Hi, my name is Adam Strom. I'm the director of University Archives and Special Collections at Paul V. Galvin Library, the Illinois Institute of Technology in Chicago. And I'm here to talk to you about the Voices of the Holocaust Project. I'll start by giving you a little bit of background on the project. In 1946, Dr. David P. Boeder, who is a psychology professor from Chicago's Illinois Institute of Technology, traveled to Europe to record the stories of Holocaust survivors in their own words. Boeder translated a number of the interviews in the years after they were recorded, and some were published in his book, I Did Not Interview the Dead, and a later series of self-published volumes. But the original wire recordings were lost sometime after Boeder's death in Los Angeles in 1961, and copies of the wire spools are only held by a few repositories around the world, including the Library of Congress. The Voices of the Holocaust Project began in 1998 when Illinois Tech librarians requested a transfer of the recordings from the wire spools at the Library of Congress onto dattape. The original voices site was built from those recordings and appeared in 2000. It included audio of 16 of Boeder's interviews and plain text English language translations for the 70 interviews that Boeder had translated during his life. The 2009 version of the Voices site was a great step forward. It included digital remastering and restoration of the audio, creation of the first original language transcriptions for the 70 interviews translated by Boeder, and translation and transcription work for most of the other 48 interviews never translated by Boeder during his lifetime. All of the interview texts were encoded using the Text Encoding Initiatives XML format, which allowed for synchronization of interview text and audio during playback, and supplemental critical material was added in the form of interview annotations, scholarly commentary, camp and ghetto descriptions, and a glossary. By 2019, however, browser support for the Flash Player that handled audio playback on the site was dwindling, and the site was in need of a visual refresh. So Galvan Library staff started to experiment with some other options for text and audio synchronization and playback, and we developed a script to transform the TEI files to the Web VTT format, but didn't get much further. When we started in on deciding how we were going to migrate this site, obviously one of the major goals was to move on from Flash, but of course that wasn't our only goal. Over the last five years or so, Staff at Galvan Library has been working to streamline the array of technology and software platforms in use, so an emphasis was put on finding a way to migrate the site that would be sustainable in terms of both the initial effort needed to launch the site and the maintenance and upkeep of the site moving forward. Voices also needed a new design. The look and the feel of the site were dated, and it wasn't very user-friendly for anyone who was trying to user-navigate the site on a tablet or a phone. We also wanted to find ways to enhance the site, but this wasn't a move fast and break things kind of situation, and we wanted to be really mindful of the work that had come before ours, both scholarly and technical. So our aim was to make what enhancements we could without sacrificing what was already there in terms of both data and functionality. The collaboration that made the new Voices site possible began in 2019 with an email from Steven Narron, who's the director of the Fortanave video archive for Holocaust testimonies at Yale University. We'd worked with Steven in the past, so when he emailed about the upcoming launch of the Fortanave archive's collection being hosted in a system called Aviary, we were eager to hear more, and Steven's primary interest in this was having boaters' interviews discoverable alongside those of the Fortanave archive, and that excited us too, but we also soon realized that ingesting the boater interviews into Aviary could serve two purposes. Not only could we unite the Fortanave archive's collection of video testimonies with boaters' audio, but we could also use Aviary as the playback platform for the new Voices of the Holocaust site. Aviary is a cloud-based platform for discovery and playback of audio-visual content developed by AVP. Aviary provided the basic playback and text and audio synchronization functionality that we needed, but what sealed the deal for us was that Aviary made it easy to embed media players for Aviary objects on external sites. Aviary also provided things like the ability to add textual annotations, uploading multiple transcripts per file, and searching within transcript text. These were functions that users of our site had come to expect, and the realization that we could replace the Voices site without any loss of features or functionality made Aviary an easy choice. With the generous support of the Fortanave archive, we moved forward with setting up an Aviary account and ingesting the boater interviews into the Aviary system. Since we'd already crafted a script that transformed the TEI records for each interview into the WebVTT format, the majority of the work necessary to get boaters' interviews into Aviary involved the metadata. Aviary accepts CSV files, so Tim Floor, our repository and systems librarian, made a Python script that pulled the relevant metadata from each interview's TEI file and added it as a new line to a CSV. There was manual work that had to be done to reformat some data or add data that wasn't included in the XML, such as the file name of the MP3, with which we'd need to associate each row of metadata. The MP3 audio files and corresponding metadata were all loaded into Aviary, and we temporarily redirected the Voices of the Holocaust URL to the Aviary collection and began to work on the new Voices site itself. Drupal was a really obvious choice as the platform to host the site, as it's the platform on which most university websites are built at Illinois Tech and at Galvin Library. We'd used Islandora 7 on a recent institutional repository migration and decided that the then-newish Islandora 8 update might be worth trying out on the Voices site, partially because of its built-in linked-open data functionality, and also because we hoped that the way that Islandora had applied Drupal's taxonomy feature was going to help us with all of the people, places, and corporate bodies that are part of the Voices of the Holocaust metadata. Since the actual digital objects presented to the user, namely the audio and text, aren't actually stored in Islandora, it's kind of an odd application of the Islandora system. But a test instance of Islandora 8 pretty quickly proved that it was going to be up to the task, and so we started to make plans to ingest our data. Ingesting the Voices metadata largely took place via Drupal 8's migrate tools, using YAML migration routines to add interview metadata, as well as the people, geographic locations, corporate bodies, and other data that make up the site. Luckily, the migrate tools make reverting an ingest easy, since we soon realized that the interdependence between data points meant that we had to run the ingests in a specific order, and we learned that lesson over and over again. The TEI XML files included unique identifiers for each person, camp, ghetto, and geographic location, and provided comprehensive lists that could be used to create data dictionaries to facilitate the linking of data between the interviews and taxonomy terms. It sometimes took a few tries to determine the best means of ingesting a particular set of data, and in some cases we chose to manually create CSV files for certain smaller datasets. We had unique identifiers for everything and structured data for most of it, but as always with any sort of data migration, structured data is only going to get you so far, and as always it seems like some things absolutely needed to be done by hand. Islandora's controlled access terms module uses Drupal's taxonomy feature to create vocabularies with the ability to link to controlled authorities. The module provides 10 different vocabularies for things like people, places, corporate bodies, and languages. Islandora uses Drupal's entity reference field type to use taxonomy terms from these vocabularies as values in other metadata fields. This meant that we could build links between, for example, an interview and its interviewee, and then again between the interviewee and a concentration camp in which he or she had been interned. The previous versions of Voices of the Holocaust had used authority sets in creating geographic and other metadata, but there were no identifiers or URLs to link between a value and the particular authority from which it had been taken. Reconciliation of names and places with authorities was a key feature that we wanted to add to the Voices of the Holocaust site. Each type of data required a different reconciliation technique, and despite our best efforts to avoid it, eventually each record was touched by hand even when an automated reconciliation process was used. The people were relatively easy to get through by hand since most of the interview subjects didn't have records. Camps in ghettos and corporate bodies were also relatively quick hand reconciliation processes, though for some of them different names used for a single camp caused some headaches, and there were times that we had to do a fair amount of research to identify the particular camp referenced by a term or disambiguate between camps with similar names. Geographic locations is where things got pretty daunting. The geographic locations XML file contained more than 600 terms, everything from well-known cities to tiny villages that may no longer exist. Name changes, redrawing of country borders and vernacular names for regions also caused us some headaches. We used a combination of open-refine and Microsoft