want to continue to grow like how do you maintain these numbers, right? There's constant effort that has to be put out and I think that's where the struggle goes.


Thank you, Arpit. Good morning, everyone. I was thinking, why don't we play Jeopardy or the Family Feud? I mean, I tell you what, Heather really set the bar for moderators. So, Heather, wherever you are out there. Give all of our answers and questions. That's right. And I'm going to limit you to 60 seconds. What is the edge? Come with us. Well, great. Well, let's get into it. So, yeah, what is the edge? So, it's the Wild West out there, right?  And Jason, we've spoken about this, and you definitely have some opinion there. Let's start with you and get your insight. Yeah, it's fine. So, when we draw edge-to-cloud architectures, we always put it in portrait mode, because it's easier to fit in a PowerPoint. And literally, the edge is on the left all the time. It's the Wild West. The West side and the East side is kind of usually the cloud and all that, more civilized, and they're longer, centralized data centers. So, a key point, there's no single edge. And this is part of the confusion. I mean, it depends on kind of who you are, how you define it. The way I define edge computing, it's moving compute as close as both necessary and feasible to subscribers or devices. If I'm a telco, I'm going to move compute as close as I need to, to reduce latency for subscribers, reduce bandwidth congestion on my own networks, so that could be my cloud edge, or maybe I push it into CPE equipment on-premise, because I have to do that. So, that's the other thing, is they're kind of associated with locations, but that's even that's an organic boundary. If I'm an operations person, I'm necessarily going to move compute on-prem. If I'm a nuclear plant, I'm not going to do the cloud. Right. And stuff like that. So, this spectrum of edges. A lot of the fragmentation is due to inherent complexities. As you go from cloud to device edge, software and hardware always gets more customized and complex. So, you can kind of envision this curve where like the cloud data centers, IT standardization has happened over the years. It's relatively standard, I'm not to say it's trivialized, but as you get further towards the device edge, the wild west, hardware starts to get more complex first. You know, I need elevated temperature support, and I need regularization, then I need all kinds of IO, then I need class one div two for explosion proof, then I need custom hardware for every single thing on the planet, like in terms of connected products. You know, car is a little bit different than a toaster. And software goes a little bit slightly. Software goes flat a little bit longer, you get into everyone wants a different flavor of OS, and then you get into all the crazy protocols, and then there's a point where you hit this trade-off at memory constraint, and this is what we call the thin compute edge, where you can no longer do virtualization or containerization. You want to extend cloud native principles as close from the cloud all the way down to that last point where you now have to go embedded because of memory. And so we'll talk more about this as we go, but there's these inherent trade-offs. Part of the reason why there's so much fragmentation historically in the edge is because the models for a long time was I'm going to lock you in with my proprietary protocol. I would argue that IoT is more about, IoT and Edge is more about the maker of movement than anything, the maker of movement, Kickstarter, Indiegogo, just Raspberry Pi, Arduino, whatever, that ecosystem has driven people to change. I can no longer lock you in because someone's going to out-innovate you faster. So now it's about how fast can you innovate, and you win by merit, not lock-in.  Everyone thinks that if I can just lock you in, then I can sell your data if you let me. The reality is, and this is why we're all collaborating in All of Edge and broader communities, is open always wins in the end for scale, always. And if you want to really scale, you need a multi-cloud strategy that starts with an open edge. And so this is all the stuff that we're working on, and I mean, there's so many different things we could talk about, but you know. Well, you hit the nail on the head, right? Proprietary, and that's the power of open source, is really opening up. Melissa, let's get your insight there as well. Well, I think part of what has been also really tremendous in the shift that's happening, you know, Jason kind of talked about what's the movements that are moving up the stack. I think we've also seen the cloud technologies and the infrastructure software that's been well-established and is becoming, you know, more and more productized and implemented in common ways. And I think Dan will probably talk a little bit about this and his keynote as well, are coming down the stack. But then when you complement that with the diversity of workloads coming up the stack, you know, for all of these different use cases, and then you introduce AI and FPGA and GPU, the complexity of what workload needs to go where, physically where, and how much can I put at that workload? You know, and then how do I load balance and make sure that I've got the right resources, physical resources with regard to, you know, AI, neural networks, GPU, et cetera, where it becomes really, really complex. And I think that's one of the things that I hear from our partners a lot is help me figure this out. I get why Open's gonna win. But holy heck, it's really complicated. And how does Open do that? How does Open Source make it easier to manage that complexity? Well, I think part of what you see in the dialogue at sessions like this at ONS and other Open Source gatherings is a conversation around what is the architecture. And by being open, you get the best of the community's mind and you are able to leverage the best of the intelligence of where technology is headed. And you're also able to leverage a pool of resources. It's not just me doing my development on my own or anything. And so we can actually, if we can agree, I think it takes a little longer to get to common consensus on what that should be. But once we get to a common consensus, we can go much faster. And then you also have the shared maintenance burden because everybody's not only contributing to the creation but contributing to the maintenance of it as it moves forward. Excellent, excellent. Eric, your thoughts? Yes, I'm falling onto what Melissa said. I mean, I think that the horizontal nature breaking up the vertical integration so that you reduce friction and making it be easier to go deploy things, I think that that's one of the things that we bring to the table. This is what's been done in the data center is what the whole sort of how the open source stacks have evolved over time. And the same thing needs to happen on the edge and it's going to happen on the edge. We just try and accelerate it. But in terms of what is the edge? Well, it seems like if you read in the trade press it's anything that isn't in the cloud. OK, that's not necessarily a very useful definition. What I'm trying to do is tease apart what are the edge unique requirements and they're different for different parts of the edge or different edges in the system. So one thing that to me is interesting is this very sort of deep edge or enterprise edge. In many cases, it's things that are deployed sort of 1C2Cs, a server sitting in the ceiling over there managing the AC or whatever, right? Something that does some video analytics, vibration analytics, next to a generator or something. And how do you actually manage that? It's very different than what people have done with your laptop and even with your phone because the assumption is that you don't want to send a human being out there to update the software because it's too expensive. Because this thing is you need a ladder to whatever, a crane to get up there. And some more else, you need to drive for a couple of hours to get there. So what can you actually do from a software perspective to go address those unique things, whether it's about physical security, about the access to the things? And as Jason said, the different IO requirements you have at that edge, right? Dealing with legacy, serial ports, random radio technologies. So that's a very diverse thing, but how can you make that stuff be more accessible so that it's easier to deploy applications out there? And what's exciting about, from my perspective, the LF Edge Initiative is unifying these domains and reducing that complexity and that fragmentation when you look at telco, IoT, enterprise, and cloud. And there may be some disagreement here on the panel, but as an analyst, when I sort of look at those domains, I think telco has one of the biggest challenges when you look at latency, location, and mobility issues there. So, Eric, let's start with you. Are there any best practices or learnings that the telco domain can take from those other domains? Well, I mean, I think that sort of building on the sort of horizontal layers. I mean, one thing working up from the hardware is looking at what is actually common in terms of getting hardware root of trust, being able to have some notions of measured boot in the space where you can actually take and boot something, typically Linux, but maybe it's a real-time OS or something else that's going to run out there, and then build up from there by providing common connectivity for your applications. The applications, cloud-native applications, they assume that, oh, what I get looks like an ethernet. Well, but you're running a RLT, right? Because you're sitting out there. So does the application need to know about that? Well, it shouldn't have to, right? You should be able to abstract that thing away. So that notion of inserting a layer of virtualization is something that we're working on in Project Eve and as part of LF Edge, so. Jason, your insights into the different projects that are also driving that momentum? Well, I'll start on the telco side. I mean, so just like many folks, like many companies over the past couple of years, IoT Edge, whatever, like, everyone's trying to lock everybody in again, because it's like, if I can just lock you in, then I can make money on your data, if you let me. Yeah, historically, that's that's not true. That's historically, but the reality is it's just like trying to own the internet. So what anyone, telcos or otherwise, we look at it as you must open it up and then there's use technology to bring checks back from strangers. This is how it's also making a scale. So there's a lot of stuff that we're working on. You'll hear about that, this notion of inserting trust at a system level so that you can cross between systems of systems. So step one, though, is like solve the insanity around everybody reinventing the middle. Yeah. You know, with IoT specifically. So one of the first projects, well, a project that I helped to get started with a great team at Dell and it became a Linux Foundation project with, you know, EdgeX Foundry, and one of the anchor projects along with the Crano and there's a bunch of others, Eve and others coming in. We'll talk about EdgeX was, hey, let's go extend cloud native principles, you know, down to the thin compute edge, so to speak, you know, gateways is one way to look at it. Let's do it in a way where you then open up an API in the middle and if you get enough folks using something through collaboration, then that becomes a de facto standard. And so if you have this open API that allows you to replace components, whether it's device connectors, and I don't care what protocol you speak because you'll never have one protocol, southbound especially, there's thousands of protocols southbound in IoT. Why? Because of the lock in place. Everyone created protocol so that it was really hard to switch from my control system. Well, now everyone's moving towards, it's about software and services, not about the lock in, you need to democratize the South so you can monetize the North. Right. And so EdgeX. That agility, yeah.  