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>> Ok so so as I said the book is called all day in our local and I'll explain what that means in a 2nd. As I was saying I started writing this book. To me when I 1st came to Georgia Tech and for context I'd like to say that I actually had 2 kids during that period.

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Who are now 3 and 2 and I think the kind of shape the work as much as anything else but a lot a lot of a lot has changed in the world in the outside world this well and this book is a book about data and data probably you have noticed has been in the public.

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On in newspaper headlines and such. Data and algorithms and the kind of interrelationships between the 2 have kind of hit the spotlight and we're at a moment today where the idea of the kind of unfettered potential of big data to transform the way we do science or business or government now seems kind of quaint compared to what it was when I 1st started writing the book but I do see articles like this all the time.

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Calling out the biases the absences the unintended on in to speed it impacts of data and algorithms and even blatant misuse right think of Cambridge in a little. But you know we know now that data can be racist and it can be sexist data hold a lot of values and assumptions.

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Stemming from the systems through which they are created. And the examples run from hacking to election hacking right now I don't think any of us though are ready to give up on data as a way of knowing about the world so the question that this book tries to address is how do we move forward how can we work both effectively and ethically with data knowing that data is imperfect and we should we can't assume that they are just facts about the world.

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Here's an image of the front cover of the book I want to say kind of straight out. This book builds on a long history of research. On on data but also on knowledge systems more generally and really coming out of what one might call a feminist perspective on knowledge and understanding that knowledge is situated and that means that knowledge is produced by people who have bodies who live at a time in a place and those those things matter.

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Knowledge isn't just some kind of de materialized right so people like Donna hare away and Sandra hardy people have really kind of reshaped the way we think about knowledge and objectivity the argument is really summed up by the title of the book. And what I mean by all data are local is simply that data are created by people and they're due to 4 machines at a time in a place with the tools at hand within existing organizations.

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Often for audiences that are conditioned even disciplined to see those data and interpret them in certain ways so data are kind of rooted in our material cultural and historical moment right. Now one of the one of the examples that I like to give kind of straight out to the kind of really bring this home in a concrete way is coming from a project I did a couple years ago with the student we were looking at we're trying to work with library data and this is data on collections of books images maps newspapers all kinds of things that libraries hold then you know these are the kinds of good this is that I'm sure you all have kind of work with in one way or another and we're looking specifically at.

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Collections of the New York Public Library Anybody been to the New York Public Library the one with the big staircase New York the lion. They have they have an enormous number of records it's one of the biggest research libraries in the country if not the world it's an amazing place has a long history it's has lots of resources we were interested in kind of putting all the stuff that they have onto a timeline and in order to do that you have to look at the dates of publication or production of these different artifacts but we started to find some anomalies or kind of what Paul Edwards calls in homogeneity differences in the way that dates are written so this is an example of what you might think of as a date format so it's a way of writing the day to day month year how many different ways of writing the day do you think we found in your public library just in the collections of this one this situation that if you've seen this before don't answer.

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Anybody have any guesses how many different ways of writing the date to the New York Public Library you. Won another guess. 100 and the other guess is. About 1719. Ok And this just to give you a little closer view this is some of those some examples and when you see them you're like yeah I get that some of them are proximal.

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Some of them. May write out to the monks some of them don't even have the date or may have the name of the printer but they you look at these and you realize these are very human. And these are not errors to be normalised away right they really came out of catalogers diligently putting down the date as they best understood it you know for.

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You know for human service not for computers necessarily and a lot of our data. Has has these issues I think that. One way of thinking about these kinds of date formats. Comes comes from Mary Douglas who is actually an anthropologist and she's written a lot about der Now why do we care about dirt relationship data Well if any of you have worked with data you probably talked about cleaning the data right.

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And that often means kind of getting rid of the day the data or the pieces of data that. Are errors or are wrong or shouldn't be there making the data set kind of neat and tidy and very Douglas in her studies of kind of what dirt means in different cultural situations and I came up with the following definition she says there's no such thing objective really as her dirt is jest matter out of place so if if you know you are eating your burrito you've got some salsa on your burrito that salsa is not dirt right but if it gets in your shirt that's dirt so it's not the property of the souls of per se but where it is and I think we can use this to think about data so the stuff that we want to clean away that we think of as data detritus dirt it's really just.