Excel to reconcile our list, country by country, we went against lists downloaded from GeoNames.org. And at first this process seemed incredibly fruitful, though what we hoped would be a small number of terms that needed reconciliation or disambiguation by hand turned out to be a majority of them. Since many of the terms were linked with the wrong place, for example, there are lots of different places named Washington in the United States that are not either Washington DC or the state of Washington, or terms for cities were linked instead with records for rivers, districts, provinces, you name it. The process of checking and correcting these links if needed for more than 600 locations was time consuming, but it was necessary. And we ended up using this as an opportunity to also link out to authority records for the Getty's thesaurus of geographic names when possible, since that was the authority used for geographic terms in the previous versions of the site. We were able to find matches for the vast majority of our taxonomy terms, though there remained some ambiguity with some of the records and a few total mysteries with a handful of geographic location and concentration camp records. The design process for the site involved the typical concerns and problems that you have with a site of this sort, and I don't think I need to talk about things like responsive design or things like that here, but I wanted to focus on just a few facets of the design and layout process for the voices of the Holocaust site. Anyone who's used Drupal knows that views can be an incredibly useful way to display data, and we use Drupal views to display the embedded metadata player or media player, sorry, to customize interview metadata display and display an interviewee's biographical data on each interview page. We also built customized displays for terms in each taxonomy. I won't go into the nitty-gritty detail on everything that we did, but we repeatedly leveraged the flexibility of views to do things like use contextual filters to dynamically display data based on a value in a URL or rewrite a field's display using data from other fields. The payoff for some of the work felt small based on the time we spent troubleshooting, but being able to display an interview location as the Grand Hotel in Paris, France, rather than just the Grand Hotel, was worth the effort spent. The ability to have a user perform a search in Drupal, click on a result, and have the same search already appear in the Aviary player search interface was huge for us. And when we weren't sure it would be possible, it loomed as a potential headache and a source of confusion for our users. Even after Aviary had added the preloaded search term functionality for embedded players, getting the search terms out of Drupal and into Aviary iframe URLs involved a handful of dead ends and a fair amount of confusion. We ended up creating a custom token in Drupal that would use URL syntax to identify and store a search query if it found one. We then modified the interview page template to build the iframe URL dynamically by combining the Aviary identifier for an interview and the search term provided by that custom token. So now when a user searches for a keyword in Drupal, the Aviary player loads with that term in the search box and occurrences of the term are highlighted in the translation and transcription. This is the sort of thing that users might not notice when it's working, but would be really aggravated by if it were missing and we're really happy we were able to make it work. Throughout the migration process, we tried to remember that we were carrying on someone else's work. The voices that have made up the voices project are not only those of David Boder and the interviewees, but also the librarians, scholars, developers, historians, and others whose hard work and expertise have made the site what it is today. This meant that we wanted to make sure that we were able to include things like the interview commentary, recreate the annotations, and generally adhere to content-based decisions that had been made earlier in the project. In some ways, this made our job easier because it removed some of the decision-making and provided easy answers for some of the questions that we ran up against. For example, we tried to stick with the people, place, camp, and ghetto names as they were defined in the 2009 site as much as possible, and when authority records convinced us to change the primary name of a term, we were sure to include any other versions of the name that had been used as alternative names in the record. The voices the Holocaust project started over 20 years ago, and while we wanted to update the site and make it more modern, we didn't want to erase that over 20 years of history. So we retained almost all of the pages in the about menu on the page. We also wanted to continue to credit those responsible for making the site what it is, even if their contributions happened more than a decade ago. We wanted to preserve the documentation from the old site, so the project notes and description of the site as it existed in 2009 were updated for the Drupal site, but the 2009 version was left unedited on the site as well. It's our hope that preserving as much as we can of this history of the voices of the Holocaust site and project will help us to tell the story about the project and inform tomorrow's decisions about where it goes next. So speaking about where it goes next, in the short term, hopefully by the time you're watching this video, the final tweaks on the site will have been made and the new voices site will have been launched, but the work on the site won't be done with the launch, of course. Our quest to preserve as much as possible from the 2009 voices site has one major component left, and that is the annotation of a defined list of glossary terms and linking taxonomy terms in the interview text to the appropriate term pages in Drupal, and this work is going to continue into the spring and summer of this year. We're excited about the ways that the new voices site will exist in a larger ecosphere of online resources and scholarship, and Aviary's new flock feature is a way to group similar Aviary collections for better discovery across collections, and so one way we are going to leverage this interoperability of the voices site will be in a flock of Holocaust testimonies with collections from the Fortnough Archive and the William Bremen Jewish Heritage Museum. We are eager to do some things that will help increase discoverability across these collections, some things like subject analysis and some data harmonization. We're also looking forward to the possibilities of expanding or enhancing the critical and scholarly features on the site, and there are a handful of interviews actually conducted in Yiddish for which we don't yet have transcriptions, we only have the translations that were done. And some of this work will definitely require scholarly expertise that we certainly do not currently have on staff in the library, but we're hoping that the new voices of the Holocaust site might inspire some new enthusiasm for Bowder's work and maybe the genesis of some new collaborative opportunities moving forward. We're honored to have been a part of telling these stories and advancing David Bowder's work and we're very happy and excited about the new voices of the Holocaust site. So to wrap up, I want to thank the other members of the team who made this new version of the site possible. And I want to thank all of you for checking out this presentation and please visit the site, get in touch, and we hope you like it.


Yeah, it was that and just the urgency that you want to see from a young player in terms of coming off the ball and everything we do, he's just had to learn how to play a little faster. I'm just pushing him. Couraging him. How much does that allow for a sport of every rookie's transition and how much of it? It's been, you know, different forms of it in terms of me, Coach Rable, he's been getting it from a lot of different directions and some of it's been tough love and some of it's been, you know, just, you know, being in the classroom and showing it to him on tape. So he's gotten a little bit of everything. I think one of the biggest hurdles for young players is being able to make that leap in maturity in terms of how you prepare, how you conduct yourself in meetings, all of those things are learned traits and he's in the process of that. We always hear about the receivers, you know, getting to kind of address your, you remember that. Josh, you were just talking last week about Tana who's willing to adjust to the receivers in some situations. Is that maybe more of a two-way streak than we think or is Ryan unique in that way? You know what, I'll be honest with you, these guys, I think they do understand how lucky they are to have a quarterback like Ryan because there's some quarterbacks that don't like certain moves at the top of routes because they can't read it. His whole mantra is, man, get open and I'll get you the ball. So anytime you have a quarterback like that, it's easy to play with. That's the first thing they hear when they walk in this building. There is no secret that they know that if they want to make this football team, they have to make a significant contribution to special teams. And that's something that Raves doesn't, you know, he doesn't hesitate to mention or make sure these guys really understand the importance of it. Do you still remind those guys in the room that special teams are part of what they think because they're going to have to pick up? Listen, if you're not A.J. or Julio and you're sitting in the meeting room with me while special teams going on, you got a problem. So those guys understand that.   You know, so he's just trying to impart the wisdom that was given to him and just kind of passing it on, trying to help shorten his learning curve.