You take your pick a protocol, you plug it into the open API and then everything can be heterogeneous around it. And so that's what EdgeX was all about and is all about. And so we're seeing a lot of pickup in that community and there's... So good stuff happening there. Give me that decoupling point, the moment data is created and then I can send data wherever I want. The cloud strategy would normally be, my multi-cloud strategy of, like I'm a big cloud is like, send me all of your data and then you can send it anywhere you'd like with if you pay me a lot of money. The EdgeX strategy is decouple your data, the moment it's created and then you pick any permutation from edge to cloud to all permutations work. And then you can kind of transport stuff left and right across the chain. The other one I've mentioned, so we've been talking about earlier this morning, like the glossary project, very important. You mentioned the terminology, there's a lot of buzzword bingo. Yeah, like fog and foggy. The fog is basically all the edge. Well, fog is foggy, that's why we didn't really subscribe to that term, but like fog is basically... I can't see. The fog is everything that's not in the cloud, same thing, all of the edges plus the networks in between or whatever. But when I... So it was really important, there's a project as computing glossary is like basically how do we get aligned on terminology as an industry? It's semantics to some degree, but here's some examples.  But what's near and what's far? These are loaded terms, it's like ever seen Sesame Street, Oscar the Grouch, they're far. Like what do you mean? It's better to talk in absolutes. People say real time, way too much. Real time to a building automation person is 15 minutes. Real time to an airbag is like a fraction of a second and it must be deterministic. Real time to a financial person is milliseconds, but no one dies if you don't get your start. In a 5G world, it's less than 5 millisecond latency. So we need to talk about absolutes, tiny. I hear all the time data center people say tiny, it's tiny, it's 500 megabytes of footprint. The world that Eric lives in, it's five kilobytes, some cases. So we have to get aligned on absolutes that are descriptive around inherent trade-offs. And that's where I think, there's some general terminology and then there's like let's not use loaded terms, let's use absolutes. And it matters because I think like every conversation I'm in, we spend the first 15 minutes of the conversation just getting grounded on what we're talking about. Potato potato. You're like, get over this, right? I think another thing that's another lesson learned that is an area where LF edge is investing and it's really important is, if we think back to what happened with the cloud transformation and telcos and cloud operators and tier twos, they all implemented common open, many of them implemented common open source projects for their cloud infrastructure. But because they were standing them up as things were being developed, each one of them did it slightly differently. Open stack running, Kubernetes running, Open stack, all of them container, cloud native. And so now all of those different companies are spending enormous resources maintaining what is essentially is open source that became proprietary because of the way that they integrated, it was unique to their particular business. And so taking the lesson learned from that and really being focused on, hey, if the vision of edge is to be realized while I'm still working, we need to stop kind of the fractured nature by which these open source projects get consumed. And we need to work as a community, not only in their building of these components, but in the way they get integrated to realize actual use cases. And so there's a project as part of LF edge called a crano where that's exactly what the project is about. It's the community coming together with specific use cases in mind and saying, okay, for this specific use case, I would like to architect this with these components, these assets and integrated in a completely declarative fashion, this hardware, this software, this release of this software, et cetera. This is how you integrate it. This is how you render it. This is how we validated it. A lot of the open source projects don't actually validate the functionality that they attest to. They'll have a feature and they release it, but how is it validated and what kind of test cases, what kind of bandwidth constraints, et cetera. And so a crano is really trying to say, hey, let's simplify this. Nobody's gonna differentiate on infrastructure software, right? It's all gonna be on the app services, business models running on top. How do we accelerate the infrastructure to make this possible and do so in a way that the whole community benefits for now and for the long term? And I think that's really, really compelling. It is a different type of open source project in the sense that it's not as much of a development project. There are some development aspects, but it's really folks coming together to do the dirty work of making this stuff productizable. And I think that that's really a tremendous effort. No, I'm not an engineer, but I like to play one on TV. Shouldn't that be the model for all open source projects to drive adoption and drive scale? I think it should be. And I think we're kind of at the cusp of seeing some of this. I think OpenStack's done some of this. The Cloud Native Compute Foundation's doing some of this. But what I think is differentiated about a crano is it's actually pulling from all the upstream projects to render these use cases as complete vertical stacks and reference stacks for the community. And so I think we're in a pioneering kind of step forward when the context of open source. And we'll see if it works. There are definitely skeptics. Awesome, awesome. Well, we've got some time left, so I want to throw a bonus question to the team here. Our pit stood up and dropped the mic when ONS got kicked off and talked about how the impact of the edge potentially could be a forex that of what we've seen with Cloud. Cloud's been pretty accepted. So for me, that was pretty mind blowing. So I'd love to get each of your insight on how you see that happening.  That'll really drive that. So Melissa, maybe start with you. So I apologize. I wasn't listening to your question. I was thinking about the fact that I failed to mention a couple things when I was talking about it. I got totally passionate about a crano. And I failed to mention the fact that there are a couple other projects as part of LF Edge, including the ones that our pit mentioned yesterday. When you said that, I was like, oh, shoot. Go for it. Didn't say anything about those. Anyway, so there are some new projects that were part of LF Edge that were announced. Badal and Fledge were also, there's seven projects total. As part of LF Edge, you've heard a couple mentioned here. There's also a laundry list of new projects that are being contributed to LF Edge. So there's a lot of opportunity for folks to get engaged. And I think the reason that there's so much interest from the ecosystem and from the participants in LF Edge and these different new projects is the diversity of use cases. So while there is infrastructure software that are common components that can be leveraged, some of the challenges that exist when the context of edge technologies are unique and they're different and they require different software and different capabilities and components. And so I'd encourage you to check out some of the new projects that were announced. There's a ton of documentation on the LF Edge website, the wiki.lfedge.org. So it's very easy to get informed in true open source fashion, everything's open. So please leverage what's available to get more informed about these different projects. Thanks. And then the key, of course, is within LF Edge, all Linux Foundation projects is the governance. I mean, that vendor neutral governance, the whole point of an umbrella project as probably many people know, cloud native compute, LF networking, there's a bunch of different, the model's been proven time and time again. It's bring it, be inclusive, bring projects in even if there's some overlap up front but then let the community, working with that structured governance, the technical advisory committee and all that help to harmonize these projects over time. That's a key part of that mission is that nobody wins, as Melissa said so well, kind of reinventing the middle. Someone told me once, open source is all about minimizing undifferentiated heavy lifting. Okay. This is important. I've been thinking about that one for a while. Yeah, but to answer your question on the cloud. So there's a lot of stuff. All data is created at the edge and there's no doubt that there's just more and more devices popping up on networks and this is going to cause a lot of congestion and a lot of need to kind of shift compute. You know, is the cloud going away? No, there's a lot of click bait. Oh, the cloud is going to disappear. No. Will the cloud always do the deepest of deep learning? Yeah. Are you going to see more learning happening at the edges? I call that shallow learning. You know, closer to the edges? Yes. You're going to see, the point is that we don't know where things are going to run in the end. We haven't even scratched the surface yet on the business models and the use cases and get out of just like, POC, party of one, now try to scale that solution. Now try to do that solution that intersects with other domains. That's the real potential and this is why you need to open. You can't cross public and private and all these different folks without trust at a system-wide level. So that's the big next conversation. The point is if you architect properly, even if your right answer day one is to send all your data to some cloud and do stuff centralized, great, but you're not going to want to stay there because you're going to get the bill. It's going to be a hybrid model. You're going to want a hybrid model and so over time, you need to be able to have that, you know, transportability of workloads anywhere along that continuum. And that means that you right now have to architect properly and this is why things like an crano and stuff with Eve and we're doing, I mean, the home edge is another project within LF edge of what's, even in the home, you're going to start seeing folks, you know, your gateway looks more like a server, hosting all the different services, you know, and all of these trends, all these abstract, everything you can, abstract, you know, compute storage, networking, always separate the data from the underlying infrastructure. In the end, it's about consistent infrastructure with domain knowledge applied. The winners will have the best algorithm, have the best specialty hardware, you know, specialty software. No one wins reinventing the middle. Right. So the short answer is like, you're going to see a massive, you know, change across the board.  Right, yeah. Yeah, latency, you know, very low, but number one, I don't care how many nines you have on reliability, I'm not going to let you to deploy my ear bag from the cloud. Yeah. Right. Not going to happen. And number two, bandwidth always comes with a cost. Right. So the analogy I use is if you build freeways, people think, oh, solve the traffic problem by building freeways. Well, what happens? More people move to town, suburbs, and then you have more traffic. Yeah. Everything raises up, but new experiences is going to be incredible, like all this cool stuff, but it's not going to solve the bandwidth problem. Right. And economics drive it as well, you know, license versus unlicensed. Spectrum wouldn't go all day. Eric, why don't you close us out, you know, any additional thoughts here? Yes, I mean, if you look at where the edge is at, I mean, yes, all the people that are analyzing this stuff are saying that the amount of data that's generated, the edge is growing, and it's going to grow, and it's going to take over the amount of data that actually makes it to the cloud, just in terms of amount of data. If you look at actually deployed infrastructure, you can even argue that the edge is already here and already bigger than the cloud. It's just not connected yet, right? If you walk into a factory floor, you will find industrial PCs sitting in every machine on the floor, right? They might be running some app, running Windows or whatever, and hopefully an old version of it, but hopefully not connected to the network because it probably hasn't been passed in a while, right? Since they installed the machine. Windows on fire. But people want to get that data out, right? And how can they get the data out? Well, maybe they deploy a separate, you know, industrial PC next to it that runs something more modern, software-wise, not Windows 98 or whatever, and then build it up. But I think the opportunity is there, and being able to gather that data, do a bunch of the computing locally, and then saying, okay, what do I need to export from here to the cloud? How do I build the system? And making that easy to leverage from the perspective of people developing applications as well as the people that want to deploy the network services that can actually connect that stuff together. So I think that that's the key thing. Excellent, excellent. Well, Eric, Jason, Melissa, thank you so much. Great insights. Enjoy the rest of the event. Yep, thank you. Thank you. Thank you. All right. Look at that. Thank you.


