The Law by Frederick Bastiat, probably the book, very thin book, some call it an essay, which changed the way I see the world more than any other. It was the first thing I read, the first arguments that I heard that describe the law, not as the thing that protects me, as I had been taught since kindergarten, but as the biggest criminal among us. I want to read a few excerpts and I want to dedicate it to those among the Occupy Wall Street protesters who believe a lack of laws were our problem, and a greater abundance of laws or a stricter enforcement of laws are the solution to our economic problems. The law is perverted, the law and in its wake all the collective forces of the nation, the law I say not only diverted from its proper direction but made to pursue one entirely contrary, the law become the tool of every kind of avarice instead of being its check. The law has been perverted through the influence of two very different causes, naked greed and misconceived philanthropy. Let us speak of the former, self-preservation and development is the common aspiration of all men, in such a way that if everyone enjoyed the free exercise of his faculties and the free disposition of their fruits, social progress would be incessant, uninterrupted, inevitable. But there is also another disposition which is common to them, this is to live and to develop when they can at the expense of one another. This is no rash imputation emanating from a gloomy, uncharitable spirit, history bears witness to the truth of it. Now labor, being in itself a pain and man being naturally inclined to avoid pain, it follows and history proves it that whenever plunder is less burdensome than labor, it prevails and neither religion nor morality can in this case prevent it from prevailing. When does plunder cease then? When it becomes more burdensome and more dangerous than labor? It is very evident that the proper aim of the law is to oppose the fatal tendency to plunder with the powerful obstacle of collective force. That phrase and several others are what make Bastiat a minarchist. And I, after reading this and thinking I became a minarchist too and held on to those arguments for quite a long time before I was, after being put to task by some friends, I became a complete volunteerist. Um, the powerful obstacle of collective force. That all its measure should be in phrase in favor of property and against plunder. But the law is made generally by one man or by one class of men and the law cannot exist without the sanction and the support of a preponderant force. It must finally place this force in the hands of those who legislate. Bastiat critiques many philosophers of his day and some of his predecessors including Rousseau as great supporters of the tyranny of law. And then he specifically addresses socialism. Socialism like the old policy from which it emanates confounds government and society. And so every time we object to a thing being done by government, it concludes that we object to its being done at all. We disapprove of education by the state, then we are against education altogether. We object to a state religion, then we would have no religion at all. We object to an any to any quality which is brought about by the state. Then we are against education altogether. We object to a state religion, then we would have no religion at all. To any quality which is brought about by the state, then we are against equality, etc, etc. They might as well accuse us of wishing men do not eat because we object to the cultivation of corn by the state. How is it that the strange idea of making the law produce what it does not contain prosperity in a positive sense? Wealth, science, religion should have ever gained ground in the political world. The modern politicians, particularly those of the socialist school, found their different theories upon one hypothesis and surely a more strange, more preposterous notion could never have entered a human brain. They divide mankind into two parts, men in general, except for one, form the first part. The politician himself forms the second, which is by far the most important. In fact, they begin by supposing that men are devoid of any principle of action, of any means of discernment in themselves, that they have no initiative, that they are inert matter, passive particles, atoms without impulses, at best a vegetation indifferent to its own mode of existence, susceptible of assuming from an exterior will and hand an infinite number of forms, more or less symmetrical, artistic and perfected. Starting from these data, as a gardener, according to his capris, shapes his trees into pyramids, parasols, cubes, cones, vases or fans, so the socialist, following his chimera, shapes poor humanity into groups, series, circles, sub-series, honeycombs or social workshops, with all kinds of variations. And as the gardener, to bring his trees into shape, needs hatchets, pruning hooks, saws and shears, so the politician to bring society into shape needs the forces which he can only find in the laws, the law of tariffs, the law of taxation, the law of assistance, the law of education. It is so true that the socialists look upon mankind as a subject for social experiments. These gentlemen, the reformers, legislatures and politicians, do not desire to exercise an immediate despotism over mankind. No, they are too moderate and too philanthropic for that. They only contend for the despotism, the absolutism, the