Hey everyone, welcome to Higan Irruthil and we are with a new event, Wander Fantasy. So this is going to run up until June 8th. I assume this type of event will be on a limited time but it recycles every month. So without further delay guys, we need to for this content, we need to be able to get illusion prism so you're going to be farming these and spend these in the shop here. We'll go back to the shop later and discuss what items do you need to prioritize for this content. So Legendary Epic is just going to be your leaderboard and you really need to stick with the two contents that you have down here. This one, the Factory Express, this restarts daily and you could get points daily once you've cleared this one. This is a more simpler content than this one, the Lava Dragon. So Lava Dragon is going to be your longer event. So Factory Express and your Lava Dragon is going to have the same mechanic in which you're going to be clearing them so if you want to get more points, go with the Factory Express. This is actually easier and kind of manageable to get. The Lava Dragon has better rewards though but is harder to get. So I'm just going to be giving you guys a crash course on how to clear this one. So on your left most side, you have their difficulty, so 1, 2, and 3. The difficulty also corresponds to points. So next up is going to be your buffs. The buffs you have to open or unlock as well, you will be needing them in some of the stages that are very hard that you can mix and match. So I'll go first, I'll zero the difficulty here first. So if you notice you have stages here that you have to unlock. So like this one, Super Devil Kings 3 clear, you have to have 12 levels and achieve 8 points to unlock. The points are here, the fictional aura, these are points. As soon as you unlock something, you're going to be getting points. So that is how simple it is. But when you unlock a certain stage, for example, all of them are unlocked but you have to unlock down below here. For level 3, again you'll be requiring certain levels to activate. So clear with 12 difficulty, what that means is you need to have 12 here, 12 here below. So the max that I could have is 9. So definitely I could not clear level 3 at this point. So you have to clear this one, let's say with this one. So for this one you just have to achieve 8 points. So I'm at 2 at this point, I cannot actually clear it again. So you just have to grind this out and meet the criteria. So 8 points here or fictional aura to unlock. For here you have 8 but you need to clear total of 12 point difficulty. So again just clear some of the stages. I would suggest that you clear the top first for level 1 then go down to level 2 as slow as possible because so achieve 5, this is going to be 5. So let's see what I can unlock here as an example. So this one is 5 points. Okay, bear in mind guys that if you use a buff all your difficulty will be reduced to 0. Even if for example you only use one buff, like for example I'll use your 5 difficulty here, if you just use one buff it goes down to 0. So buffs are really for cases wherein you don't have to unlock a stage. If you don't have to unlock stages that have difficulty levels, if it's only points like for example this one you can use them. But if it has a difficulty number then you cannot use buffs definitely. So let us see here, so this is okay. So so far the ones that I have checked, like for example this one, this I have cleared this I think I cleared these two already. So those I can actually use if I actually clear this stage here. So this one doesn't have a qualifier so let's clear this one so that I could increase my points. So again this is going to be a bit grindy because each stage is going to have 5 difficulty levels. So what I do I usually bring my, you know, the lineup that I think is stable enough that it could clear enough survivability. I just do a auto play for this one so that I could clear these. More or less the harder stages you would need to, what do you call this? You would need to manual play most of the difficult stages. But for the easy ones you can actually do a auto play. So also you can deploy at most 8 characters for this point. So now you have 4. You can actually deploy, let's say for example I want to swap in, that's your 5th. So technically you have deployed Sheru there. So again you can actually mix and match depending on the difficulty of the stage. Don't forget that you have that option but if you want to, you know, use one team it's not a problem really. So again stages which don't have any checks, you can get points still from them and because those points again you'll be using to clear the other stages. As you can see I had 2 before this stage. Then after this definitely I will be having 3 already as you will see in a while. This is going to be cleared in a bit. So far I'm actually liking the stages. It has a bit of difficulty mixing and matching your heroes in terms of team build and gameplay.  But again the easier stages are going to be easy, the harder stages you will have to be more diligent in building your teams. So we're now at, I think we're done. Okay so we're back. We have the last boss here again so where was that? So more or less again what I'm saying is it's a slow grind again clearing stages first that don't have checks. You have a aura info points and also making sure that you go down so that you can get more difficulty points as well to clear the harder stages below. So just going to be going back so aura added as you can see from 2 to 3. So again this is going to be a slow grind guys. This one is I think the next that I can unlock with 5 points so that is it. So again if you don't have a deep enough lineup you can actually go with the factory express and try to ensure way here and get points as you go along. This one has a lot more points as you can see the rewards here are better 2000 points and as you go you actually have a 5 star play selector here and there are a lot of goodies here. So again make sure that you try to clear this again step by step clear stages without a check mark because those will generate you points. So please make sure that you clear difficulties one going down 1, 2, 3 so that difficulty points may be credited so that you can clear the harder stages here with this one 12 and 10 and 8 points here. Let's go back to the fantasy shop first. Okay best buys for the fantasy shop you will have brilliant sigil, inspiration sigil, the top row and these two. So you have reagent, pure potion, muses guide so these six you have the finish they are very very scarce the rest once you have extra be my guest on what you want to purchase but at this point make sure that you get the top six here first six. So that is it guys thank you very much for staying this far also comment down in comment section take care stay safe this is The Warden and I'm out of here.


Good morning, John, so I assume that you, like everyone else on the internet, has seen Mark Rober's amazing new video in which he builds a bait package that explodes glitter on people when they steal it. I don't want to take away from the amazing engineering here. It's very good, but the reason I've watched this video five times isn't that, like, amazing glitter swirl, and it isn't the well-engineered fart sprayer. It's the part where the thieves don't look or act the way I thought that they would, and... Look, I'm just being honest. I don't actually know what I thought they were gonna look like, maybe because I dehumanized what a thief was in my head, but that challenge to my expectation, whatever it was, immediately changed the way I was thinking about thieves, and I went from, like, maybe a little bit of anger and also pity to more curiosity and also still anger. Because getting offended that, like, your theft ended in a prank? Sh**. Look at that.  Yeah, come on. So I did a bunch of research because that's what I do, and some of that research involved talking to package thieves, which I managed to do. And here, in the voice of actors, not the people themselves, are some of the things that I heard from those people. All of my friends were doing it, and I didn't have that many friends, and I didn't know how to make new ones. There's some satisfaction in just getting away with something. I had a debatable need to steal when I was in my active addiction. I could sell or trade the stuff I had stolen. You don't ever know what people might have ordered. Could be a USB cable, or it could be an iPhone. It's like anything addictive, and this is better than my other addictions. I never really thought about the consequences, but I knew that even if someone called the cops, I'm young, charming, white, and I have a clean record. So the consequences wouldn't be too bad. We were just having fun until we put together the fact that this was almost certainly a toy meant for a child to open on Christmas morning. It stopped being fun then, and we took it back. All these perspectives were super helpful for me in understanding how theft happens without the need for theft, and that doesn't mean excusing it, the better understanding it. At the same time, like, I felt very out of my depth. Like, I'm not a psychologist. But Dr. Robert Tominsky is, and he wrote a book called The Psychology of Theft and Loss, and I just called him off my job. Most theft is not because people need it. There's a small category that's opportunistic based on, like, social and economic deprivation. That's actually pretty small. They felt like there was strength in getting away with it. That they can fool people and dupe them and in the rush. And that's also true for shoplifters. It was also really interesting to me that this was often not a solitary activity. And that's like a defense against experiencing the guilt then. Like, if you are prone to some guilt, then somehow you get to divide and not let quite as badly. But more than that, what I came away from this thinking is that theft, like all sociological enterprise, is super complicated. I did a lot of research on theft this week, and one of the most fascinating studies I came across was from Dr. Dan Ariely. He did an experiment where a whole bunch of people came into a classroom and they had to answer math problems. And for everyone they got right, they got paid. But there was one actor in the group, and the actor stood up 30 seconds into the test, walked to the front, said that they got all of the questions right, took the money, and left. So, did the existence of an obvious cheater make it more or less likely that the other students would cheat? Well, it depended on what sweater the cheater was wearing. If they were wearing a sweater from the same university that all the rest of the students were from, cheating increased. But if they were wearing a sweater from a rival university, cheating decreased. I came away from that research thinking the same thing I thought about Mark Rober's video. Genius design and theft is a lot more complicated than I wanted it to be. John, I'll see you on Tuesday. Thanks to my voice actors, to Dr. Tominski, to Andrew Huang for the music, and to all the people who shared their experiences of package theft with me. And a reminder, John isn't going to have a video next Tuesday because that day is Christmas.


