 Okay guys, we're going to get started. We are sorry for the delay. Thank you all for joining us today for our live presentation. Our speaker today is Dr. Gayatri Kallura, Adjunct Assistant Professor at Columbia University. I am Anna Dimina, the second year student and urban planning program. I'll be moderating the section today. Before I introduce our speaker for today, a couple of logistical moments. First of all, we are recording today's lecture, so if you don't want to be recorded, please keep that in mind. After the presentation, we will have a Q&A session. We'll start the Q&A around 2 p.m. to 15, so we'll have enough time for everyone's questions. I will aim to give everyone an opportunity to ask the questions, so please, if you had a chance to do so, give others a space to ask their questions. To ask the question, please raise your hand and I'll get back to you. Unfortunately, we are sorry. We don't have food today. We'll have it next week, so please don't worry. So with that, I'm excited to introduce our speaker for today. Gayatri Kallura is an urbanist researcher and adjunct assistant professor at Columbia University. Her research uses quantitative methods and geospatial tools to engage debates about governance, technology, and justice through the lens of infrastructure and geography. Her recent publication examines the effects of spatial inequalities in the availability and access to critical infrastructure on spatial, on health vulnerability in New York City during COVID-19. Professionally, Gayatri works as a data science consultant and recently co-founded Earth Labs, an enterprise focused on simplifying the way we interpret collective data. Previously, she worked as a strategy analyst at the Rockefeller Foundation in York. Gayatri calls a PhD in urban planning program and is a master in global thought from Columbia University. So please, welcome Dr. Gayatri. Hey, everyone. Wow, it's so weird to be back here. I just graduated in summer and also posted lips like a couple of years ago, so it's kind of surreal. Thank you for having me. I'm not going to spend too much time introducing myself. I'm actually teaching the informatics class this fall. So all my informatics students that are here, grateful. Thank you. And yeah, I just wanted to talk a little bit today about my dissertation research, which I very recently finished, so it's pretty fresh. We'll see how it goes. I'll be focusing more so on the methodological and theoretical considerations of my research, less on the findings and results of the work. So hopefully this way it's a bit more engaging and opens up some interesting questions for us to think about. So the agenda for today, we'll talk about some of the themes for my research, one of which is disruption as method. We'll talk about why does infrastructure matter. We'll go into topics of actors and agents in processes, infrastructure and government, information geography, which is lecture, and I'll end with some closing remarks, some studies and open it up. So with that, I'll get into it. So I wanted to start with what I call disruption as method. Here I essentially use COVID-19 as an entry point to engage debates about infrastructure power and justice through the lens of geography and technology. And I've kind of done this throughout my work. So I'll be presenting one of those studies. I essentially argue that the COVID-19 pandemic has been a prism and an amplifier for the uneven geographies of the city. And it's revealed decades of history that have worked to maintain boundaries between communities in New York. So in this research, I actually critically examined the paradoxes, linkages, and questions embedded in infrastructures that shape and are shaped by the politics of the pandemic. I use Stephen Graham's concept of infrastructural disruption here to discuss New York City's uneven geographies during the crisis. So according to Graham in his chapter, when infrastructure fails, infrastructural disruptions are heuristic devices which reveal the hidden politics of daily urban life and serve to problematize the very normalities of stable flow and circulation. This conceptualization essentially follows from the idea that design is invisible until it fails. So power blackouts, subway system failures, things like that all bring the city's electrical circuits and transit systems into clear view. And sociologists of technology essentially call this a social process of unblackboxing or unmasking of complex systems and technologies that shape our everyday lives. Similarly, Colin McFarland in his book Infrastructure, Interruption, and Inequality finds a relationship between inequality, interruption, and infrastructure in his fieldwork in cities of global south that I also believe applies here. And he essentially talks about the fact that crisis in infrastructure essentially reinforces power relations in local government response. Crisis themselves are actually mediated by forms of inequality and particular forms of crisis become the new normal for some which are worth paying attention. And finally, in their analysis of the 2008 foreclosure crisis, a study by Pais et al emphasized the crucial role of neighborhood perceptions during crisis. They essentially argue that neighborhood sentiment, resident sentiment influence mobility decisions often