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Data out of place it's in a new situation for a new audience where we weren't expecting to see it and we don't know how to kind of recognize it or deal with it so if we stop looking at this stuff as. Dirt in an objective sense but start being curious about where does this data come from I think that takes us that's going to take us a long way and the underlying message of the book that you know if you take away one thing from this talk it's that we need to move beyond talking about data sets you know what is a set imply imply something that's complete you know we know this from coming from.

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From the language of mathematics is something that's complete it's something that's closed it's something that can be moved that can be transported from one place to another without losing its fidelity but I think we're better off thinking about data settings the situations in which data are made and the situations in which data are used because data have.

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Really complex relationships to those settings and there are different kinds of settings that will talk about what I mean by settings but data do not exist Thomas Lee They're part of knowledge system. As are always ways of knowing right now some people ask me what do you mean by data and who has had a hard time explaining what data means maybe to their friends or parents or it's like what is data Well Christine.

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Is. Is a professor of Information Studies at u.c.l.a. she kind of Michael Buckley who also works there have thought a lot about this and they have kind of come to the conclusion that actually data kind of like dirt the term dirt it's kind of a rhetorical term there are no essential properties of data.

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And they like to just characterize data as alleged evidence right so they say that actually asking what our data or what is data is the wrong question we should ask when our data when does something become data because anything can become data if it starts to be caught if it's taken up as part of a claim that you're making so if we leave this room and we leave behind all kinds of stuff maybe some boxes from both maybe just like you know a napkin or a pencil somebody could come in here and see all that stuff and start making claims about who is in here what they were doing and so forth right Boardman gives this great example of imagine you find a box of photographs in your parents' attic that's not inherently But if you start making claims about those photographs or with them about your family at structure kind of clothing trends of the day in those photographs or even that can excite the cameras used to take those pictures then suddenly becomes data so I think that's a really nice way of summing up how we can use this term I think it misses something that's important though that comes from another.

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Another theor is a philosopher of science named brutal a tour maybe you guys heard of the tour. And he says he actually doesn't use the term data so much but he talks about what he calls inscription. But essentially the same thing but he says inscriptions are used as a great term to summarize data and scriptures this is their immutable Mobile's What is that well it's something that's moved all that moves and as it moves it's immutable it doesn't change so you go online and you download a data set you don't expect it to change when it moves from the server to your computer that's what that's what it means so we expect data to traffic.

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We expect evidence to travel as data and I think that's very important for how we think about. Data and work with data so Mike I kind of like to smash the gather and say actually data are a little mobile evidence evidence that we'd like to believe could move but there's actually a lot of work that goes into getting transferring data from one setting to another and that's work that may be a lot of you as you x. designers are going to be faced with how do you move data from one setting to another and in a responsible effective way and I think this something and something to Portman's conception of this question when our data because I think we should also be asking where our data and something can only be data and we can only make claims with it in certain kinds of settings and I'll talk more about what I mean by that Ok so.

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I approach data not as facts in this book but as what I call cultural artifacts. That means they come out of practices that are there there are situated within a certain disciplinary historic all setting and like other kinds of cultural artifacts things like books. Things like photographs paintings films they need to be interpreted we need to understand them as expressive right.

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And. And we need to approach them with a certain kind of curiosity about what they might mean rather than a sense that data should should speak for themselves right and a desire to kind of investigate beyond the data we had of these data come from and so forth.

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Now the book is structured by a number of principles I'm not going to go through these in depth but I have. I have some examples I want to show you but the principles are kind of just to go through them quickly all data are local data have complex attachments to place which I've said.

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Often data are collected from heterogeneous set in the search sources so it's not just one source that we have to worry about but many. And the data and outward are are in Tangled in ways that aren't always obvious sometimes we like to think of these as separate forms of computation or elements of computation but they're always kind of developed in conjunction and that the interfaces that we make and use are our recontextualize and that means they're bringing them out of one set and use of the setting in which they're produced and they're creating another setting in which those data are meant to be fully understood.