The first one is, put up a fight. The Bible says in here, that when the evil spirit goes out of a man, he goes to dry places, dry places. And interestingly enough, in the dry place, he could not find rest. He found rest in a clean place, but he couldn't find a rest in a dry place. One of the reasons he couldn't find a rest in a dry place, I believe, is because just because that place was dry, that place was not willing to put up with demons living there. Demons are gonna find rest anywhere they are not resisted. Anywhere they are not fought against. Demons will find rest anywhere they are not fought against. Anywhere they are welcomed, anywhere they are assisted, they will find rest there.  They go from the service and they say, I don't wanna drink no more. I'm not gonna look at pornography no more. They take that pack of cigarettes and they dump it on the way home.  It's gonna take me time to rebuild my feelings and my consciousness and my mind, but I'm not gonna go in that lifestyle no more. A person who used a razor to relieve their pain, throw that away and say, you know what, Jesus' blood is enough. I'm not gonna do that again. And they felt that power, but this is what happens. Three, four days later, they don't feel that again. On the opposite, what they will begin to feel again is old thoughts coming back. Old feelings flocking at them and a person begins to feel dry emotionally. Have you been dry? You know what I'm talking about. Well, you feel like, I don't feel what I used to feel at Wednesday night. That presence, that hype, that excitement, that drive, that good vibes, they left. And it feels like I actually never got free in the first place because same feelings are crouching back. And the lie of the enemy is this, I've never been free in the first place. All of that was a good pap talk and it was Vlad and others manipulating my emotions. That's exactly what happened to Israel. Israel got free from Egypt, walked with boldness from Egypt for three days. And three days later, Pharaoh assembled his chariots and did not go meet Israelites to give them a farewell gift. He went back to take them back to slavery and when Israel saw that, dryness came in inside. And what did they say?  All of that was fake and we knew it. They experienced exactly the same thing we all experienced. And God tells Israelites, do not be afraid and don't surrender. God says, I want you to keep fighting. I want you to keep going through and I will give you a victory. Do not give up in your dry place after you get free. Put up a fight. Satan is not as persistent as he claims to be. Satan is not as persistent as he appears to be. The Bible says resist him and he will flee. That means he has a very small persistence level. He's very impatient devil and if you stand your ground in your dry place, he will leave you alone.


Hello everyone, welcome back to the next lecture in the course. Today, we are going to start with the last part of the course that is we are going to see few applications of remote sensing. So, actually applications of remote sensing is that itself is like extremely wide to discuss because whenever we want to apply remote sensing techniques to some applications we need to have some sort of domain knowledge in that particular field so that we can effectively use remote sensing data sets. So, dealing with applications normally will involve discussing the domain knowledge plus how remote sensing will help. So, we have to discuss both the things which is like a huge task actually because there are plenty of different fields in which remote sensing is applied and it will be almost impossible to cover all applications and with the essential background knowledge required for us to apply the remote sensing data. So, what we will see in this course is just like few basic applications in different fields like we will not go much deeper into it. This will give you all a good flavor of different ways in which remote sensing can be applied. We will broadly touch different different data sets optical, active micro view, passive micro view and so on with which you will be able to learn further or there will be like lot of references I will be giving along with this particular lecture itself like within the if you note within the video itself there will be like lot of references from which the materials are taken or the references which will provide you like a good knowledge about the subject those things will be there. So, those who are interested can look at them and gain more knowledge about that particular field. So, today in the first applications part we are going to start with land use, land cover monitoring and change reduction. Actually land use, land cover falls under category of analysis remote sensing image analysis like after we acquire remote sensing data we will do certain analysis over it or certain operations over it which will help us to categorize land use, land cover and also to understand or identify changes in the land ok. In this lecture we are not going to discuss those techniques because that itself again will be very elaborate but I will just briefly tell you what land use land cover is, what all the basic base in which we can get it from remote sensing data sets and some fields or some applications in which these land use land cover data sets are directly applied even without having much domain knowledge we will be able to appreciate the way such examples and we are going to discuss in the lecture. First of all what is land use and land cover, land cover indicates the physical land type that is present on the surface say vegetation, built up area, water body, these are all physical features that are present on the land. So, identifying them using remote sensing data sets we call it as land cover mapping whereas land use means how we humans are putting that particular land puzzle to use. Say vegetation is a broad class, within that vegetation if someone is using that particular puzzle of land for cropland then that classifies as land use ok there is a piece of land containing vegetation as land cover but humans are using it as cropland. Again a built up area, so built up area is just buildings that is all but for what use that built up area is being put whether it is for residential purposes, for commercial purposes, industrial needs, plenty of different use we can put, so that is land use. So, mapping and monitoring these two like land use and land cover are essentially carried out using remote sensing data sets. In olden days people used to do ground based survey to collect such information but after the advent of remote sensing actually this was one of the earliest applications of remote sensing identifying like land use land cover what people called us like classification ok. So, normally land use and land cover will go together we would not be exactly splitting them like strictly speaking ok this is a land cover map, this is a land use map normally we would not do that both there will be like a slight mix whenever we identify something as vegetation we will always identify whether it is cropland or forest or plantation, shopland and so on. Similarly, whenever we identify a built up we will identify whether it is urban, rural, residential, commercial and so on to some extent. So, both of them go together normally and for various purposes we will be in need of such information. So, what is given there in this slide is an example for a global land cover map obtained from modus sensor ok. So, this is produced yearly. So, here you can see there are like 17 classes together say blue color parcels or water and there are like different classes of forest, grasslands, wetlands, croplands and so on. So, you can see here it is for if you talk about India there is forest near the western guards majority portion is kind of like cropland according to this land cover map and so on. So, this is just one example for land cover mapping and in this lecture we will see some users of these sort of maps. So, why LULC information is necessary? So, land use land cover data is needed both for scientific as well as administrative purposes. Say for example, someone needs to understand how the hydrological nature of region has changed because of land cover change. Say for example, there may be a kind of like area people might have built up like huge residential apartments, they might have changed the characteristic of land cover or some forest have made might have been cleared to introduce croplands all these things might have happened. When such thing happens normally there will be some change in other processes like hydrological processes, ecological processes and so on. If we want to understand them what is the effect of change in the water cycle in the region due to change in land use land cover change for that purpose we need. Okay, this was like the older map and this is the new map when the land cover changed like this how what was the change in hydrology correspondingly. So, these are like scientific investigations also for administrative purposes say someone wants to develop like a new airport let us say like our country is growing really fast and many airports are coming up and actually many different civil engineering projects are being taken up. When those things happen when you want to do like a large scale infrastructure development we should take a look at LULC map identify okay this is like a land that is empty it is not being used for anything or whether there is any other plan for this particular parcel of land. So, it is like a complex process people will not just quickly go okay this is the land I am going to start constituting an airport it is not going to happen like that. So, for identifying where to develop and if we develop something whether it will be useful to public say someone wants to develop like a new airport for Mumbai city it is