reinforcing urban area stereotypes and perpetuating spatial inequalities. So again, this underscores the criticality in examining participation in 311, especially in a moment of infrastructural disruption as it offers insight into neighborhood socioeconomic disparity, conflict, decay, and the role of the state in mediating some of the circumstances. So for context, this study, I look at New York City's non-emergency complaint line 311, which essentially is a repository of millions of geo-coded citizens requests that have historically displayed, I argue, uneven geographies of participation. A lot of you in the informatics class have had exposure to this and others have as well. But it's a really interesting data source, especially for urban analysts. And especially during the pandemic, which bore witness to a flurry of neighborhood level tensions between race and public health, protection and policing, social order and justice, safety and privacy. The 311 system became a critical line of communication as citizens grappled with escalating health threats, stringent government restrictions, and a hiatus in city infrastructure provisions in general. In particular, the surge actually peaked in August 2020 with a total of about 300,000 requests made, almost 100,000 more requests than average monthly rates over the last decade. Another feature of 2020 was the elevated crime rate that New York saw more so than it's seen in an entire decade, also followed by mass protests triggered by George Floyd's death and existing racial tensions and police brutality that sprung up that year as well. In response to this, the Department of Technology actually introduced a number of new complaint categories that are related to social distancing violations. So some of these here are like non-compliance with phase reopening, mass gathering complaint, non-emergency police matter related to social disorders. So all of this is kind of the system responding to new levels of interaction with the service and what that kind of means for levels of distress or service demand that are not being met or trying to be met by the pandemic city or the response to the pandemic. So my research hypothesis essentially is that the engagement in 311 surged in socially vulnerable neighborhoods in 2020 driven by COVID-19 case rates and escalating socioeconomic tension. I also argue that complaints regarding social disorder in particular made up a majority of the increased call volumes in these areas revealing a heightened propensity for residents to police their neighbors during moments of crisis and we'll unpack what that is. But this research essentially is meant to offer some new methods and approaches to understanding existing questions of power, race, class order, and ultimately I show that the uneven geography of 311 complaints in New York City during the pandemic is an important indicator of the existing cracks in neighborhood cohesion, public trust, and technocratic governance that pervade the city's underlying and the pandemic actually offers a mirror to better understand some of the forms of power and the political economy that underlie. So to get to the question of why infrastructure matters, as you'll see infrastructure is a key component of this work and it's critical to my dissertation research in general. I use infrastructure here as a material vehicle to analyze the relationship between people, justice, and space. I think understanding the city and its systems through the lens of infrastructure which is an otherwise mundane aesthetic material form is actually a fascinating methodological exercise and I think allows for some sometimes absurd yet encyclically paradoxical linkages and questions that that may be worth exploring. So my research actually explores three different modalities of infrastructure, one is the mobile phone, one is the 311 complaint line and the last one is infrastructure services in general. So for the purpose of this talk I'll focus on the 311 platform and ultimately my goal is to observe how each of these forms of infrastructure and are in turn shaped by the pandemic. So what is infrastructure? Infrastructure is essentially vast intricate networks of physical technological and organizational systems that bring modern life. According to Reinhold Martin, the professor here, infrastructure is actually that which repeats, so essentially systems that repeat themselves constantly, railroads, postal services, satellites, highways, housing, housing, classrooms, all of these things that were in the process of consistent repeated forms are essentially and I take this further to say that they're not only system of repetition but they're also a system of measurement and record that quantify, track, and maintain our everyday interaction, shaping our local environment in critical ways. In terms of the scale of infrastructure, Paul Edwards argues that there's three different scales. The first is micro which is visuals and small groups, macro which is institutions, standard setting bodies, and then the last one is large scale system structures like political economies of the country. And so essentially what this research aims to highlight is the connections and juxtaposition between each of these scales. Ultimately my goal though is to highlight and make visible the kind of displacement