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And then finally that data are indexes to local knowledge that means that data are always partial one metaphor I like to give is you know I wrote this book I want you all to read it but imagine that I just gave you the index. Well you know that's not of no use it tells you a lot about what's in the book the subjects that are in the book I mean in a way it's a data set based on terms I use in the book but you wouldn't expect and that index can be a great guide to finding things in the book but you wouldn't expect to use the index.

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For the index to substitute for the book right you want to have that book so with every day you say that your source you have to ask kind of what is the larger knowledge system that this data set is going to kind of connect me to and help me explore right and that maybe people human beings that maybe sites or other phenomena.

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So before I get too much into examples I want to talk a little bit about the stakes because maybe you think Ok so what there are lots of ways of writing that date. And I think that what I'm trying to speak out against here is what I had what I had characterized as a form of digital universalism Now this is a.

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Phrase that comes out of the work of a need to Changi anthropologists of science and technology who's. Written about kind of computing but not in the places that we expect computing to happen as she calls it at the periphery so. And she talks about digital universalism being this assumption that these devices software data sets that we're we're creating We expect them to go anywhere to be used by anyone at any time and that it shouldn't matter where people are who they are.

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And it's just not true place still matters it's part of a long history of what I call place agnosticism the digital media the sense that place doesn't matter anymore you know ubiquitous computing we should be able to do computing anywhere and it's always the same kind of generalize all these systems and there is there's a long history of this I think one of the most ardent supporters of this is Nicholas Negroponte who some of you might have heard of started the Media Lab Now it's been more recently in the news for other things well related things but he wrote this book back in the ninety's called being digital and.

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Prophesies that being digital was going to mean less and less dependence upon being in a specific place at a specific time eventually he wrote the transmission of place itself will start to become possible so this. This determination that we're going to make it less and less important. Kind of where you are and on the face of it you know I think this was put out there as a kind of.

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A kind of a way of creating equity a way of kind of distributing resources a way of making everyone matter but it has also had the inverse effect it is also kind of it in many circumstances become a kind of digital colonialism I'm going to read you a little bit of the book.

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Here to kind of characterize that I write the diversity and the prosperity of the world's varied and contingent digital practices because everybody is using computers in different ways around the world really depend on our acceptance of data locality in fact the stakes for the future of the Internet could not be higher if left unchallenged digital universalism could become a new kind of colonialism in which practitioners at the periphery are made to conform to the expectations of a dominant technological culture if digital universalism continues to gain traction it may yet become a self-fulfilling prophecy by enforcing its own totalizing system of norms Fortunately there is still time to halt this March towards placeless this as I argue in the book learning to look at the local conditions in data can be a form of resistance to the ideology of digital universalism and the threat of a razor that it poses C'mere Yad data cultures so this expectation that all data should conform to certain kind of universal standards can really be threatening to different ways of knowing and making information in different places around the world and different times.

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You were different. So these principles in the book I back up through a series of case studies. Of different. And these are these are data they're organized around data sources that I have kind of investigated in the hands on way a lot of them I've. Made visualisations. Or built our thems to explore like the date formats came out of and algorithm kind of written to explore that dataset the Arboretum is a scientific institution it's a.

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Open laboratory composed of trees vines and shrubs from all over the world that's based in Boston the Digital Public Library of America is up a platform that brings together records and digital collections from libraries archives in museums all across the country new Scape is similarly a collection of of news largely broadcast news from all over the country and then Zillow and some of you might have heard of Zillow is might be kind of the more kind of familiar one is actually a market place for a house so it's a collection of listings for housing and each of these data sets becomes a way of kind of telling a different story about.

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The relationship between data and place the the 1st to really examine the attachments to place and then the. Or should say 2 and 3 explodes attaches to place and 4 and 5 explore some of the implications so the implications for our rhythms and how we should think about algorithms and 5 is really the implications for how we think about interfaces.

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See here Ok let's look at some concrete examples of this guy who is think this helps So these are photographs from a balloon. Balloon mapping project over the arboretum that I was involved in years ago as I said they are in Boston you just kind of give you a glimpse of what the arboretum looks like from above.