already being taken let us say. So, the airport should be not very far from the city it should not be like 2 to 3 hours drive from the city it should be close to the extent possible. So, there are like plenty of different planning has to go through when such large scale infrastructure projects comes up. So, for such things you need or some if a government wants to develop like a satellite township definitely land use land cover map will be need up. So, for all these purposes LULC map is needed in addition to this due to normal human activity LULC pattern changes over time in response to our own needs and demands. So, when this happens it will have its own impact in the natural cycles like ecological processes as I told like water cycle even like climate and so on. So, understanding the effect of this change also is really necessary for sustainable development. We cannot like go on doing something which is causing lot of ecological damage. So, when this study happens the land use changed from the last 20 years like this because of this such damage has occurred to the environment. So, can we further proceed? So, such kind of decisions it is both like scientific as well as administrative. So, not only LULC map at this instant, but also the change that occurred in the last say 2 decades or 3 decades is of vital need for both scientific as well as administrative purposes. What has changed reduction? We just gone through about like LULC. So, what exactly changed reduction is very simple identifying what has changed between 2 time instances or different time instances say in the last 30 years what has happened to the city. So, we can do kind of like a multi temporal analysis say every year what has happened or every 5 years what has happened. Identifying such things is known as change reduction simply put. So, as I told earlier the changes that are happening in the land use land cover is again going to impact our surroundings, our environment and whatever natural things that we are dependent upon. So, this is also really important and say like what like some of the clues that we may get about like change reduction. Say when there is kind of like a large scale like urban expansion we may see like whenever you are seeing from the top especially from satellites or airborne you will be seeing like a sudden large new kind of patch that is growing up or when a forest is cleared for like agricultural use that will appear differently when you look from satellite. So, all these things are kind of like clues for identifying ok some changes happened. So, even with like kind of visually looking satellite images we will be able to identify such changes. How remote sensing is useful in getting this LULC information or for change reduction purposes? In olden days like in the early days of remote sensing people used to do what is known as visual interpretation like even before the advent of satellite remote sensing aerial photographs were being taken like when we discussed about different platforms I briefly told about like taking photographs from aircrafts kites or PGNs and so on. So, aircraft based photographies were in wide use in earlier days in the technology developed rapidly during the world war times and people realize the civilian applications of such photographs. So, so many developed countries had like a planned program of acquiring aerial photographs. So, people will use to look at those photographs and identify what feature is there. So, that is known as photo interpretation or visual interpretation of aerial photographs. We have to visually look at it and identify ok this particular say this is like a photo normally it will be like a square ok this is like a patch of land with the crops in it. So, this is crop land this is like a township. So, this is built up area maybe some sort of semi urban. So, here there is like a pond which is like a water body. So, identifying this visually urban water. So, whatever is present in the photograph identifying them visually is called visual image interpretation or photo interpretation. Even after the advent of satellite images people were still doing it for quiet sometime. So, normally in satellite images I told you especially in the olden eras we will be getting like each band will consist of like one image whether it is like hard copy printed maps or something ok. So, each band you will get one one data if it is computer based analysis like you will see an image in a big screen and identify it. So, it is still visual image interpretation, but identifying it on a screen that was also done like just displaying the image on the screen whatever screen like TV screen or computer delineating there using a some sort of input device or during the days in which we were receiving paper based satellite images like hard copy satellite images will be delivered in olden days. So, when that happens normally people will order for what is known as like a true color composite or a standard false color composite. So, what is true color and standard false color? True color is say satellite can take data sensors can take data in RGB, NIR bands all these things we know. So, whenever you get like a printed map on like a particular type of like a paper or something the print not a map is a satellite image. When you get like a satellite image printed on a sheet we would not be getting just one band separately. So, normally especially like for a Indian remote sensing satellites they used to give us kind of like different bands combined together say red band, green band, blue band. When you want to display color images in some sort of like display medium either on a paper or on a screen we will mix it in terms of its corresponding colors. Say in display pattern there will be again red, green, blue colors when these three colors mix you get an image color image display that is the nature how normal display systems works. So, when you display a red band image correspondingly in the red color of the display system like a bright red color will appear like bright red in the display like high Dn values in red band will appear bright red color. Low values in low Dn values in red band will appear like very light red in color like that. Similarly, green band from satellite data assigned to green color in display, blue band in satellite assigned to blue color in display. So, such corresponding mapping between the data and your display system we call this as true color composite. But since blue band undergoes lot of attenuation like atmosphere scattering normally blue may not be printed people will replace blue with NAR band okay. So, satellite data it will be red, green NAR in display system NAR will be mapped to red, red will be mapped to green, green will be mapped to blue. So, you change this see this particular pattern of colors. So, this is known as like a false color composite. So, the true colors which we see are not there. So, what is actually appearing red that is not red that is the signals in the NAR band. Similarly, what is appearing as green it is not green but what is due to the signal in red actually that is red color but we are seeing it as green. So, this kind of mix and matching like or displaying some other color for a data in some other band is known as like false color composite. So, whenever like hard copy satellite images were printed and delivered to users normally users can order either like a true color or like a false color composite. Say people will order like a false color composite they will have a look at it okay. There will be what is known as like a light table and there will be a huge tables attached with lights underneath. So, the satellite image will be spread across that particular table then transparent tracing sheets will be placed over the image and they will be like bounded properly and expert image interpreters will manually trace whatever different features are by looking at like the false color composite data and by using what are known as visual interpretation clues I write here. So, visual interpretation clues people will be able to identify what is there to like a very good extent. So, they will manually trace such features and that thing will be considered as like LULC map then it will be transformed to like a proper map. So, that was how LULC map were prepared in the olden days. So, if you want to do like a change deduction analysis okay this is like the LULC map produced using this year this is the LULC map maybe in the next 5 years compare them and what to say overlay them and identify what are the changes. So, such things were done manually using what is known as photo interpretation or image interpretation. So, normally there is like a slight difference between photograph and image if you look at like some of the textbooks they will define as if you use the detector itself as a storage medium you call it as photograph say our film what we used in our olden day camera that is the one which deducts the incoming light at the same time that will be used to store the image also right when you take the film you will develop it. So, that contains the actual information you call that as kind of like photographs in satellite image what you call it as an image it will be stored somewhere like digitally or something and then it will be transformed to the ground and you will take like separate printouts. So, the detector is different system what displays the image is kind completely like different system. So, there are like minor difference especially some textbooks will have this difference and also it is convention to call aerial photographs and satellite images we will not normally call satellite photographs nowadays most of we call them as like images. So, that is like the minor difference. So, this was done in the olden days at present especially in the last say 2 decades or so digital classification techniques