of individuals and small groups caught in the everyday micro layers for our social and political. To give an example of what we think about as infrastructure and why it might be political, this recent essay by Shannon Mantern called Fountain Society evokes the otherwise mundane drinking fountain to discuss society's attitude towards health, hygiene, public goods, and civic responsibilities. She quotes political scientist Bonnie Honegg to argue that public things actually furnish the world of democratic life. So I don't know if you've all read but like politics have artifacts of text that all of us had to read but essentially how we think of daily objects as reinforcing and shaping our political lives is what I'm essentially. In the same way that the fountain tells us about the politics of health, hygiene, and civic responsibility, the 311 line I argue tells us about the fragmentation of infrastructure services, the distribution of systemic failure across neighborhoods, and citizen participation in urban maintenance. An example of complaints made on 311, you can see here these are just like a few snippets that I picked. Things like mundane things like a block driveway, illegal parking, graffiti, homeless person assistance. These are all sort of captured systems of failure by the system and I would say that this kind of shows you a couple of different things. The first is that there are thousands of infrastructural disruptions that are happening around the city at any given time and 311 actually serves as a way to capture and record a lot of those. It also shows you a sense of what the New York government or the local institutions think of as disorder, which is essentially a constructed firm here. These are categories that people have coded in. Nothing is effective. It also gives you a sense of the vast and intricate network of New York City's organizational system because you can think of everything from streets to parking to homelessness, all of these different systems that are coming into play when you're looking at this list of disorders essentially. The last thing that I think is actually interesting is that the 311 complaint line actually makes itself invisible when you look at these complaints in this way. You forget that this output is itself a system of organization. Instead, you get lost in the crises that it's supporting. Turning to an interesting art exhibition by Meryl Euclid. She did this exhibition on maintenance art in early 2000s. It basically serves to question what the routine everydayness of maintenance looks like and how when we shine like a specific spotlight on it, it really tells you about the underlying political and sociological and economic crises that are actually underlying them. She argues that maintenance is a drag. Essentially, she has an excerpt of what she thinks of it. Clean your desk, wash your dishes, clean the floor, wash your clothes, wash your toes. All of these are just mundane everyday actions that you don't really think about because they become invisible to us. What I'm trying to say and what I'm trying to gather from a lot of these arguments is that these everyday interactions are not benign. Essentially, 311 is a system of maintenance in the same way washing your clothes, drying the dishes, all of these things are systems themselves. She calls it art and here we're looking at crisis. There's two different ways of spotlighting what maintenance is. She's made it an exhibition and I'm trying to look at crisis as a kind of exhibit to understand. You can see here these are one of her exhibit pieces where she's actually photographed doing a bunch of routine everyday activities and it was supposed to be kind of thought provoking as a white woman who's fairly well off as an arts degree kind of doing these things that are really not reserved for somebody like her on the streets of New York. Similarly, Wendy Chun in her book Updating to Remain the Same, which is an excellent book, I'll read it if it's coming up ready. She suggests that media matter the most when they seem to not matter at all. So when they've moved from new to habitual, she argues that the smartphone, for example, no longer amazes us but they increasingly structure and monitor our lives. Through habits, Chun says, new media become embedded in our lives. Indeed, we all become machines. We stream, we update, we capture, we upload, we link, save, trash, troll. All of these are mundane actions that we don't think to question. And in the same way, infrastructure, I argue, has a mundane everydayness that makes us overlook some of the politics that actually it's the hide. And Chun actually has an argument that habit plus crisis equals update. And she argues that crisis is actually a moment when the habituation of infrastructure is undone. It undermines the autonomy of the habit and in turn shows us that these are addictions that we've gotten accustomed to and it forces you to kind of make a change. And so my question is, what did the crisis of COVID-19 do to the habitual systems of failure recorded on New York City's 311 complaint line and what does it tell us about the spatial politics? So in this section, I actually would like to discuss some of the agents of maintenance that we discussed previously. And these are essentially the users of 311 systems and their motivation. I won't actually be speaking to the work of maintenance itself, which I think is saying category of exploration on its own. But here, we're talking about the people that actually interact with them on a daily basis or at all. So the 311 system was set up in New York in 2002, following early examples of them in Chicago, San Jose and San Diego. Bloomberg announced his instance of city-wide service for non-emergency falls in 2002. 