Former Nigerian head of state Yakubo Goan has urged Nigerians to be patient with President Bola Tinnabu and give him time to resolve various issues in the country. Speaking to state house correspondents after the meeting with Tinnabu in Ahrarok on Wednesday, Goan said it is too early for the president to achieve absolute results after eight months. Goan, who said he told Tinnabu not to worry about criticisms, noted that the government is doing its best to resolve various problems in the country. The government is trying their best to deal with the various problems of the country, but with Nigerians, don't you worry, you can get criticized. I think people out there know better than what you know there, but I think all that one can say is that we've got to Nigerians have to give the president the time to get things really done, and it is too early to sort of say yes, that absolute result, perfect result would be achieved now. So that is my opinion. At least I know, I think if I remember, I was told that, I mean, I was fighting the war, I was too slow, and that, you know, probably we, Nigerians cannot make it, and that we should not seek for discussion, you know, and, well, did we do it or not, they probably do not know the problem in there on the ground.


We're delighted that our speaker today is Kate Cagney, an Associate Professor of Health Services Research in the Department of Health Studies. Kate's research interests include neighborhood effects on health, race, and ethnic differences in access to healthcare, in long-term care, and particularly the relationship between family structures and long-term care arrangements. Kate has an MPP, a Master's in Public Policy from the University of Chicago and a PhD from Johns Hopkins. She's also the Director of the Population Research Center, an interdisciplinary research center here at the university designed to facilitate population research. I had the occasion to review the Population Research Center before coming to do this introduction, and I can only say that it reads like a who's who of the University of Chicago faculty. Anybody who is not on that faculty list is surely missing something, and I congratulated Kate on that. Some of Kate's recent papers deal with neighborhood-level cohesion and disorder, the measurement and validation in two older adult urban populations. An earlier paper, a joint effort by a number of scholars, was a vision for progress in community health partnerships. Kate tells me that our speaker next week, Rob Samson from Harvard, who of course was here when Kate arrived as a postdoc, sort of created a kind of theoretical framework from which Kate's research departed to lead to her current work. But there is a relationship between Kate's work and what Rob will be talking about next week. So it's a delight to welcome you to the Health Disparity Seminar. Thank you. Thank you, Mark. I'm very happy to be a part of the seminar series, and so I wanted to thank Mark for, including me. I also want to thank all of you for being here today. Because I've been sort of looking around the room, I keep wanting to gravitate toward people and have a conversation, so it feels like a room full of friends. Which I think is a good way for me to say that since we're all pretty comfortable with one another, and I feel I don't need to say this in this audience, but please ask questions at whatever point you choose. I feel like if I say that, then it seems, right, that that's an invitation to all of you to begin this. It's really nice for me because this work is ongoing, and although I've been doing work in neighborhood-based research for probably the last seven to eight years, this is new research. We're looking at change over time. We're looking at biological outcomes. Well, in this case, obesity for the illustration today. And I've also taken us on the road. I've spent those last years examining the city of Chicago, and now we're going to Dallas. So I'm going to show you maps of Dallas. We're going to talk a little bit about how Dallas might differ from Chicago and whether some of these neighborhood social processes are things that are generalizable across urban context. I also want to share with you one observation by my friend and colleague, Mario Small, is that much of the research that we discuss in urban sociological world, seem to such as ASA, and here at the University of Chicago, is so dependent on research that's been done in the city of Chicago. And where I feel a lot of pride in that and pride in what's really arisen from research in the Chicago School of Sociology, I think Mario's point is well taken that we might want to do a little more research in some other urban contexts and at various levels of aggregation. So with that, I will start by saying our paper is on the neighborhood context of health disparities. This is joint work with Chris Browning at Ohio State. It's been funded in part by the OSU Initiative in Population Research. And the other thing I want to say since I did foreshadow that we're going to Dallas today is that my colleague, Leanne Carina, who has spent most of her research time thinking about human genetics and had been working with the Dallas Heart Study, that's the data that we use today, came in my office one day and she was holding the questionnaire from the Dallas Heart Study and this was developed by cardiologists in Houston and she said, oh, your neighborhood questions are in this questionnaire. You know, they're interested in research on neighborhoods and I thought, okay, sure, you know, I hadn't really talked with Leanne very much about neighborhoods. I think I presented a couple of times in the department and I thought, oh, they probably have some question on whether people are satisfied with their neighborhoods. If there's ever a general question in large scale service that usually asks people, are you happy where you live, what's the quality of your neighborhood, that kind of thing. But, of course, Leanne was right and that subset of questions that exist in the Dallas Heart Study are exactly what came from the project on human development in Chicago neighborhoods which Rob Sampson felt in Urals and Steve Rodinbush developed in the early 90s. So it was really amazing for us to have those neighborhood data in a survey, in a social survey and also in a large scale research project that included biological indicators. So this is really the first cut on this. We've also looked at blood pressure. We're starting to look at CRP but sort of in the spirit of the NSHAP study which Linda directs, we're really trying to link the biological with the social and so this is a nice opportunity for us. And the third thing I'll say about these data and our pursuit here is that we were also able to get crime data on Dallas. And for any of you who have done any work with crime data, it's surprising that it's hard data to get. It's hard data to get at a particular level of specificity. And so colleagues of Chris's at Ohio State, Lori Crevo, Ruth Peterson, have put together crime data for 60 US cities, one of which is Dallas. So we were able to triangulate these data sources in such a way that we're able to look at a change in a neighborhood outcome. What we're going to talk about is crime and I'll motivate that in a moment. And a change in our example today, obesity. So for those of us in neighborhood based research as I'll talk about now, that speaks to one of our key challenges. Which is this idea that, how do we get at this notion of causality and selection? And as you can imagine, we start talking about neighborhood context. Many people have said to me, many of whom are economists will say, it's selection. That people of particular characteristics move into particular neighborhoods. And so what we're observing, what we think is an aggregate or a meso level principle that's emerging in a community is really more about those people who compose the community rather than anything that the community has impacted upon them. So that's one story, one challenge we've had here. We've had issues of measurement, how best you get at neighborhood level concepts. We've relied a lot on census data, which again aggregate information in communities on income status and on things like residential stability, ethnic heterogeneity. But we don't really have so much on some of these measures like trust and solidarity, some of these things we imagined would emerge. We're also not so sure how to test some of them. Steve Rauner-Busch really led the way in thinking about how we might consider psychometric properties as ecometric properties. And he's a very nice paper on that to help us think about using some of those same statistical principles to measure meso level properties. I've mentioned data limitations, but also I think really thinking, and probably most importantly, this idea of mechanism. So what is it that's linking neighborhood context to health? What is the bridge between structural characteristics, which I'll describe in a moment, and a particular health outcome? And so this is where we're going to emphasize these ideas of social process. So because our task today is really to think about how neighborhood context could inform research on health disparities, we have to think about what are the challenges that rest in the health disparities literature. And I would say that much of the research on health disparities focuses on individual level sorts of experiences. We think about the relationship between, for instance, a doctor and a patient. We think about discrimination that might happen in the dyad or people's perceptions that might inform the way a physician makes treatment choices. So that's one story. I think we also focus a lot on demographic based disparities. So we'll think about things like gender, like race, like age, and the extent to which they may shape people's opportunities. But I think one important principle to take away, I hope, from this conversation today is that disparities may exist not just at the individual level, but at the neighborhood level. And I think for those of you who have read William Julius Wilson's work, who is also one of the key people who really helped us think about how social disorganization theory could inform contemporary analyses of urban context in one illustration was helping us think about individual characteristics like social isolation and how it matters that a socially isolated individual is in a socially isolated community. So this idea of characteristics that are nested and the extent to which it may not be, for instance, about an individual descriptor such as race, but it may be about the racial composition of a community. It's really trying to extend some of these ideas about disparities that we have generally looked at at the individual level and think about how we might aggregate those up and consider disparities related research at the neighborhood level. So if you're all with me thus far, we're going to spend a few moments talking about the model. So as I said, this is the general model that Chris and colleagues and I have developed as an extension of collective efficacy theory. And this really comes originally from social disorganization theory in the 30s and 40s, Shawn, McKay, Burgess, and others here at the University of Chicago, where they theorized that structural features of communities like economic disadvantage, the absence of affluence, residential instability, ethnic heterogeneity led to a particular kind of outcome related to crime. So they were interested in how these things might be criminogenic. What we've tried to do in this context is take this same sort of model and apply it to a health outcome. So I'm just going to show you three quick research findings and how it