have developed. So, digital means rather than using like a hard copy satellite images people moved on to digital images which can be directly displayed on a computer. So, digital classification techniques emerged or developed using by looking at an image we will be able to extract information using computer based analysis using either spectral clues or spatial clues spectral clues means say each particular land cover category may have like one spectral reflectance pattern. If you have like multi band image you can just try to extract this spectral reflectance pattern and maybe match with your ground or reference information such thing can be done or you can again or you can like manually digitize few pixels telling that this is like water body this is like built up say maybe like some 100 pixels from like a big image if we classify this and then tell the computer I have identified you few pixels then you classify the rest of the image. These are digital classification techniques which is widely used now. So, from satellite images by normal either by looking at it visually or by using digital classification techniques it will be possible to get LUL's information and also to do change reduction. So, this is like a schematic of how remote sensing is helpful in getting LUL's information. So, there are like plenty of different satellites different modes of platforms are available. So, we have different landscapes each having its own spectral and spatial characteristics. So, by we will normally acquire the image over the landscape we will have images. So, we will get the image downloaded we will develop what are known as classification schemes or decision rules. So, by combining the image and by combining the spectral and spatial characteristics of different landscapes inputting them as inputting them into classification schemes we will be able to get like a LULC map. So, we will use not only satellite images we can use digital elevation models we can use other existing maps and so on. So, these are called like ancillary data sets apart from satellite images if you use something we will call it as ancillary data sets. So, we get LULC map. So, let us say I have LULC map for say year 2000 and now I have LULC map for year 2020. If I compare them and produce like a change map that is what is depicted here. So, temporal change analysis dynamic change this can be done. Otherwise, so this is called post classification change reduction. Post classification change reduction that is we do LULC mapping identify the land use land cover features there then after producing two maps we will do change reduction and hence this is called post classification change reduction. But this need not be done always like this and some people even do not prefer this rather than this if our goal is to only to do change reduction we can directly do from here. Just by comparing multi temporal images and by applying this classification schemes to those images like without producing this we can use what are known as certain change reduction schemes and we can directly classify this. So, we can either create LULC map then perform change reduction analysis or directly by using multi temporal images and change reduction algorithms we can directly come up with a change map. So, both of them are possible. So, this is like the brief or broad schematic of how LULC information or change reduction information is obtained from remote sensing datasets. So, there are plenty of or variety of algorithms and methods available to do land use land cover classification and the different using different criteria we can classify them. So, we will just broadly see how the different algorithms for LULC mapping can be classified in what all the different ways it can be classified. We would not be going into details of seeing those algorithms but we will see what different classes of algorithms exist for our basic knowledge. So, first thing is whether we are going to give any sort of like inputs to the computer during like what is known as like a training sample to the computer or not based on that we can classify the algorithm. So, whether training samples are used or not that is like the first category of classification that is I told you like we will display an image in the computer and then we will identify few pixels for each class. Say this is water, this is built up area, this is cropland like that we will identify few pixels and then we will ask the computer algorithm to work on the rest of the image to classify it entirely. Say that can be like a thousands of pixels in the image but we will identify one maybe like a few tens of pixels for each class and rest of the pixels will be classified by the algorithm. Such classification procedure are called supervised classification approach that is as humans we are supervising, we are telling this particular pixel this group of pixels is built up. So, similar pixels should be categorized as built up by the algorithm. So, we are training or we are inputting something to the computer. Then on the other hand sometimes we may not be knowing what is there but we may want some preliminary information. So, we may ask the computer to identify spectrally similar features like I told you we will be looking at like the spectral characteristics basically we will spectral or spatial characteristics whatever say this is similar how spectrally similar these features are that can be like say 100 pixels comprising of cropland. So, most likely the spectral signature will be closer to each other they will not be exactly matching but they will be closer to each other. So, we will try to identify such pixels which have similar spectral characteristics or maybe spatial characteristics. So, identifying those pixels the computer will tell these are all the groupings I can make say these are this is one cluster of pixels this is second cluster of pixels it will tell us. So, what we can do is we can have a look at such cluster of pixels and identify and then maybe go to ground and identify okay this is like one cluster the cluster in the ground is actually like a cropland. So, this entire cluster must be cropland this cluster is actually like we can like have a corresponding look in the image as well as the ground information right doing such thing like getting the clusters as output first from the computer then assigning actual clusters to it that procedure is called unsupervised classification because the computer is actually doing the clustering thing as to us it will not classify computer will not tell this is urban or this is cropland it will just tell maybe like the basic algorithms maybe nowadays AI based algorithms with our input it can function many things. But still older classical algorithms will be telling okay these are all the clusters or group of pixels it is up to us again to further define what those group represent on the ground. So, this is called unsupervised classification. Then next major category of class if I erase whether we do one pixel one class or sub pixel classification that is let us say there is like some image is there with 100 meter by 100 meter pixel resolution. But so each pixel corresponds to 100 meter by 100 meter so there can be many different features present within that particular pixel that can be a small point that can be like a building and many so and so on. So, are we telling okay one pixel corresponds to only one class say what is their present majority if you assign that particular thing we call it as like per pixel classifier or sometimes we will be also interested to know what are all different features present within that pixel say okay this pixel contains 50% cropland 20% built up area remaining thing as like say water body if we identify such sub pixel level classification that is called sub pixel classifier there are a few algorithms even to do that. Then like the next major classification will be whether are we using only like the spectral information or spatial information or both. So, spectral information means we will just take an image derive the spectral characteristics of the data and just by looking at the spectral characteristics of data we will identify what is what so those sort of classifiers are spectral classifiers. But with the advent of very high spatial resolution data like less than 5 meter spatial resolution if you have a look at those data only by looking at the spectral reflectance curve we may not be able to get actually what is there present on the land we may also need to have a look at the spatial information present like say when you look at like 50 meter by 50 meter data 50 meter pixel data a building will be like a small part of the 50 meter pixel. But when you have a look at say 2 meter data so a building will be the same building will now be occupying several pixels maybe like 10 pixel by 10 pixel or something. So, the building can be say 20 meter by 20 meter can be right. So, when you have a look at like a medium resolution data a land cover feature will be present entirely present within the pixel but now it is now spread across multiple pixels. So, we have to look at have a look at the spatial patterns. So, such algorithms also develop they are known as contextual classifiers or spatial classifiers. So, whether are we using spatial information or not in our classification using that we can classify our algorithms as spectral classifiers or contextual classifiers or hybrid. It can use both spectral information as well as spatial information. So, these are some examples of different classification procedures that we have these are just like a very brief introduction to the topic. The topic is very wide like image classification pattern identification is very wide but we have just discussed very briefly about the different classifiers. So, with this we end this particular lecture. Thank you very much.