311 platforms essentially act as citizen feedback systems. They encourage city residents to behave like users or customers of a city, participating in urban management and maintenance and providing feedback on failures and disruptions in city services. But unlike customer feedback systems, citizen feedback systems serve as a contract between governments and citizens. So citizens are actually able to pressure local governments to fix their street conditions, place police parking violations, close down loud parties. You suddenly have a sense of a place in the process of maintenance of the city itself. And we'll be looking at what this looks like in the pandemic moment. Salim and Haq argue that the new normal in most organized societies requires that citizen engagement be explicitly embedded in the design, development, and maintenance of urban infrastructure. So essentially we always have a process to provide complaints and feedback. We have this in most private sector products and services as well. And essentially it offers a way for these services to exist while making you feel like you're participating and contributing to them. And we'll look at what kind of participation that actually entails. So the plan IT NRC document that was released on the 311 line was getting set up. The vision was for New York City to use information technology to treat its residents, businesses and visitors as customers, providing information services when desired, but eliminating the need to understand how city agencies are organized. So when you have an online system of interaction, you rarely feel like you're going through the bureaucratic process of getting somebody on the phone, figuring out which department to contact. You have like a one-stop shop to really understand or to make your complaint rather. So it really is more of a portal than a two-way interaction of participation. Scholarship has argued that the degree of accountability a platform has essentially depends on the level of participation it encourages. And the extent to which user feedback impacts the development of our productors. Because of the low cost, quick and easy access to government information and services that 311 increases, it actually enables a high level of participation and has the potential to maybe democratize public discourse in a way by allowing a diverse group of individuals to voice their concerns and report issues in their community. A Wall Street Journal report found that in 2020, the NYPD charged with enforcing the city's social distancing rules that we saw in a couple of slides ago. They actually took action on four out of the 10 reported complaints, which is actually pretty impressive response rate and encouraging for more users to come on and make requests. So why actually participate? What is the motivation for users to participate or citizens, residents to participate in this system? White and Trump have a study that argues that the act of placing a 311 call is actually a low cost form of political participation. They argue that digital platforms like 311 offer city residents access to local government in a way that has shown to instill a sense of trust and promise of increased bureaucratic responsibility. Here, one might argue that these systems exist essentially to make you believe or feel like you're falsely believing that these systems are actually responding to you and instead you just feel like you're participating in the main city almost as a way to kind of like silence your actual presentation. But given the response rate, this may actually not be true. So some of the reasons why people participate as scholars should have argued is to get access to information for a sense of community prestige and influence. Things like resident efficacy, right? Like some individuals might actually feel like it's their duty to take care of their neighborhoods, make sure they're being well maintained, make sure it's safe. Neighborhood reputation, some individuals might not want their neighborhoods to be hot spots for noise or partying or garbage decay. Resident sentiment in general, studies have shown that if residents in general feel pretty happy and proud of their neighborhood, they're more likely to call in 311 because they want to keep it up. And lastly, there is a sense in which people tend to call into feedback system, even the private ones, in order to lodge complaints about other users. And this is probably the most popular form of participation in most user feedback systems in 311. In fact, the fact that it's called complaints or requests is pretty telling in terms of what kinds of things people call in about. So here, I just wanted to kind of encourage you guys to think about the kind of governmentality a system like 311 encourages. It's essentially a crisis management system that is promoted, that's focused on reactionary government fixes rather than