led to the paper we're doing today. And then I'm going to roll into that analysis. So on the structural side of the model, so if we think about three components, structural process outcome, we can think how the absence of affluence led to understanding the difference in self-rated health between older blacks and whites. We found that in Chicago-based research. We then, moving around the model, we've had some interest in trying to understand, again, another structural feature, ethnic heterogeneity, and that resonance and ethnic enclave reduce the level of asthma prevalence. We then move around this model to think about collective efficacy. So I'm going to define this more fully in a moment, but well, how many of you have heard of the term collective efficacy and not just through me? So a little, yeah, it's closely related to the concept of social capital. But the way I think about collective efficacy and the way it distinguishes itself from social capital is it's really about the ability of the community to come together for the common good. So it's more sort of action oriented in some sense. It rests on these two components of social cohesion and informal social control. In today's analysis, we're going to focus on social cohesion. But an important way to think about the idea of collective efficacy is it's this notion that you feel like everybody has your back in your community. And it's not that your friends with your neighbors, but you trust them. And if your house were burning down, you'd feel like you could go wrap on the door and somebody would provide help, provide assistance. It's that feeling and normative orientation at level of trust that makes you feel some comfort in the community. That's what we focus on. So that's one result related to that social process piece that's linking the structural outcome or the structural piece to the health outcome. We then have done some work on the 1995 Chicago heat wave, so we're still in Chicago. And we found that neighborhood commercial decline was associated with excess mortality during the 1995 heat wave. So this is where we're starting to tease out what might be this mechanism. What are the stressors, if you will, that relate these structural and social process features to these outcomes? Because of this work, I think in the heat wave, we're really interested in this idea of drawing out what is disorder apart from commercial decline. So in the heat wave analysis, we're really interested in trying to characterize communities in a way that, I guess in some sense, would make them more or less inviting. So it's not just that older adults couldn't get outside. There might not have been anything appealing to them in that outdoor space, or any place where they could seek relief. Might not have been any place to buy a fan, or to go indoors to cool off. So it's not just that people might be afraid, but that there wasn't anything drawing them outside. That said, we're really interested, too, in trying to understand why, for instance, might fear matter. And what is it that might be closely related to fear in a community context? And so that led us to want to understand, I think, in a more detailed way, the impact that crime might have in communities. And again, sort of thinking about that as a social stressor. So we're emphasizing, as I said, that piece. But one of the other things we wanted to do, too, we want to draw out that mechanism. We also want to understand health in a more detailed way. And I think, and it'd be interesting for some of you to comment upon this in the room, one of the things that is somewhat of a frustration, one of the things I like a lot about the NSHAP study, is that it can be hard to find appropriate or more detailed health measures in a social survey that also has really rich information on networks, on community life, on lots of other aspects of social engagement. So to be able to put these two together is quite beneficial to us. But it also made us realize that we had to turn to other theories, not just to contemporary elaborations of social disorganization theory, as I mentioned, Rob's work and William Julius Wilson's work. But we also turned to the work of our colleague, Tressa Seaman and Eileen Crimmins. And this is a model that they developed, a heuristic biopsychosocial model of health outcomes, where when they originally developed this piece, it was meant to look at disparities in health and health care and to try to understand how demographic factors, socioeconomic status might lead to particular kind of health behaviors, might lead to these components of biological risk. And sort of in our conceptualization today, we're thinking about obesity, consistent with Vicky Friedman and others, sort of being one signal of biological risk and how that might lead to particular kinds of health outcomes, mortality, physical functioning, cardiovascular disease and the like. And what we'd like to do in this context particularly is we're really interested in thinking about differences by sex. I'm going to share some other information about the way that women and men react differently to crime, but there are also lots of other reasons to think that gender is an important concept to pull apart when we think about the nature of neighborhood life. But the one thing we wanted to do to this model, so what I'm trying to do is synthesize a social disorganization approach with this biopsychosocial model. So visually what I was trying to do is insert the neighborhood structural component into the Crimson Seaman model so that we see both that we can imagine neighborhood social features affecting biological risk and we can also imagine that there might be some kind of feedback mechanism. But what we noted that was missing in this model was, again, through this meso-level process, that it's not just about these individual-level characteristics, but the environment in which people are situated that might have an impact on biological risk and some of the kinds of outcomes we're interested in examining. So that's essentially where we are in the theoretical motivation. And I'm now going to talk a little bit about the literature on VMI. And as I said to you, I'm really interested in thinking about obesity as a biological risk. There are lots of reasons, I think, why obesity in particular is interesting in neighborhood context. Some of those reasons have been outlined in research by Jason Borman, by our own Virginia Chang, thinking about how some kinds of contextual factors like racial composition and economic disadvantage matter for obesity. And if we think about the short piece I shared about our research on the heat wave, you can imagine that if people are fearful going outdoors or if there's nothing appealing, drawing them down the block, then they may be more likely to gain weight. There are also some stories about the built environment. The work of Salas and Zick has showed us that in communities too where our sidewalks are difficult to traverse, if there aren't really easy ways for people to get out and about, apart from this component of fear, that it may be difficult for particularly older adults to navigate in community. So, you know, on the neighborhood social process piece, which is what we'll focus on, there's again some evidence that disorder and psychosocial hazards, the work of Burdett and Hill, again, another piece by Virginia Chang and the work of Tom Glass, indicate that if communities, apart from a built environment concept, that they might not be so inviting, but if we see a lot of litter and trash around, might see people in your neighborhood, you're not so used to seeing that that idea that you live in this disordered physical and social space might have an effect on the likelihood that one gains weight. There's also a suggestion in this work, particularly articulated by Cubbin and by Glass, that a focus on individual level treatment might be ineffective. So it's this idea, again, you know, if we think about how we are accustomed to treating obesity or to talking about weight, we really think about it as dialogue, usually between a physician and his or her patient. And so it's the idea, too, that interventions for something like obesity might be better aimed at a group level form of initiative. And so I hope we return to that point at the end of the paper. So I'm just going to reiterate these mechanisms. I really can force you out of this in the diagram, but it's really our intent here is to link theoretical approaches from neighborhood research to models of physiological dysregulation. I talked about this urban social context piece and the work of Wilson that's helped us think about racial differences in neighborhood SES disadvantage, the work of Rob and collective efficacy theory, and this idea that there is a collective capacity on the part of communities to control and to create a cohesive urban space. And then we're moving to, you know, thinking about, again, sort of these two models of combining social disorganization theory with this bio-psycho-social model of Teresa and Eileen. And we're thinking about, you know, potentially this mechanism of allostatic load, impact of adaptive physiological responses that as they know chronically exceed optimal operating ranges. And then, you know, really helping that, helping, I think, what that model can do for us is to help us think about elucidating these biological pathways and this idea that it must work through these biological precursors. So as I said, we're using obesity as one example today. We're hoping to draw out several from this data source and be interesting to see what kinds of ideas you come up with on that piece. Okay, so we've talked mostly about this larger concept of neighborhood social context and about how it could inform the bio-psycho-social model.  And, you know, you may remember a few weeks ago, right, there were all these B&E's in Hyde Park. Do you remember this, where people were breaking into windows? Yeah, so I'm going to kind of set the stage with that. And what's interesting about a place like Hyde Park, and we'll get back to this too in a moment, is that information about crime travels pretty rapidly. And if you are, how many of you live in Hyde Park? There's a lot of Hyde Parkers here. It'd be interesting, and we could talk about this later, how you found out about that. And how you think about the transmission of information in a community that has what the data indicates and what it feels like on the street, dense social networks and how they might matter for transmission of information. Okay, so with that, there is evidence from Samson and colleagues that cohesive social environments associated with reduced crime. And there are, you know, we can believe, or we can theorize at least, you know, this influencing us in two ways. First might be health behaviors. So we've described this idea of the use of outdoor space for recreation, changes in diet, and this idea of comfort food. So there seems to be evidence in the literature that people turn to chips. Yes, someone just did. When they're feeling sad, anxious. I don't know how many of you are feeling sad or anxious, but you do at least have those. And that exposure to stressors may increase caloric and take disproportionately saturated fat carbohydrates. So there is this idea not just that people are eating more, but they're eating a different kind of food and that that different kind of food may have implications for particularly rapid weight gain, which is what we're going to care about today. Then there's also this idea of the stress response, perceived threats trigger a fight-or-flight response that can lead to higher BMI, and this is related to the release of hormone epinephrine, and then this follow-up