So our speaker for today Will with Zara Raman and she will be talking to us about communities marginalized communities and their representation in data And more specifically, she will be telling us whether it is preferable for marginalized communities and in which cases to be Reflected to be represented and institutional data and in which cases this could be harmful. So Privacy and anonymity are preferred. So, please let us give Zara a warm round of applause All right. Thank you all for coming on this lovely warm day. And so I'm going to be talking about The representation of marginalized communities in data So this is based on some work that I've been doing with the engine room on the theme of responsible data and Some work with tactical tech on data and discrimination So to start that work, we I started thinking like what is discrimination? It's when groups of people are treated in unfair or biased way based on some arbitrary Category like their skin color or their gender or any number of other things So how can data? facilitate or mitigate discrimination in all its different forms and So I started looking at The people who are living on the margins of society So that's what I mean by marginalized communities people who don't often get their needs seen to people who are excluded from what's considered to be regular Society and I started by doing this by looking at the anti-discrimination movement And what they're pushing for with regards to data and marginalized communities And so one thing that seems to be a theme among many Anti-discrimination movement is the right to be counted So the right for these communities to be reflected in data sets. So why count and so got a couple of examples of Yeah, marginalized communities or communities that should be or need to be reflected in data for their needs to be met so this is a slide a screenshot from UNICEF's annual report about The state of the world's children and in it they really push for better data collection of Around children with disabilities living in developing countries because as they say without this data It's impossible to know how these children are being treated Whether they get access to the services they need whether there are any particularly harmful policies or any particularly beneficial Policies that really help them get what they need And they found for example that children with disabilities are much more likely to drop out school a lot earlier And the data can help Yeah, understand why this happens and try and mitigate this in the future There is a problem with comparability of data in regards to different abilities and Disabilities because the environment that people live in has a huge effect on The effect that their disabilities have on their lives for example Someone with limited sight who has access to a guide dog has much more independence and can lead a much more Yeah, much more fulfilling like one might say life than someone who lives on their own and doesn't have access to a guide dog And doesn't have anything that they need to live their lives So this means that even if you do collect data across different countries It's very difficult to compare what happens in one country like the quality of life from different countries Yeah, without lots of different caveats, but it still might be interesting to see yeah different patterns And this is an example from That you might have come across there was an article about it on the front page of Hacker News a couple of weeks ago Until just over two weeks ago. This messy thing was the border between India and Bangladesh So 51,000 people live in these bits marked in red known as enclaves Which are basically portions of a state that are entirely surrounded by the territory of another state And this also was the home to the world's only third order enclave So this was a piece of Bangladesh. No a piece of India within Bangladesh within India within Bangladesh Which is ridiculous So but for the 51,000 people living within all of those I think 162 different enclaves They didn't have access to any basic human rights. They weren't represented in any administrative data sets They didn't have citizenship. It meant that things like if they wanted to go to a market that was outside of their Enclave they could have lots of problems. They had no Access to water no electricity. They couldn't travel all sorts of things and reportedly more than 75% Of the people living in Bangladeshi enclaves have spent time in prison for invalid travel Because in theory someone who lived in an enclave who wanted to travel outside the enclave Would need a visa to enter the foreign country But to get that visa they would need to travel to a major city in their country which was Impossible for them to do without first going through the foreign country So this is an example of yeah a Population who really really really needed to be represented in some kind of data set and have their needs seen to And actually just on the on the 31st of July Indian Bangladesh exchanged various parcels of land and Hopefully cleaned up the border a little bit which was very momentous for these people and in preparation for that one of the First things that was done was Indian and Bangladeshi officials so a team of like 75 different teams of we each of one Indian official one Bangladeshi official Conducted us a field survey and they went around and collected data on the enclave residence to in the first two weeks of July And each of the enclave residence was allowed to choose Citizenship of either nation so by the time the official swap took place They'd all decided upon what citizenship they could claim and this went into effect on the 1st of August So it's not I mean the problem isn't quite solved as simply as that because obviously if for example families had chosen different Citizenships then now they'll get split up But it is a first step towards getting these people the yeah The access to basic rights that they need like schools hospitals electricity water that kind of thing And this is another example of Really missing data that was very very crucial and this is a campaign or a project from the Guardian called the counted So in the US There's no comprehensive record of people killed by law enforcement. It's up to the states whether they collect this or not And this lack of really basic data has been very glaring among the Black Lives Matter movement for example So at the moment the FBI run a voluntary program So where law enforcement agencies have the choice whether or not to submit Their annual count of what they call justifiable homicides To this centralized database so to to counter this the Guardian together with his readers are Basically collecting the the most comprehensive data set of people killed by law enforcement agencies in the US So they're asking people to submit Reports when someone is killed or if they know of someone who's killed in the past and then they verify it and together They're yeah, they're getting this data set so this is a really really crucial piece of data that's been missing until now of People who've been being killed and there's been no way to put all this data together and thus no no way of knowing if There's any patterns like if it if they happen to be of a certain demographic for example This is another nice one that is also quite new called gender balance So another missing data set is that there's no data set on The gender balance of different Parliaments across the world so you can't answer for example What country has the highest proportion of women in Parliament or do women vote differently on different issues? And when did different when did women come into power in different countries as and you know And did this make a difference in the way that the country is run? So this is a tool called gender balance created by developers at my society And it's aiming to collect a database of the gender balance of every Parliament in the entire world and it's doing this by a kind of tinder like games You can sign up say what language and what what kind of country you're most familiar with and then you swipe right and left If it's a man or a woman and then they're crowd sourcing this data verifying it And then they'll build up this data set and release it as open data So basically the reasons for counting For making sure that communities are represented is so that you can notice different patterns You can see if there's needs or gaps in the services that are being offered So that there's more accurate dissemination of public funds like in lots of countries It depends very much on population as to where money gets given And of course like in our field a lot and the stuff I've been working on it strength is Advocacy hugely to be able to point to kind of concrete numbers and say this many people Can't get access to a certain basic human right But then the other side of it why not count This is an example from World War two and it was quite recently revealed after decades of denial That during World War two the US Census Bureau provided the secret service with data from the 1940 census So that they could identify people of Japanese ancestry And in their words to to assist in the roundup of Japanese and Americans for imprisonment in internment camps in California and six other states during the Second World War So yeah, so this enabled them to collect and to round up and to imprison these people very very quickly and Efficiently so to speak and researchers who studied the way that this happened the relationship between this the Census Bureau and the Secret Service Say that the speed with which the data was released So it was released just seven days after it was requested Leads them to think that this that wasn't the first time that that has happened, but it is the first time that it has been publicly admitted Another example from Bangladesh So the Rohingya people are one of the world's most persecuted minorities in Myanmar or Burma. They're stateless They live in Myanmar where they're regarded as kind of illegal Bengalis, but in Bangladesh. They're regarded as Yeah, people they're not regarded as as Bangladeshi really so they have most of the 1.3 million people have no citizenship and Yeah, they also lack very basic human rights. They live in camps in yeah in all sorts of really terrible situations And so recently a couple of weeks ago the Bangladeshi government announced that they'd be holding a census of the hundreds of thousands of Undocumented Rohingya in Bangladesh who were who entered Bangladesh seeking refuge from persecution in Myanmar But the Rohingya people within Bangladesh are also really really unpopular And lots of groups watching over the human rights of the Rohingya are very Skeptical as to how this data will be used So some officials have said that the people are really reluctant to give their data for fear that they'll be deported So this will be used as Yeah, by the Bangladeshi government to say we can't take any more people We need to send you back and then they'll go back to Myanmar and they'll face the same thing another thing that is Happening among the census when they're collecting the data They've often been asking you have the choice either of identifying yourself as Bengali or not at all So they're completely trying to erase the Rohingya identity in this census data And yeah, I mean they say that that the term implies a lot more than just the ethnicity that it Implies a claim to land and that's their reasoning for raising this data But for the people involved, it's a very important part of their identity obviously This actually isn't the first time that the Rohingya have faced Discrimination within data sets. So from the other side in Myanmar last year They conducted their first census in decades and they included questions on ethnicity in the census and some human rights groups said that this was overly sensitive at that time and they shouldn't be including those questions, but they went ahead anyway And it turns out that there were a few census workers During the census who would go to households ask them their ethnicity and if they said