long-term planning, right? So you call in about a broken window, you call in about illegal parking, someone can come in and make that fix right away. But you're not really thinking about the longer term systemic outcomes of some of those issues that maybe compound and make neighborhoods less or more vulnerable than others. Citizens are kind of turned customers that are encouraged to complain about the city and citizens as a product service, public policing grants some citizens neighborhood watchdog status and it creates potential conditions for government surveillance, policemen conduct and potentially even a criminalization of certain neighborhoods as some scholarship is argued. So here you might ask, so is 311 truly a public good if it can actually lead to a lot of these negative consequences? And here, I kind of will turn to this concept of broken windows, which essentially is a theory of crime that popped up in the 90s, especially in New York. And since this theory came about, there's been a lot of scholarly and political attention paid to measuring the physical conditions of neighborhoods. The theory actually says that when there's a broken window and it's left unrepaired, it's essentially doesn't elicit the window to be repaired, but more windows. So in a sense, if you look at disorder or disarray or decay, you're less likely to go fix it and more likely to compound it. Robert Samson's work on the stability of change of Chicago neighborhoods actually argues that perceptions of disorder are what molds reputation reinforces stigma and influences future trajectories of a neighborhood. And his work highlights the important implications of perceptions of disorder and how they hold true for the evolution of urban neighborhoods. Experts have actually commented that the introduction of services like 311 make long-term residents tangled up essentially in the criminal justice system because they're complaining about quality of life crimes that draw into these initiatives, which a lot of experts or scholars argue is highly unnecessary, especially for the kinds of crimes that are being reported on. So this is an excerpt from Reinhold Martin's book, The Urban Apparatus. And here I'll just read it out. A window here is a threshold between environmental order and environmental disorder that takes the form of what Kelling and Winston call a signal. It is also in their accounts a filter between orderly and disorderly being, not as they say violent people, nor necessarily criminals, but disreputable or obstreperous or unpredictable people, handhandlers, drunks, addicts, rowdy teenagers, prostitutes, and mentally disturbed. It actually provides a picture of what the aesthetic of failure looks like and what it might mean for urban neighborhoods in general. And according to Martin, the aesthetic of urban order actually accounts for its outcome. So if neighborhood is disorderly, it's more likely that the outcome of the, in terms of economic, social, political is actually negative as well because of the disorderly appearance. So then one might ask is 311 an important infrastructure or record keeping, and who actually benefits from the existence of the service? I'll just gloss over for running low on time. But essentially socioeconomic disparities in the spatial one is what we are most concerned with in the study. And questions like are people more likely to call in from less vulnerable neighborhoods, considering the work in Windows paradigm? Are they likely to call more if they are increasingly vulnerable? What happens during moments of crisis? Studies have shown they're actually evidence that 311 and 911 calls are rising unevenly, gentrifying neighborhoods. As demographic shifts, activity that was previously considered normal becomes suspicious as newcomers, many of whom are whites or different ethnicities and other neighborhoods are more inclined to get law enforcement involved. So why does this all matter? We get to the question of geography. Just briefly, information geography is a stream of scholarship that argues that geographic augmentations or digital augmentations of place are much more than just representations of places. They're actually part of the place itself and shape it as much as they reflect it. So in the same theoretical vein as what we're thinking of is infrastructure that is constantly shaping and shaped by society, information geography is a way to think about code or data right across space that acts as the kind of infrastructure that shapes and is both shaped by political or social. The information geography of 311 then is the spatialization of the repeated records of infrastructure disruption. And it matters because it tells a story of how spaces and therefore neighborhoods are counted, perceived, engaged with and transformed by the politics of the city. And consequently, spatial patterns of 311 requests actually hints at probable shifts in future dynamics of that neighborhood as well. So reasons why it matters. This research is that it potentially gives local authorities or governments an understanding of participation hotspots and cold spots in New York City during an emergency. It offers a calibration of the level of service demands placed on city governments by individual neighborhoods. And it is suggestive of subtle forms of conflict and essentially