stage results and this release of cortisol. And there's some evidence that indicates that this particular kind of process may lead to a little bit more weight gain around the middle. So, and it's an ID, too, that over time these frequent strains can lead to heightened reactivity. You know, there's been some research here at the UFC looking at the extent to which people who gain and lose weight a lot. John Colley was one of the people who actually looked at this initially, he was in the Department of Economics now at Cornell, but that there's something, it's harder on your body when you gain and lose and gain and lose. And so when I introduced the construct of crime spikes in a moment, it's really to sort of motivate this idea that there's something that can really throw off the health of the body that is not just about gaining weight, but it's the gaining and the losing. So just to kind of think about that and the construct of why we might be interested in thinking about this idea of crime spikes. And so here we are, crime spikes. Neighborhood research tends to neglect changes in the psychosocial environment, and this is one thing that Chris and I have been particularly interested in. It's not so easy to get data that tells us anything about change. The data, and Rob actually might talk a little bit about this next week, because he's been really interested in questions of mobility. But there's some evidence that neighborhoods don't change a lot, or that the kind of data we can get from census information may not indicate the level of change that's probably really in those communities. So it's important to think about how do we obtain information about change in our neighborhoods. And so it's also important to think about the health consequences of significant increases in crimes within the neighborhood. So we're really interested, again, in this idea of spikes. So we're going to look at the extent to which neighborhoods experienced an increase in crime over a one-year interval. And we want to understand how that may independently affect BMI. And as I know, the weight fluctuation is associated with higher risk of all-cause and cardiovascular mortality. And one of the things we're interested in, I mentioned this with the idea about Hyde Park. It's not completely clear what social cohesion would do in the context of a crime spike. So the way I told the story about Hyde Park is one where someone breaks in down the block and everybody on that block by 7 a.m. knows that that occurred. And then a lot of that's happening by word of mouth and by other forms of communication because people are pretty close-knit. So most of the time we think about social cohesion as something that's protective. If you live in a cohesive community, you feel, like I was saying, everybody has your back, this idea of social control in communities, social cohesion. So you would really believe that it would bring people comfort. So that's one hypothesis. But the other, so that's really sort of as we think about the buffering piece and it might reduce fear. But at the same time, you can think about information being shared and that kind of information might cause some anxiety. So there's this idea that the connectedness of communities may actually amplify a particular kind of threat like crime. So that's going to be an important piece of the way that we approach the analysis today. And this gets us back to this idea of gender and might there be a gendered response to neighborhood stressors. The fear of crime literature suggests that women exhibit heightened fear in response to local crime. There's also this idea, too, that cohesion might be anchored on women's social networks. And I shared this recently in a workshop that Melissa Gilliam put together. And there was a lot of discussion about whether are women's social networks better? Are they more prevalent? Are they more consequential? And do women share... I'm trying to think about how best to say this. Do women share more and more information in a particular kind of social interaction? So is there really a different... Are those networks different in the extent to which you might act upon the information you received in that exchange? So the work by Roundtree suggests that there is something different about women's social networks. And we're a continuing explorer literature that might suggest some of the other kinds of concepts that I just raised. Okay, so... We argued that... We're testing, you know, residents in crimes like neighborhood is associated with short-term weight gain. But we think probably and likely this effect will dissipate over time. We want to test whether social cohesion exerts a protective effect on weight gain. And it has, to some degree, determined the way that we're characterizing crime today. Is it true that people react to information about crime, about different kinds of crime in different ways? So one of the things that I found most surprising when I started to read this literature is that in general, people are more afraid of burglary than they are of homicide. And here's the reason. We'll see what you think. That there is a belief, and, you know, to some degree the data indicate this, that people are killed by people they know. And people are killed in risky sorts of situations. So there is some belief in literature to support this that people believe they can shape their lives to protect themselves from that kind of crime. I'm not somebody who would be in a setting where I might be murdered. There's also some literature that indicates that for women, when they hear about burglary, they believe that it might lead to other sorts of crimes, like, you know, sexual violence. So that's one reason why burglary is thought to be that type of crime which evokes the most fear. Yeah. I think about a neighborhood right now is where people live. And that has been generally a convention in this literature to think about where people own a home or rent an apartment or sleep at night is their sort of main base of activity and where they are engaged in what would be described as routine activities, grocery store, laundromat, all those sorts of things. One thing that we're really interested in doing actually, and I were talking about this the other day, for a grant that we're proposing, is to look at activity space. And so that's really what, if I'm understanding your question correctly, I would get it. It's not just where people live, but it's how they live out their lives. So where are they going? What are they doing? You know, there's a general belief in the literature that older adults' worlds constrict as they age, but we actually don't have any data to tell us whether that's true or not. So that's the motivation for this new study. But right now, I'm using a crude indicator of neighborhood which is where people live, but your point's well taken and is something we'd like to extend. Okay. And I just wanted to remind you that one of these hypotheses is that the effects of intra-neighborhood change on intra-individual change and weight are what we're looking at. So it's change on change. What Laney was asking about was really this idea of social networking sites. What are they? What do they mean? How do they change that idea even if we think about activity space? One of the ways in this new study we're thinking about measuring it was some kind of chip in the heel or something. You know, it's GPS device, you know, some way that we could track individuals. But how we track them in cyberspace, yeah, we... But that would be an interesting thing to consider for this other study. I haven't seen work like that that intersects that form of research with research on neighborhood context. That's a nice idea. This is over a very short term. So it wouldn't be long enough for the neighborhood to have undergone change. So we're looking at these crime spikes so really it's this idea that we look at how an individual's weight changes and then how an individual's neighborhood has changed. So we're using individual level data in that sense. So it's really just saying, you know, how is my environment changed? How is that affecting my health? So we're not looking at anything over the long term. Yeah. Related to this study, very few people move because we're looking over, you know, on average a 10-week period. What is the title for Rob's talk next week? Because I think he's actually talking about this issue of mobility. I'm not sure from that type, but that's one of the things I know he's been concentrating on. I think, you know, one of the things we've experimented with different ways to try to get at that kind of information and also, you know, look to use life history calendars and other sorts of things, not to just know where someone lives now but how often they've moved, or what neighborhood they lived in when they were a kid, what neighborhood they lived in when they were an adolescent. All these different sorts of context could matter and could intersect from a developmental standpoint. So there isn't great literature in that there's some promise in the LAFANS data that we might be able to look at that sort of question. But the tracking is very difficult. And you particularly see a lot of movement in low-income families. So that's the biggest concern when we're trying to make inference about that. All right, so I'm going to describe the data quickly. As I said, this is the 2000-2002 Dallas Heart Studies Area Probability Sample of Dallas. We have detailed SES, biomarker imaging data. We have a Visit One social survey with baseline biological data. We're looking at ages 30 to 65. We're invited back for two follow-up visits. We're looking at information from Visit One and Visit Three. We don't have weight status at Visit Two. And that's why we're looking at it that way. In large part, although we're not sure we would have had enough movement, but it might have been nice to have that data point in between.  And that Visit Three occurred within 24 weeks of Visit One. And there's an overlap with this available crime data that I already described. And to note here an oversample of African-Americans. At the census track level we aggregate measures of social cohesion from the DHS social survey data. So again, our neighborhoods here are census tracks with your points very well taken but that's how we're conceiving of it today. We're using the national neighborhood crime data that has incident-based crime data geocoded to the census track, 2000 decennial census and as I noted that's a nice opportunity for us. Our outcome is the change in BMI between Visit One and Visit Three. 