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It I said it's a laboratory but it's also it's also open to the public it's a kind of park and they have data running back to $872.00. On about 70000 plants that they've collected and how it's now they are now this is a really interesting data setting because it's been around so long and their ideas about what constitutes data and how data should get made have changed over that time and that it really allows us to see how an organization and its history shapes data in a certain way and I want to talk about one specimen from the Arboretum and this is a particular plant it's called the common name is Sergeant Sherry and the Latin name is produced Sarge and it's actually the after the founder of the are you know I'm a guy named Charles Sprague Sargent and here you can see the beginning this is a record from their from their database you can see it the 1st number 13040 is a accession number so it's a number that's given to the plant when it's brought into the collection and that 40 is the year that.

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Planet is brought into the collection 1900. You can see of course these various kinds of Nene's families that it's evolved you can see. I've highlighted there cultivated plant of wild indirect wild origin so it's a plant that's kind of comes from the wild rather than a rather than another collection a lot of their plants come from other collections and then it's collected in fact by Charles Sargent.

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And it gives the address of the arboretum as his address. Now. If we were to kind of we didn't know much about the arboretum we downloaded this data set and we wanted to mine it or visualize it do other interesting things with it we may see this information and think And then at the end it says Japan is where the plant.

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Heralds from so I think Ok Sargent collected this plant in 1940 from an expedition to Japan. The problem is that Sargent died in 1027. So. So that assumption would be wrong and in order to really understand this record we have to know about this other field right before cultivated plant of known in direct Wild origin there is this is eat and there's a little clue to what that means in the kind of parentheses where it is in direct.

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Z. means it was a cutting from another plant that means they cut a piece of this plant and they replanted it created a new plant in the arboretum grounds now this is a very different expression number 16760 because actually the way they created accession numbers changed in the intervening period they no longer included the date.

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And you can see it's still a lot of the other information of the same Charles Sargent but it has a w. instead of a z. and that means that it's collected directly from the wild and. And then here you can see that the date of accession is 892 so accession doesn't necessarily mean.

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It means many different things that isn't there sort of mean when the plant was born so to speak. So there's a complex now it's not this is not an error something wholly mysterious but you have to know something about how these institutions. Create their data in order to work with effectively when I started working with this data to make visualizations like this you know I had to learn by talking to the folks at the arboretum about you know how to decipher and interpret this data and.

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Places that you know on Monday may be missed in the lead now this is a visualization of all the accession So it's all the plants that they brought into the arboretum over from 872 to 2012 when I started working on this project and. You can see that the green there are 2 different colored dots now the.

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Green dots and this is kind of too small actually to see here very much but the green dots are plants collected from the wild black dots are plants collected from. That come from other cultivated collections in the United States or in other places around the world and yellow dots are these cuttings and if you kind of squint you start to see these patterns and actually this story of cultivated Versus Wild tells us a lot about the arboretum generally and its history more generally because where you see there's a large cluster in the 20.

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From the wild when it stops that's when the arboretum had a big battle with the u.s.d.a. u.s.d.a. said hey you're big bringing in all these invasive insects along with the plants you can't collect from the wild anymore and Sergeant lost that fight and they transformed actually from being this scientific institution where they're collecting from all over the world to a place to do horticulture essentially demonstrate models of gardening and for a long time until the seventy's they stopped doing while collecting from abroad and then in the seventy's you see that there's a there's a new ring of green so I should say that this is organized kind of like the rings in the tree so each ring is the year of collecting and as the as the kind of visualisation grows so does the collection now just in case that wasn't clear.

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So in the seventy's they. Which was the centennial anniversary they were bird and they initiated this collecting from the wild they kind of resolve their issues of bringing invasive bugs and they changed the mission so in a way this is a story about the history of the organization.

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You also see some other weird things that are going on there's like a kind of ball and in the middle there that's World War 2 where they didn't do much collecting there's also I don't think I have a winter but you can see there's another swap here in late December when you think that is.

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That's Christmas so these kinds of institutions are shaped by all kinds of forces in these collections so kind of really understanding them. Is about understanding how they're kind of situated and of course this there's a bar at the bottom of dots that's actually. Expressions that we lost the dates of accession for we don't know when these are exceptions so there's so much going on here.

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Here's another simple example I want to talk about the Digital Public Library of America has anybody visited or been online to a public library America. In a lot of ways it's a great thing because it pulls together all these resources gives us new a new kind of access but the problem is you know it presents you with this search bar and says discover 21000000.