even a deepening of mistrust between neighbors and select neighbors. And why all of this matters during a moment of crisis is that during periods of relative stability, neighborhood sentiments typically remain consistent. However, when new realities come about like the foreclosure crisis and COVID-19, there are new ways of living that come about and the upheaval may challenge prior assumptions as individuals and communities are forced to confront deeper systemic issues and intersecting that arise. The New York City's stay at home order, in addition to COVID, did elicit a radical shift in the demand for infrastructure services. And I argue that the uneven impacts of the pandemic on residents of color in underserved low income communities are revealed in the information geography of 311 science and and now I'm going to breeze through the findings. That's a little less, but this is essentially, this is essentially the different data sets I looked at. I looked at six years of 311 data. I looked at the social vulnerability index at the census tract level and I looked at COVID-19 case rates between 2020 and 2021. And methodologically, briefly, I did two different machine learning regression analyses. The first one I did a time series prediction model, essentially, that looks at five years of 311 data between 2015 and 2020 and forecasts what 2020 would look like, essentially. And then we compare it with actual complaints to see the level of difference in the in the service request itself. And then I use the SVI to kind of give us an indicator of the neighborhood vulnerability and how that coincides with a level of complaints in those neighborhoods as well. And I look at COVID. So this was the first bit of the analysis. And the second one, I do machine, I just do multivariate regressions to look at three different models and see what kind of correlation we find. So here you could see the time series plot shows two distinct phases of the pandemic. Essentially, the black line is actual 311 complaints and the red line is the forecasted 311. So you could see there's an initial surge in calls during the onset of the pandemic, peaking August 2020, around 300,000 calls. And one can argue this might be due to like the phase reopening. That was the time in which I think we were in phase three where restaurants and things were opening as well. And that might be a consideration here. But overall, the level of complaints still remained above pre-pandemic levels, even at the end of our study period. And here you could see the total 311 requests in 2020 were about 2,700,000. Those of which were related to social disorder were about half of that, which is pretty significant. And I define social disorder complaints below here. There are two different studies that categorize complaints based on different types of social disorder. And I use that couple of times. If you look at the forecasted complaints, it's about 500,000 complaints off of what the average number of complaints would be for 2020. And social disorder complaints is almost double here as well. So really, there was a lot of calls related to social disorder and a significantly higher proportion of calls during the event. And here I've essentially mapped out the change in 311 requests between the previous model, which is like 2015 to 2020, and the zip codes for 2020 alone. And here you can see the darker spots are where the percentage is higher than average, and the lighter ones are where the model is potentially under predicting complaints. And on the other map, you see essentially social vulnerability index coupled with the COVID case count. And so you get a sense of one correlations between COVID case rates and social vulnerability. And then you can start to piece together a story of the level of service demands from 311 and social vulnerability and COVID triangulating. These are some of the neighborhoods with the highest 311 service request demands in 2020. The largest discrepancy was identified in WAC, which is the Bronx, where the DOITP actually fielded I think 76,000 more calls than the model actually prevented. And although the Bronx did not actually have the highest rate of COVID, among the city's boroughs, outcomes in the Bronx were reported to be more severe with the highest hospitalization and death rates, which might explain some of the discrepancy. More over 90% of Bronx residents are minority residents, and many are also essential workers. More than 70% of the Bronx workforce works in face-to-face histories, especially during that time. But despite the models under estimation of call volumes in these neighborhoods, the results are generally consistent with areas known for their active participation in the digital problem. In contrast, the neighborhoods that most overestimated by the models were Hell's Kitchen, with an excess prediction of 2,700 complaints, followed by places inside the Staten Island, Windsor Terrace, and Brooklyn. These are fairly well-off neighborhoods that have lower SVI scores, hinting at a potentially inverse relationship between forecasted complaint patterns and so forth. These are some of the results from the models that we looked at. So I essentially have four different models here. The first one looks at complaints between 2015 and 2020, five years of training data, essentially. And