4% of the Visit One sample had self-reported weight. Not surprisingly, their weight changed more on that third visit who'd have thought. And I hate to say this but I will that it changed much more for women than for men. Yeah, BMI descriptives mean BMI women 29 men 27 so they're a little heavier and I just want to note here too that BMI or drops are BMI that was reported over 6. So with list-wise deletion and other missing values we probably lost about 7% of our sample. What are our individual level variables? Age, race, ethnicity, we're interested in form-born status particularly in Dallas, marital status, years resident in the neighborhood, SES education and income are our key measures here. What else do we know on the individual level? Well, we have Visit One BMI, we have physical activity, smoking, alcohol consumption, usual source of care and then we did put in individual level measures of social support, the number of friends you feel close to relative to you feel close to just to kind of get some sense of whether somebody might have a particular orientation. As I said we use a social cohesion measure based on the work of Samson, Radovush and Earl's. Our neighbors close-knit, are they trustworthy? Do you perceive them as helpful? That final measure characterized in the same manner as it was initially in the project on human development is an empirical based residual from a three level rating scale analysis. So, okay, on neighborhood crime rates, evidence of fear of crime by type, I've really talked about that already. We look at the burglary rate by year, city of Dallas through your average rates per 100,000 and then we look at this change in burglary rate. And this is our consideration of a crime spike. There is no literature on crime spikes. This is something that we came up with. So, we are open to other ways that you think it might be useful to characterize a crime spike. We did a very simple cut this time around top 20% of neighborhoods on change in burglary rate in the year prior to visit one interview. So what we did was we took everybody so you would each have your own crime rate. So, you would carry with you your track level crime rate. And what I'm looking at over that year is I'm dividing the year prior to your visit to one interview. And I'm saying, I'm cutting that in two and I'm saying, am I seeing a spike in crime in the six months prior to the interview date from what I observed in the six months prior to that. Does that make sense? And I'll show that graphically in a second. And then on average for those, so we took the top 20% who would experience some kind of crime spike. And on average, so in that top 20%, about, you know, they experienced on average a significant increase in crime. So a pretty significant increase for those who did experience it, which is about 40% of our sample. Okay, so this is just to show you visually what we want to show you here because this is a clinic sample. So it's an area probability sample, but it's executed in a clinic manner. So we have case one, we're following that person. This is visit one and visit three. We can have variation between visit one and visit two, case three. You can see that there isn't always for all of our cases overlap in access to those crime data. So we just wanted to show you that in some instances we do miss some cases because the crime data were unavailable during that time. I'm also going to show you if there is time. I put in this little red line on the 9-11 effect. So one of the things, one of the things that has come up in conversation one of which for my health studies colleagues is this idea that we do collect data over the 9-11 period. And one of the things that we see that's most striking is that we see a lot of weight gain. So I think we're seeing a lot of weight gain nationally. We're seeing a lot of weight gain in Texas as compared to other states. So one of the things we're trying to do right now is to draw on some national data sources to see if we can validate the weight gain we observe is something that we're seeing broadly. But also because data were collected over 9-11.  We thought it was worth just exploring whether we see a little action there. So if there's time I'm going to show you one slide that's related to that 9-11 analysis. But this makes sense. Everybody's carrying his or her own crime spike in this period of time and interval. And I just want to illustrate here our follow-up time in visit three. So we use a multi-level linear model, individual-level covariates. We incorporate time and then person-period specific crime rates. That's at level one and then level two, our neighborhood characteristics, those structural characteristics, and then the measure of social cohesion. This is our statistical model. This is our level two, which is informing level one. And what I just want to note here is that these lengths is centered at 10 weeks. So that's how we interpret the results. And then we're going to look at differences. We're going to look at two different tables and we're going to look at differences by gender. So in the results section, I'm going to show you three maps because I love maps. I don't know how much you know about Dallas, but I thought it was worth sharing a little. So we're going to look at three maps. We're going to look at two tables and we're going to look at two figures. And then at five time I'm going to show you neighborhood level social cohesion, crime spike effects over time, and then we're going to look at that interaction term that you noted. So this is socio-economic disadvantage. The measure is scaled, but what's important to note here is that as the color gets darker, disadvantage increases. So one hint from this kind of descriptive analysis, and I think it's always a nice place to begin in neighborhood-based research, is to try to understand what we see at the neighborhood level. So these are census tracts. I believe there are 193. And you can see that there is some pretty significant clustering. So that's a economic disadvantage. That is a scale score, and that includes a number of different variables, including economic status of the community, but also a percent of persons on unemployment, percent of female-headed households, other sorts of indicators. It's a, as I said, a scale score. This is the measure of social cohesion that gets darker. Cohesion increases. So you can see that there might be some. It looks, you know, not as much sort of density as we saw in socio-economic disadvantage. One of the, I think, a really important contribution of Rob Samson's work was to show in the Chicago-based data that not all poor communities have low levels of social cohesion. And so I think it's an important thing to remember that there can be lots of ways to reduce this disadvantage, but they may be socially connected. As an extension of that kind of concept, we also note that, and this comes from the work of Mary Patillo, that you can have highly cohesive neighborhoods because you have a lot of gang activity. And so you can have different kinds of social cohesion, and your social cohesion might not be my social cohesion. There may be different ways that we view it in different ways that it operates, but it may all contribute to the form of connectedness. We don't know how that connectedness necessarily unfolds in different kinds of contexts. Yeah, I mean, that's one hypothesis one might have, is that there are ways in which these communities could be connected. I'm not sure about that. I haven't done on the ground kinds of research in Dallas to really know those neighborhoods in the same way I feel like I know the Chicago neighborhoods. We can see lots of ways in which social cohesion is the same thing always in all sorts of ways. So I just think it's important to say that it could play out in a different manner, in different sorts of contexts. And this is just to show you the three-year average crime rate where, again, as it gets darker, the crime rate increases. And we see, again, there's some overlap, closer overlap with levels of disadvantage than with levels of cohesion. Okay, this is where I'm starting to show you, I'm going to show you this is the analysis for women, then I'm going to show you the analysis for men. And then I'm going to show you three figures based on this analysis for women. So this is the crime spike in the results on the crime spike. This is Model 3 of our set of models for women. As you note, we're putting in, you know, in our first set of models, age, race, ethnicity, study period length. We then incorporate a measure of cohesion. And then we're looking in this third model, the crime spike. And we're not seeing any action from cohesion, but we do see this crime spike effect. So what this is really telling us is that it looks like, you know, people are gaining weight due to the crime spike. I mean, that's that first cut. And what we learn here is that that, so that's in a BMI point what that translates into, essentially, if you're sort of, you know, a 5'5", 130-pound woman, that's going to be the same number of rounds. I think so that's above and beyond what others are gaining. And it's going to be a little easier to see this graphically, so I'm going to show you that in a moment. So I want to show you that when we look at this interaction of the crime spike and social cohesion, we saw no action from cohesion initially, but it looks like we have an amplifying effect. That's one indication. So in communities with higher levels of social cohesion, people seem to gain weight more rapidly than those without. Okay, I'm just going to show you one more quick result here, which is just to show that, you know, this, the finding on the interaction between crime spike and study length indicates that there is some dissipation over time. So what I want to show you now, and then we're going to revisit these results when we look at them graphically, is just to show you on the men, what's interesting is that we see an effect of crime rate. It's not so powerful, but there are some indications. We see an effect of crime spike for women that's significant, but not crime rate. So, puzzle over that for a moment, and then we'll look at these two figures. So this is the crime change in BMI between visit one and visit three by a crime spike neighborhood. So the top line is where communities that experience a crime spike are individuals who are resting in communities with a crime spike, and this is the non-crime spike. One important thing to note here is that everybody's gaining weight. Enjoy that lunch. Yeah, just to add. Yeah. I hadn't really fought so carefully about presenting this during lunch, and the impact it might have on how, yeah, much people cared. So, yeah, what's interesting is that everybody's gaining weight, but we do see this pretty significant increase, of course, or difference, I should say, in those crime spike communities. So I said we centered on 10 weeks, so this is where we're seeing that .46. So this is about 2.7 pounds. So everybody's gaining about 2.5 pounds over on average, a 10-week period. And, but the people who live in crime spike neighborhoods are gaining about twice as much as those non-crime spike neighborhoods. They're gaining another 2.5, 2.5 pounds from those folks. So we controlled for that because we wondered that too. An indicator in the model that controlled for whether or not there was underreporting. So this is what led us to this 9-11 question, whether there were some, you know, the demographer and me, of course, wanted to think about important period effects. And so there might be some possibility, there's something unusual about this time period because of 9-11. But, and I'm just going to show you this quickly, I don't have a website for me, but lest you think you can't gain weight that quickly.  