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Or more images texts videos from across the United States and we don't really in some ways it's suggesting that there's just all this data is just kind of one big pile of stuff on the other side of this search bar doesn't really matter where it comes from but it does and there's one story that particularly stands out for me that came from a Curie from the Smithsonian which is one of the contributing collections Also University of Georgia system is another cool contributing collection and she said.

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Mario McWhorter is her name she's an African-American historian working at the Smithsonian for many years and one day she decided that she was going to query the collection she was interested in if she typed in the word black into the search what would she get and she got all these and this is the kind of one on the left.

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All these images of art made by artists. Who whose racial identity was black or African-American. And or artifacts that had some relationship with blackness as a cultural and racial identity and then she typed in white and what do you think she got well paintings that were white that had the color white in them or maybe the only thing about like whiteness as a racial identity was like one or 2 items that dealt with white supremacy and her reaction to this was.

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Black blackness or black identity is something that's tracked by the Smithsonian and it's readily it's. Discover a ball and and whiteness is it. That suggests that whiteness is what it's the default it's the expectation and black is the deviate deviation from the norm and so that deserves to be kind of called out or stamp and there's something that is very powerful about that realisation that whiteness is it has a power in its invisibility so here in absence in the collection an absence of data about whiteness suggests in fact you know the you know what we might call the kind of white supremacy mindset of the Smithsonian the doesn't see that as an identity that needs to be tracked because it's so.

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It's so expected and this was very much her interpretation of it. So what happens when all this stuff gets folded into these mega mega collections and we lose this sense of these kind of. You know meaningful interpretations that come from people like curators. And historians and we just see this as data just as kind of a set of facts the last example that I want to talk about comes from Zillow the marketplace housing right.

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Now. Zillow claims they want. Data to be free and they've they've made this website so you can kind of bypass real letters and see all the data on the housing market across the United States and you know you open the site it says find your way home and again we see the search bar and it's just a portal to data from counties across the country and so they're actually using public data data about our homes are the places we live that's created by our local governments often for purposes kind of taxes and tax assessments and so forth and they're using it to drive their their engine and a lot of people use this and some people are quite attached to it and this is not to pass judgment on on people who use it I haven't used it.

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But I think we should we need to think critically about. What's happening in interfaces like this and here is a situation where data is again. What one might call de rason pulled up from its roots from where it's me in places like Fulton County and presented in a new setting.

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And this is what I characterize as the setting not of course settings have many layers you know there's a setting like this room where we're looking at this data but there's also the setting of the interface that really brings a new kind of context the data in the form of Xeloda own maps right and the language that Zillow uses find your way home and algorithm that they call this estimate that ingest all that data and actually tries to predict the value of the current and future value of every home in America so not just things that are on the market but about everything and you can see there's the so this is actually a house on the market and there you can see.

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That's coming soon there's the sale value and under it there's this estimate now here it says This estimate is 649025. So it looks like it's close right but the truth is that Zillow zest admits are only within 5 percent of the sale value about 50 percent of the time which some critics and a lot of religions who don't like those who.

[00:35:39]
Pass off or say it's really just close to a coin toss it's not a very accurate assessment and actually Zillow will tell you that if you kind of read the fine print and know encourage you to see this as a starting point for it. But people but these kinds of predictions are very tantalizing for people people who are thinking about selling their homes people are want to buy a home people who are just anxious about what's happening with the market and many people will check in every day because updates on a daily basis.

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And my worry is that this kind of use. Of data using data like tax assessments and if you go and do something like interview. The Fulton County Tax assessors which I have done you will learn that those Texas estimates are riddled with errors and why is that Ok for the tax assessor well because.

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When the when the county mails you your tax assessment that's actually the beginning of a process of. Contest station where you can actually try to correct your tax assessment by saying actually you know I don't believe my home is worth that much you got the square footage wrong or so forth it's part of a social process it's not just a fact actually those assessments are based on models themselves so they're not pure data they're not the fact of the value of these properties and yet Zillow ingests them and uses them as the kind of ground truth for there's estimates and when people don't really understand where these data have come from.