I look at the regression of that with just SVI. You can see that social vulnerability definitely impacts the level of through and on requests, but not compared to the later models. The second one looks at COVID-19 and its relationship, the request during COVID-19, and its relationship just to SVI. Again, it's about 30%. So still fairly significant model. You can see all significant values, but it gets more and more significant as we look at SVI and COVID together, like model 2A, essentially, is our model of SVI, where it's almost 75% of requests can be predicted based on the social vulnerability of the neighborhood and the level of COVID rate in that neighborhood. And you can see the arena, the forecasted models, don't actually work well. Social vulnerability. So essentially what my research has contributed to, hopefully, is literature on information geography. And I've aimed to tell a story of uneven participation in government services during emergency situations like COVID. And departing from other research and information geography literature, this research actually finds that an increased digital participation amongst socially vulnerable communities during moments of crisis. So in other literature on information geography, you often see that things like the digital divide play a huge part in who participates in different services platforms. Here you actually see in moments of crisis, vulnerable groups tend to turn to the government for assistance and help, which brings us to questions of public trust before and after an emergency. What kind of individuals are most vulnerable during these times? Are these individuals that actually remained in the city while others were leaving, either work from home, Odin said their work, et cetera. And I will end with some closing remarks and we can open up questions. Hopefully, there wasn't a lot coming at you. But some of the limitations I would serve in this study is that problems that are actually reported on 311 occur unevenly across neighborhoods. So there's really only so much you speculate in terms of the variation and opportunities to complain. Like some neighborhoods just tap for infrastructure. You're going to have more plot holes. So there's probably more people. There's also a risk of a few heavy users or super users of 311 in a neighborhood that could have a sequence on the total number of calls that area. And this is not something you age from the data. So it is. And the last one, which is actually what I would like to focus on my future work on is that I've only looked at SBI as a baseline indication of socioeconomic disparity. But I think more nuanced indicators of conflict and difference within neighborhoods such as measures of gentrification would be an interesting future study for some future questions that I'm left with at the end of this work is, you know, what can studying differences in 311 participation crisis events tell us about neighborhood resident sentiment and custom state. So maybe it's between COVID and the foreclosure crisis or Hurricane Sandy. Like are there different levels of demand? Are there differences in participation between neighborhoods between these crisis events as well? And what can that really tell us about some things? New York City police data on arrests during this time might also be interesting to collate with the level of service demands, especially looking at some of the police related complaints that 311 captures. Another question I had is what kind of infrastructure failures are actually normalized during a moment of crisis like COVID and in which neighborhoods. So which of these systemic failures kind of become permanent wherein people stop complaining about them because they're so routine? You know, in Columbia it might be there's an issue of street trees being cut down or something like that and you stop noticing them. People stop complaining about them. There are like routines of failure that get absorbed in space and an interesting exercise would be to kind of dig up some of the failures that we've absorbed since the crisis started. And lastly for my data plugin here, 311 data actually be open access considering a lot of the negative consequences we've seen through this in terms of who's actually being captured in the data. What does they say about the people that are captured? Where are the gaps? Do we think this kind of data should really only be available to institutions or should it be open access to universities? These are some questions that are worth it. And yeah, stop here. Open for questions. Thank you. Thank you, it was a great thank you. Any questions who didn't record that they have COVID? Yeah, great. Yeah, I mean that's a really interesting question. So things like people that don't test or maybe don't get vaccinated and there's a correlation. Yeah, super interesting. Yeah, so that's why I had in my slide here that police brutality, correlation between that and the kind of confines were made is something that's worth it. I did not look into it, but I think that's an area of confusion that could be. I will say it is harder to collate just because you're going to really have to look into the timing of things and that gets a bit shady. We don't know how well they capture live requests, everyone wants either. So it gets a bit tricky, but not