I mean, thank you. Important distinction, regular Coke. And an extra bag of chips. You know, you could gain 15 pounds over that period of time. Now, of course, you know, when I've talked about this with some clinicians, you're not gaining at that smooth rate in this sense, but this is just a heuristic to help us think through how easy it is to pick up a few pounds. And if something changes dramatically in your environment, in your community, you could imagine that your life behaviors and patterns change in concert with that. Yeah. So we did look at those communities, so I don't have that analysis here, but we looked at the communities where the crime went down. It went down not a lot, so there's not a lot of movement to see whether maybe people lost weight because they felt more compelled to go outside. We don't observe that. They stop gaining, but they don't lose. Yeah. And this gets back to, yeah, some of the work by John Colley a little bit about the churning and, you know, the extent to which, I mean, we may know this totally that once you gain, it's hard to lose. So sort of something else that's kind of interesting to consider. Yeah. So I feel proud in saying that I just I think that grant is being uploaded today. I'm involved in a grant with Mercedes Carnathon from Northwestern and Kristen Knitzen here on neighborhood context and sleep. And that's one of the things we're going to look at is the extent to which it matters. So they're leading this effort, but I'm hoping to develop some of the neighborhood measures, but I think it's a compelling question on the extent to which just, yeah, the disruption over time and the noise and also these levels of fear that might shape the extent to which you have restful sleep could matter for lots of different outcomes, but particularly obesity. Okay. So I'm sort of showing you this trajectory and I just want to show you this last graph. Again, this is just for the women where we're looking at neighborhoods. This is the mean level of cohesion and this is high cohesion neighborhoods and you see that they seem to track but that we're seeing in those high cohesion neighborhoods people gaining more weight. Yeah. We did look at seasonality. Now one thing, and we didn't see any effect from seasonality, one thing that is so interesting though in what we learned from talking with the Dallas Heart Study participants is that our seasons aren't their seasons and so people aren't staying inside in January. They're staying inside in July and Linda with your experience in Texas maybe you can tell us if that's the case but those summer months are when people are less active from it, from sort of the kind of activity that we're considering here. But we didn't see it. Yeah, that would be really nice and that's one of our next steps is to incorporate some kind of spatial analysis so that we're borrowing information from these contiguous communities. Sean Rear sent some nice stuff on that too. So you get if you will some kind of smoothing because you could imagine if you live if you live right here you may be more influenced by what's happening in Woodlawn than what's happening in the rest of Hyde Park but your, but the information assigned to you is going to be Hyde Park specific so this is where the smoothing becomes critical. So yeah, thanks for bringing that up. Okay so we're looking at that tracking so I just want to review a couple implications and I want to show you that 9-11 graphic. So what we find, neighborhood crime spikes are associated with change in BMI but that effect seems to dissipate over time is anticipated more cohesive neighborhoods amplify the effect of the crime spike on BMI so there's some evidence that crime volatility is more important than overall crime weight for women less for men we're kind of intrigued by that finding although it's you know as I said preliminary and not so strong for the men but it's interesting and then stronger evidence of the potential causal impact of neighborhood stressors on health so we like it again you know we were so excited to find these data because there's so few possibilities in neighborhood based research in part because these things are so expensive to launch where you get change on change where you have change in individual outcome and change in neighborhood status so for us it's been kind of a dream to be able to analyze data of this kind we can maybe think about 9-11 as a national crime spike or it's you know certainly in a critical period event that might have changed the way that people perceived their communities or and may have altered certain sorts of health behaviors in kind there are few analyses that actually examine changes in fear or temperament nationally we might get a little bit out of the GSS I think I know we get some unhappiness but there aren't lots of ways that we sort of are able to track that it is interesting you know so this is just another this was a backup slide for fun but you know we made this quickly this is from the BRFSS but people are gaining a lot of weight and they're gaining it fast and they're gaining it fast in Texas and so I'm with you yeah I'm completely with you on the general point but I don't know that it's impossible and it may be that there's been a particular trend and then something like 9-11 you know had some kind of effect it may be that there's something we are you know we did test for seasonality but there may be something on the micro level there that we didn't completely draw I don't know but but we are seeing a lot of gain and I just wanted to sort of show you this to say that there were very few people who were pregnant in this analysis and we excluded them actually okay so this was really to say but we can imagine that it may be that something like 9-11 may be particularly relevant in this group and it could be we could imagine two in Texas there might be a heightened level of concern related to immigration and maybe there's some ethnographic research that indicates that pardon oh I'm sorry that there may be something associated with skin color that could have some kind of impact so we can ask whether something like 9-11 would lead us to eat more do we eat more when we're scared and do we gravitate toward comfort food again so it's the same sort of principle again this is very new but we think it's intriguing what we did was we took you know we just took the entire analysis and we looked at people who had a visit one before and a visit one after 9-11 we compared them to visit one and visit three before 9-11 because you know we have this graded kind of entry and exit into the study and then we just looked at visit one and visit three after 9-11 so more gender age as you asked about rental status health behaviors and the like we haven't extended this analysis to look specifically at race ethnicity but one thing that we have found in the initial cut is that it seems this does seem to be particularly important for the Latinos in the sample of whom are about one third of this sample so we see them gain more weight after 9-11 than their African-American or white counterparts we're intrigued by it we also know there aren't lots of data opportunities to investigate something like a 9-11 effect I don't know at all whether there's some kind of immediate connection for people I think we were conceiving it as something that was experienced at the national level in some way so not necessarily dependent on these the dyadic kinds of relationships but that it was something that we all felt and knew and experienced there were lots of discussion and changes about the way that people certainly traveled but also how we think about immigration yeah and that might have had an effect yeah that was one of our beliefs as well thank you all very much Kate thanks for having me thanks so have you


How narcissists feel when you move on. You move on, you heal, you regain your self-worth and self-respect.  Now that it's finally all over, you're happy with your life. You may have met someone else and you may have been fine before you met the narcissist, but narcissists destroy everything they come into contact with. Everything that around diminishes in value over time, so when you were around them, you lost everything that made you who you are.  They feed off your effectiveness of productive effort.  After they have damaged and injured you, they have destroyed everything of the person that you used to be and now they're criticizing you and opposing you for it for the result of their own actions because they were the cause of it, but now they want to treat you unfairly and unkindly. They want to kick you when you were down. They want to criticize you after you've already suffered a setback because they want to damage you even more because now you're not able to give them what you were given to them before. You're not able to give them what they need, so they devalue and degrade you. They dismiss you as unimportant. They treat you like garbage.  They begin to see other people as being more valuable than you are because other people have more life and energy and you may have given everything you had to them to sustain them until you were left broken and confused because when you're looking to them to uplift you in return they're gone. They vanished. They're off entertaining someone else. They're training another person for the purpose of being their next victim because nothing you did really mattered to them. They didn't appreciate it. They took it for granted which is why they never gave you recognition or favorable notice of your acts and achievements. They never made you feel special. They never give you compliments or praise or anything like what you gave to them which is what kept them around in the first place which is why you will experience current stress from dealing with them. You will experience feelings of energy depletion and exhaustion which may include mental distance and feelings of negativity where you may feel like giving up in your life. As a result of trying to satisfy them and make them happy it's just going to destroy you at the end and once you've given everything you have and you have nothing left to give. They're just going to see you as the least important person in their lives as though they can do better than you. They can find someone better which is crazy because they can't be satisfied. Nothing anyone does is going to make them happy. They leave you for dead instead of trying to help you or maybe you leave once you've lost everything and now you have to find a way to recover. You have to find a way to get back to being the person that you were before and when you do that you start to feel happy again. You start to realize everything that you went through with them. You figured them out. You know what you were dealing with but they don't want to accept that they did anything wrong. They want to blame you. They want to say that you're the problem but when you move on and you end up doing better without them it's verification that they're not the good people that they portray themselves to be. They're not what they believe they are in their minds.  So it eats away at them because in order for them to feel good they had to make up a story of why you weren't good for them but then you moved on and you did far better without them. It destroys their false narrative. It destroys everything that they were telling themselves in their minds to make them feel better about themselves. So when they see you consistently being happy and successful without them it destroys the lies and stories that they have created to defend against the idea of them being the problem and the more they have to witness your happiness and success it just completely eats away at them because that is the last thing they want to see. They don't want to see you doing better than you were doing with them. It makes them mad. It makes them envious and jealous. It makes them hate you once you've moved on because now they're left to accept that they are the problem but that is why they will hoover you to prove that they are not the issue, to prove that you're not doing so good without them because everything has to be about them. So when they come back it's just for them to prove to you that you're not really happy without them and even then they're just going to destroy the happiness and success that you managed to rebuild while they were gone. Thank you for watching. If you found this video helpful please give it a thumbs up. Share your thoughts in the comment section. Hit the subscribe button to receive the notifications. If you would like to support the channel you can donate at paypal.me-nax survivor. You can book a one-on-one with me on our website. It's NAX survivor the Cuddy UK. Thank you for watching and I'll talk to you soon.