Rohingya They would turn around and go away So this is completely erasing the the population of Rohingya from this data set So clearly the the issue of race and ethnicity can be very problematic when it comes to marginalised communities But it's not just ethnicity data that can cause problems So this is an example from I think 2012 where in the Netherlands there were There was a proposal to make registration of sex workers mandatory and lots of lots of people within the sex worker community Yeah, campaigned against registering because they found that would be really stigmatising for them And it would violate their right to privacy. There's also Very little evidence that points that so yeah, there's very little evidence that shows that Registration actually helps fight human trafficking, which is the reason that it was supposedly being done in the first place Yeah, so there's there's many other examples of why you really shouldn't collect sensitive data But the kind of middle ground that I seem to have come across between the anti-discrimination movement and the privacy movement in a way is A census that's done in a sensitive way But this also as we've mentioned can be a very political process so this is a map I found of Showing countries where ethnicity or race of people was counted or was included or enumerated and at least one sentence Census since 1991 And it's quite interesting to see the countries that really don't collect that data for example in France It's forbidden by law to collect any statistics based on racial or ethnic origin but in contrast there are other things that happen in France that Yeah, kind of go against this though until recently it was very usual to put your photos on your CV So even though you didn't have to state what your race or your what your ethnicity was you could see that and there's lots of research That was done that proved that there were many discriminatory hiring practices based on that And also I mean social scientists have devised kind of ways to get around this data So now if for example, they just you they just look at the names of people to kind of guess their ethnicity So it's not like the the data isn't really being collected It's more that it's being collected in a kind of ad hoc and an inaccurate way Obviously Germany knows the the horrors that can happen with collecting ethnicity data more than many countries The Nazis use census data to help track Jews and other minorities and then obviously in East Germany the stars a secret police Maintained really really comprehensive files on citizens and interestingly in the 1980s attempts at introducing a census in West Germany. I'm sure many of you know this but for those who don't Yes, parked massive protests and people refused to actually answer the census and it led to a boycott It meant the constitutional court stopped the census in 1983 and then required a revision of the process So a more sensitive data collection process. This is where the term information illa selves which they mung or informational self-determination was first used which is kind of similar to the right to privacy But it gives the individual the right to determine in principle the disclosure and use of their own data And the only limitation to this is when it's in an overriding case of of public interest so carrying out a census as we've As we've established is hugely hugely sensitive, but it is kind of crucial when you're Trying to give people the things that they need. So who do we trust to carry out the census in the UK? unfortunately, it's Lockheed Martin, it's America's largest arms manufacturer who carries out the census And this is the same in the UK the US and in Canada. It's It's an arms manufacturer who carries out this really really basic role. So they make trident nuclear missiles cluster bombs fighter jets They're really into aerospace and defense. They get 80% of their work comes from the US Defense Department And obviously when this was so this has been going on for a while and when it happened there was Kind of an outcry justifiably, but actually not surprisingly small In the UK so this is where I come from. So this is like kind of what I've been looking at with my research And the UK government released a privacy impact assessment where they said, you know It's all the data is owned by the office of national statistics statistics in the UK But it but Lockheed Martin were the ones who designed the system and who implemented the system Yeah, and the you know, they said it's a It's a criminal offense to release any of the census data, but then you know if we look to history We see some examples of how that hasn't always stood up So yeah, Lockheed Martin, they've carried out the census in the UK 2000 2010 in the UK in the US 2010 in the UK and these and then in Canada as well So concerned citizens in the UK for example submitted a number of freedom of information requests through the Freedom of Information portal, what do they know and that's been really interesting to read through But there's not actually that much more information that's given And it's quite hard to follow So Looking thinking about choice like what can people what can citizens do if they don't want to be giving their data to this Company that they you know whose morals they might not agree with. I mean, I'm obviously in no No position to make any judgment on what's actually done with the data, but just thinking about what? role this plays So there were some really interesting responses from anti-war campaigners in the UK Because not carrying not filling in the census meant that you faced a fine of a thousand pounds So these campaigners didn't want to give money to the UK government the Green Party also decided not not to support a boycott of the census because The the the results of the census are used to allocate public funds So if you don't submit your data, you don't you might not get your needs met or get represented in a way that means something to you So, yeah, so they they didn't recommend boycotting either So what they planned instead was Making sure that the census process was as expensive as possible for Lockheed Martin So Lockheed Martin received a hundred and fifty million pounds for the census and they estimated, you know They and there was a also an option to Fill your census data in online. So these anti-war campaigners said, you know, don't fill them online because that saves Lockheed Martin a lot of money Make a few mistakes here and there, you know in your telephone number in your address things that don't really matter Yeah, maybe like push it right to the deadline send it by post And they came up with this list of things that you could do to try and make the census as expensive as possible on an individual level Which I thought was really clever But you know the fact remains that you essentially had no choice of what to do you had to fill in the census You had to give your data to this into this company And this is kind of a trend that we're seeing in the UK at least of Increasing numbers of public-private partnerships. So when the government is partnering with a private Corporation to carry out really core government functions And this is what I've been looking at with my work at the moment. So just another really quick example is G4s So they're I think the world's largest or one of the world's largest security companies Making no judgment on what they do with this data But this is just just a selection not even all of the activities that G4s carries out in the UK And if we look at the people who are Affected by most of these things they're people on the edges of society. They're asylum seekers. They're former offenders They're people the survivors of survivors of domestic violence. They're children. They run eight children's homes In the UK as well as I mean G4S also do G4S also do a number of other things like run prisons in the West Bank And all sorts of horrible horrible things So but the point here is that they're doing this as subcontractors of the UK government So if you come into contact with these in any with any of these things It might not you might not even realize you likely don't realize that you're not dealing with the government It looks just like the government, but it's not And there's no opt-out function. So they're providing really core functions Children's homes CCTV at train stations for example So, you know, what does this mean for us? This could have been a really big slide So at least in the UK we seem to be giving up really core functions That should be done by a trusted state entity with accountability and we're giving up that accountability to these companies So as a citizen if one of these companies does something that I don't agree with it's very kind of murky and difficult to know Where the lines of accountability lie like who can I go to to say hey They treated me in a bad way, or I don't agree with what they're doing And there's a complete lack of well not a complete lack of transparency There's a very kind of strategic way that they seem to be doing transparency For example, if you if you know where to look with all these this bureaucracy on the government websites You can find out that it's companies doing all this, but it takes a long time and like most people I mean, I don't think many people assume that you know when you see someone in a government Function to ask hey, are you from an arms company or a security company? At least I hope not because that would be terrible Yeah, so Some of the work I've been doing with tactical tech has been looking at the the questions We should be asking when we come across relationships like the one we've just described like how can we? Reclaim that data and those functions. How can we gain more accountability? How do we kind of try and stop this trend or at least try and hold them accountable for their actions? And Yeah, there aren't any answers yet. I'm sorry So in conclusion there is there are kind of three basic things that I've I've realized are kind of emblematic or very important in all of the relationships I just described transparency choice and trust so transparency about Who is collecting the data who actually has access to it like really? I mean if you're a company designing assistant to collect data and then the government say that company actually have no access to it Do they really like who carried out that privacy impact assessment? Do we trust what they're saying or not? and is there you know, is there any Who owns that data? Do you have access to be able to say actually? I don't want that data to be represented here and then as I mentioned the kind of Accessible levels of transparency. So not just saying oh, you know, it's on a government website somewhere like making it really really Visible who is doing what so that people can have the choice And is there an option to be to not be in the data? So this you know speaks to the the German idea of information self-determination You know, is there is there an actual option to opt out or do you are you then facing more exclusion? Or a fine Is there any pressure put on people to opt in and is it being done by people who hold lots of power? And and then the final one Trusts, I mean this depends a lot on where you live Whether it's a democracy where you expect where there's some kind of social contract where you expect to have that relationship with your government I mean if it's in Sadly many of the world's countries you don't have trust and that's assumed But for example, I mean, I'd hope that at least in Germany in the UK We we try to establish or kind of try to still claim that level of trust to a to an extent Do we believe the answers that they're giving so even if they're they're being transparent are they telling the truth? Can we believe them? Will this change in the future in any way like if you give your data to one entity and they say we're gonna do this And you trust them and you believe them. Is there any chance that could change in the future? And then that same entity might still have the data Yeah, and yeah, I think that's that's just about it from me. Thank you