impossible. Yeah, that's definitely a possibility for it. I think New York City was putting much more as a hotline to explain about non-police officers. So I think that's definitely that there was more awareness about it, which is also interesting. Is it because of a lack of awareness who's previously aware? Yeah, I mean I don't have all my findings up here, but yes, there were. There were specifically more calls related to social distancing violations. And you can see the neighborhoods as well called in more about those. I broke down the maps by types of complaints as well. You guys are interested in representation. Yeah, I mean, I think it's an interesting question of what becomes like a lot of the policies that were put in place by the city during COVID was things like you could drink outside on the park. That was not something that was allowed to do. And so things like illegal drinking, which is a complaint type on one, you actually don't see a lot of it because it becomes normal. Other things like mask violations are high in 2020, but in 2022 you see that not popping up because it's a failure of a system. In a way, we all still should be wearing masks and that a system not fail, but us failing to wear a mask is actually a systemic failure. We're thinking about policies. So that is a way in which it builds and becomes that you can ask. Yeah, it's an interesting question. I've been lucky. I think, you know, there's a lot of controversy around social media data, but I think in terms of publicly available data, it does give you a kind of sense of like resident sentiment or like neighborhood sentiment that is worth looking through. Take the proper question. Get data. I think there's there's a lot of ACS data that really captures, you know, different things that are not really captured in the SVI. There's ways to like couple different data like maybe there's resident sentiment from media. There's some ACS variables that you can find as well. There is privately available data, like some of the mobile phone, like mobile phone mobility data, like safe graph and things. I think that could point to things like who's able to leave during COVID and make that tell about like who's a potential worker, things like that. All these are extrapolations, but, you know, as with all data, most of it is made up really about how you argue your case. And what these models really start to tell us are kind of theories or they show us patterns, but they're really not are your issue, right? Like we want to combine it with things like qualitative and have interviews, but they do give us insightful processes that are going. Yeah, citizen pressure. I think, yeah, it's an interesting question. I actually think it's similar to 311, except not public, right? But I actually think it allows for maybe more forms of neighborhood policing or watchdogging kind of where you feel like it's your responsibility to report other people. So then here where largely people do actually talk about the city failing COVID rates or some of the services. So I think to me that feels a little bit more invasive on actually complaining. And I think there is questions of like access as well to app, like how many people have smart phones that can download that. 311, you can actually still pick up the phone and call. So, yeah, I have two part questions. Usually, yeah, I think that's one of like the big but I think having that kind of data also would make 311 not open and accessible a lot of privacy violations. I think looking at space health and that's what I'm mostly interested in because you get a sense of demographics but in facial terms. So you can kind of grasp at a group level, this neighborhood has this number of racial minorities, this number of people that are college educated and get a sense of that from the data, which is what I've captured here. But I think beyond that having individual level markers. Yeah, there is actually data. It's not as complete as some of the other data. And it also takes time to upload, right? Like some of them take days and like missing values. How do you deal with those? Again, those are things you deal with when you're analyzing the data, you can choose to use them and then treat them with values however you theoretically make sense. But that is a, let me put it with you. Yeah, that's a great question. I mean, so there's a whole media politics of like categorization and stuff, right? Like how do we name things like the taxonomy, holding things thing is interesting. They do have what seems like at least a pretty straightforward way of categorizing like you're going to know if it's like illegal parking or like x, y violation. And they have two different layers of recording. So you can put a descriptor, which would vary based on but then there's a category, which I would guess maybe falls in the same, but you're right. I mean, maybe there's some, that's a limitation, but like maybe categorize their because we're dealing with like such massive data, millions of data points, unless they like something like that would impact the result as much. So thank you again for taking the time to get this call. Thanks, everyone. Next week, at the same time, please join us for our next speaker, Dr. Anand. It is lecture on predominant debate on what to drink and for course futures. And thank you.