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They can have all kinds of negative impacts and actually I worry that. Zillow is shaping a new culture of real estate and we can see that year after year the real estate market is kind of becoming. More bearable for many low income people in this country and there's a there's a kind of anxiety around what's going to happen with home values and I don't mean to say that Zillow has started this but they certainly enable all kinds of anxiety.

[00:37:55]
Laced behavior around kind of these values and how they're changing with using bad data base. And and when the market fluctuates and prices go up 2nd have particularly negative effects for low income. People who whose taxes might go up because these kind of perceived values might go up and they have to suddenly because you know maybe you moved in next door to them and you spent a lot in the house because you saw this estimate was high their taxes suddenly went up you made their lives more expensive and they might have to move out and that's called gentrification So all these kinds of data practices have real stakes for real places and you know that's happening all over Atlanta right now you know in particular people are worried about gentrification along the belt line how the beltline is affecting home prices and zest them it is part of is part of that system and part of those changes.

[00:38:58]
So just to kind of sum up a couple things and if I have time I want to show you one more project but. At the end of the book I suggest a number of practices for how can we how can we work with data different talked about seeing the data setting rather than just the data set.

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Thinking about the actual war not just where data are made but where they're used like you know how are people using zest to MIT's and Zillow in your neighborhood and how is that changing your neighborhood that's where it matters. Making place part of data presentation you know and there's all this new interesting work about how can we make visualizations that are.

[00:39:43]
That are meant to be seen in particular places rather than meant to be seen anywhere. How do we take a comparative approach to data analysis and and by that I mean asking you know any time you encounter a new data source ask how are the similar data made or how are data like this made differently in other places and they're actually some of these projects like Digital Public Library America below are actually opportunities to learn about because they bring together data created in different ways together in one in one place and you can kind of see those differences.

[00:40:21]
How do we challenge the kind of assumptions about what data should look like that are in algorithms our rhythms are always written with certain kinds of data in mind and data are made with the expectation they're going to be read in a certain way either by humans or algorithms so they are very So how can we kind of collect new data that might challenge some of those assumptions.

[00:40:46]
You know we might think of you know often interfaces like Zillow are called frictionless because they're meant to make a task that was previously difficult easier so it was difficult to navigate the real estate market it was difficult to find a home or to sell your home Zillow is going to make it easy.

[00:41:04]
They're going to make it frictionless but buying and selling housing and property has serious implications for the structure of our society and so maybe it shouldn't be so frictionless and how can we and where can we add in some friction to get people to think critically about how they're using the time of the book and then finally I would encourage you to not use data on their own but really take data as a starting point to build relationships with the people and places beyond data ask Who could I who could I contact that would know more about this dataset or know what's behind it or or how it's.

[00:41:49]
Now I have a project that tries to synthesize a lot of this work it's called the Map Room it's a collaboration with the artist Jared Thorp and the idea of the Map Room is to really foster the development of new local spaces for map making where people can creatively and collaboratively explore data so these are real physical bases and we have one upstairs we want to come visit into a 9.

[00:42:21]
And people get together and we treat the exploration of data as a social practice so lots of people are working together it's not one person sitting in front of their laptop it's many people working together and. One of the one of the unifying or underlying goals of the Map Room is to help people.

[00:42:46]
Not just interpret or explore data but also be able to contribute new data from their own lived experiences and so people actually hand drawing these maps there's a projector overhead which allows you to see existing data sets and so forth but. You're also able to put down experiences you've had observations you had The end up becoming part of the same surface where you're looking at more authoritative data sets maybe from the county maybe from Zillow.

[00:43:21]
From other places here's kind of what the system looks like you see there's a projector overhead it's on a rail and move makes very big maps there 16 feet long and 4 feet wide and this is intentionally to. Create things that are that can be displayed publicly that are really going by talked about place being part of data presentation how do we think about data as kind of things that are in the world and not just an owner's.

[00:43:51]
Now it's really trying to push back against. Assumptions about objectivity of data the abstractness of data how do we connect data to our lived experiences that memories really play place for making maps about the place you live not just any place not some place far away but some place you know and encouraging people to see data and data representations that's kind of locally grounded here you can see a bunch of Georgia Tech students using the system they create maps like this is actually a map of the beltline.