What's going on agent nation of my name, of course is the agent beam star. Let's get Right into the news For our first story of the day you might have caught NBA 2k's live stream They've never really done anything like that before they went live on their YouTube channel And they said they had a special announcement and special announcement was basically that Yanis was a cover athlete But if you watch one of these fake bootleg genre videos that did a couple weeks ago You would have already known that because there was a lot of rumors and leaked images going around and and I had some Verifiable sources tell me Yanis is on the cover. So we knew that already it kind of spoiled the surprise But at the end of that live stream, they came out with a little bit of information They dropped a screenshot of Yanis and NBA 2k 19, which means that's their first look into the game now Take a look at the picture man It's just a guy that's he's sweating a lot like he seems to be sweating a decent amount And I know for a fact that if he was in this room in this sunroom with no air conditioning that Yanis Making this specific video would be sweating more than me. So that's all I needed to know So you can expect over the next few weeks for NBA 2k to come out with some screenshots of your favorite NBA 2k player And then like a month maybe a month and a half from now They'll start to drop trailers with like a hint of gameplay, but it's not like 5v5 gameplay It's kind of like them panning around like trying to make it look super cool Then two months from now when we're getting really close to the games launch You guys can expect to see 2k might pull up at gamescom I dealt a pull up to PAX West so you probably won't see any gameplay there, but you will see a gameplay trailer So that's usually how they do things they've done if like the last four years So don't be surprised if they don't come out with actual gameplay for another couple months for our next story of the day How closely do you follow the NBA 2k league because they're doing something interesting for the first time in the league They're actually patching the league build So we haven't seen any patches in 2k 18 for a while, but they're patching the league build now If you've watched the 2k league, you know a lot of what it is is like the shooting is incredibly difficult So a lot of teams just dump it into their center and paint match and try and spread the floor and just pump fake And then go out for a dunk. That's what happens a lot a lot a lot but there's a lot of changes that happen and I heard from a few people that they made a lot more Changes than what they described, but these are the changes. We know of so far I put out this tweet describing the changes said NBA 2k league patch rim protectors are less efficient because everybody's using rim protectors rim protectors had the Best dunking animations of all of the builds in the NBA 2k league pierce sharps have less ball handling It was already incredibly low pierce sharps have nerfed layups dunks contested shots Free throws are more difficult and there's a shooting buff across the board I guess the purpose of this was they wanted people in teams to have more than one way to win a lot of those changes I disagree with I don't know who's making these decisions or whether they even play 2k I also don't understand why they continue to nerf sharps shooters like the sharps shooters in the NBA 2k league Do not have glitched animations, right? And I played on the league build and it was incredibly difficult to dribble around They were sitting double teams on me and every bump meant I was losing the ball So I don't know what the point of nerfing the ball handling on sharp shooters is I don't know we'll see how it plays out. I'm a little bit skeptical But I hope they have people in there making the right decisions. I'll be watching this week for our next story of the day Ladies and gentlemen, uh, I mean 2k almost released 2k 18 let it crash and burn And now they're trying to like give CPR to it and bring it back to life interestingly enough because they've been coming out With a suite of content for my team if you played my team, you know There's been a Tim Duncan a pink diamond Tim Duncan that dropped yesterday And it was it was available for like 10 minutes and then they dropped another batch of them And so if you do play the game mode, there's no way you didn't get the card unless you were trying not to get it You literally had all the time in the world, you know on top of that date for July the 4th They usually do it for like holidays. They dropped a double XP on the park and you know me I don't really care about double XP or nothing But a lot of people were excited and hyped about it and they had me asking one question Why didn't we get one for Canada Day? Does Canada Day not matter to 2k? I felt offended man. Also, they need to change the progression system for next year. This year is pretty booty butt cheeks It seems like the 2k community is kind of bouncing back a lot of figures that left or maybe did other games because they lost interest They're slowly coming back like yo, let me be like on the on the edge right here Just looking in like yo, what's 2k 19 gonna be like so we all got hopes, of course I'm trying not to you know get my expectations too high cuz that's just a recipe to get let down But I'm slowly getting more and more excited for 2k 19 They can't possibly be as boring and dry as 2k 18 so that we know right it can't be worse than that for our next Story of the day ladies and gentlemen if you've been following the drama You know that Los Polos and cash nasty have been going at it Los Polos even dropped a diss track on cash nasty, but that's not the interesting part The interesting part is that I think I may have been at the core of all of this Now I don't have anything to do with their beef per se But if you remember a couple weeks ago, I hopped and team up and I did it a video trolling cash If you missed it, it was priceless Troy Dan was wild and now it cash nasty was furious It was so much emotion and in that video I said that I failed that troll cuz I didn't get cash as angry as I thought he was gonna get Ladies and gentlemen, I am here to amend my previous Statements that troll was a massive W because that game I played with cash against Los Polos was the game that started this whole Torpedo that's resulted in the diss track after that game cash went into Los Polos lobby after losing for the third time in a row and He went off he was calling those Polos a bitch He said you're my bitch and they were going at it They were furious at each other that kind of just escalated to the point where Los Polos dropped a diss track a Diss track in which at one point he was taking shots at cash in the most fantastic way It was like this was professionally written. This was not a youtuber writing this some he paid He must have paid someone to write this because it was so well-written fact. Yo, let's react to the thing right now Yo, I'm sweating cuz I'm in a sunroom with no air conditioning and and Whatever y'all can leave your comments Ladies and gentlemen, Los Polos had this professionally written if he didn't and he has proof that he wrote this himself That is so much like respect because it was fantastically written and he got the Duffy first going and the Duffy freestyle I see you I see you Right 90 angle degree your whole life fell apart and you blamed it on me. Yeah Don't push me when I'm an album mode. You're not even top five as far as to why your talent goes Oh, you're ship-born like we're the passion go six pack by the summer. I mean salad bowls We was brothers. There was peace in that. This is nice. Now your youth.  He's not holding back man Don't make me expose. I was your brother. Now you saying that I'm cloud chasing watch your mouth when you're making all these Wild statements. I just saved up your career from being outdated. I sent an invoice I need a down payment. That's nice. Yo, I know he took that Drake flow, but he kind of had to it was a Drake It was a Drake produced. I'm sure he didn't put you get what I mean. All right. Hey, it was nice That's all I'm trying to say bro. When I saw that drop I knew for a certain fact that that That troll that I did on cash that kind of began all of this because his his eruption on loose was after his third Lost which I was a part of I was like, yo, this is a W right there Which means that including swan say cash nasty and hang the tank. I am now 3 for 0 in my trolls. I told cash I wouldn't do it again. I Don't think I could be trusted anymore, but that was fantastic. It makes for good content shout out to those Polos man I was really entertaining Ladies and gentlemen, you cannot make this stuff up It's been 2k drama alert if you guys enjoyed you can watch some more of the drama alerts if you missed them is the whole series right here or Random video if you're interested the title might be interesting. I'm fantastic with my titles I'm gonna catch you guys in the next one drop a like if you haven't already put on them post notifications. I'm out. Peace