[00:44:26]
Different parts of the beltline closeup you can see. So it's a new way of thinking about how do we present data how do we work with data. You know we want people to examine challenge even redefine the stories that data Taleb out there lives in the places they live and we think this is one path to do that.

[00:44:49]
We also have a. Version of the map and they can be set up in new places not that doesn't mean that places don't matter and every time it's kind of a different. Condition that we have to be mindful of but you know this is that a conference recently these are the kinds of maps people who make this is a new world and you can overlay different data layers on to what you've drawn.

[00:45:19]
So these are these are data about actually gentrification changes in median income and percentage of college educated residents in occupancy same kind of race. As well and in New Orleans of course as in Atlanta there's a lot of segregation we have also been working with the city planning office in Atlanta to use this system in new settings.

[00:45:44]
And I think just kind of in summary. Embracing the importance of place thinking locally about data is really a form of thinking critically. And. Of course there's been a lot of students involved in this work and if for anybody who is interested in this and want to get involved feel free to reach out.

[00:46:08]
Often you know when I wrote the book kind of getting back to that you know I've always had that my students in mind is the primary audience I think one of my students. One of my students need to know about data and how to approach data affectively and ethically but I hope that the the book reaches a broader audience and sometimes help me think about that broader audience I think about my kids that you know came into the world when I was writing this and I think if they ever grow up and read this is what I want them to take away from it and here's the message that I that I'd like to like them to take as you seek to explore the world data can be a wonderful starting point an opportunity to get closer to people and places beyond data but do not mistake the availability of data as permission to remain at a distance Ok thank you not take any questions.

[00:47:12]
Yes. Yeah. One. Way. Which would be one of your. Yeah it's interesting because you know when we say when we hear like these expressions like data is the new or your where data is the new go to. Think about I mean those are kind of a let's take the if we kind of just accept those.

[00:48:08]
At their face value Think about what Boyle has done to the planet think about what oil has done what extraction of oil has done. You know there's a lot of violence there is a lot of destruction that's come along with the oil so yeah you know data may very well may be the new oil but how we treat it if we treat it the same way do we really want to reproduce that kind of system and gold is similar and you know these are you know there's a kind of language of extraction in mining that I don't think we want to repeat so I think on one hand there's certainly there's a lot of value.

[00:48:49]
And that's why people are saying these things but I also have we have to think about. How have we dealt with valuable objects and things in the past and what kinds of destruction has that wrought and do we really want to perpetuate that I don't know if there's off the top of my head anyone out there well I should say you know.

[00:49:11]
An organization like public I don't know if you guys are familiar with public labs their kind of civic science institution. That they really encourage. Helping people. Acquire and even build their own tools for data collection and analysis and they're pretty great I think you know there's certainly probably a number of organizations out there like that but.

[00:49:40]
Off to think more about Make a list of. Organizations and the other questions. Ok. Here's. Yeah. Yeah. Where oral ization is a great way of. Expressing representing data and there's a there's a wonderful project called a sort of joy where people are reading data from an art museum which is very evocative So I think there are a lot of different ways of presenting data and thinking about writing you know I had I had to write a book and you know it's a visual medium and that I think there was a kind of flattening of practices into that form so but certainly there are many other tactile you know other sensory ways of encountering data so I think that's very good to bring up and worth exploring Yeah.

[00:51:11]
So. You can be in touch with try to be in touch with the people who are either involved in making the dado who or work with the data closely and understand that you know I think one of the earliest experiences for me and really realizing. Giving validity to this approach is I was I went in for a meeting with a guy named him a Glock and he used to be a professor here in urban planning since he's actually moved to Georgia State but this is like one of the country's biggest experts on housing and he has dealt with housing data all his life and I said What what's the 1st thing you do when you get a new data about housing and he says I pick up the phone and I call the people who made it.

[00:52:06]
And it's just such a kind of acknowledgment that like he doesn't expect the data to speak for themselves he doesn't expect the data to stand alone so he sees data as part of a larger inquiry and you know really getting back to this metaphor of like using data as an index rather than as the text and so if you can approach datasets that look that way you know it's Ok to deal with data that was made in another place but you shouldn't expect the data allude to help you see that place does that make sense.

[00:52:43]
Ok. Yeah well thanks again for coming.