omnipotence of the law. They aspire only to make the law. The law is justice, he's talking about now the proper function of the law. The law is justice, nothing can be more clear and simple, more perfectly defined and abounded or more visible to every eye. Depart from this point, make the law religious, fraternal, equalizing, industrial, literary or artistic, and you will be lost in vagueness and uncertainty. You will be upon unknown ground in a forced utopia, or what is worse, in the midst of a multitude of contending utopias, each striving to gain possession of the law and to impose it upon you for fraternity and philanthropy have no fixed limits as justice has. Where will it stop? He ends with an anecdote. A celebrated traveler found himself in the midst of a savage tribe, a child had just been born and a crowd of soothsayers, magicians, and quacks were around it, armed with rings, hooks, and bandages. One said, this child will never smell the perfume of a calamnae unless I stretch his nostrils. Another said, he will be without the sense of hearing unless I draw his ears down to his shoulders. A third said, he will never see the light of the sun unless I give his eyes an oblique direction. A fourth said, he will never be upright unless I bend his legs. A fifth said, he will not be able to think unless I press his brain. Stop, said the traveler, whatever God does is well done. Do not pretend to know more than he. As he has given organs to this frail creature, allow those organs to develop themselves, to strengthen themselves by exercise, use, experience, and liberty. God has implanted in mankind all that is necessary to enable to accomplish its destinies. There is a providential social physiology as well as a providential human physiology. The social organs are constituted so as to enable them to develop harmoniously in the grand air of liberty.  And now after having vainly inflicted upon the social body of so many systems, let them end where they ought to have begun. Reject all systems and try liberty, liberty which is an act of faith in God and His works. Try liberty.


gal and regulatory vacuum it's it's coming and it's coming more quickly now I'd like to ask whether you see is the role of geographers or the geospatial community in taking and having a more active role in this I mean we see the big the really big enterprises being the kind of tail that wags the dog right now and I wonder what are the maybe the pathways by which academic geographers or geospatial practitioners can have a more of a voice and I apologize because I don't know all the all the different names but I know that there are associations that geographers is a AG and others that that are part of and so and a lot of them have you know policy aspects right so the the bill that you mentioned caused a great deal of uproar because but it was a very narrow uproar right I've tried to get similar concerns around some of the privacy issues and other things and and I think until now anyway it's been well that's industry's responsibility we can do what we want with the data you know there's no reasonable expectation of privacy and public this is open data so I would use the existing organizations and structures for at first but I also think to think of yourselves as as as part of this broader community and so when you see things that's going on with Facebook or when you see things that the government's being asked to do in terms of not being able to to use data in a certain way think about what impact it could have on you because we I think this community tends to think in silos and I think a lot of those barriers are being broken down for the reason I said about the way we're all data collecting and sharing data now in the process go ahead can you can you sketch or if there is a clear evolution can you sketch the path from the two things I thought I understood which is if you if you're a photographer you can take pictures of just about anything from public space and then there was a case in the early 2000s where a fellow decided to document the California coastline from his helicopter in order to monitor erosion and to be on the lookout for construction that went well beyond what was permitted and he got sued by Barbara Streisand who didn't like pictures of her backyard being taken by anybody and I believe he was upheld and I don't know whether anything that would constitute a balancing or an evaluation of the respective rights came out of that was that a was that a start one thing came out of it is something known as a Streisand effect which is when you complain about something then people tend to go see what her house looks like right so people started so it actually created the reverse and there's a similar situation in Pennsylvania when Google Earth came out ironically a family named the Boring's claimed that Google Earth was violating their privacy because it was the street view and they drove up into their yard and they actually trespassed and took a picture and posted it on the internet on Google Earth and the Boring sued violated our privacy and trespass and all these other things and the court threw out everything but the trespass right away and one of the reasons was because and the argument was you diminished the value of our house by having a picture of it on the internet and the one of the reasons the court threw it out is the local county had pictures of all the houses on their webpage and so but the Boring's didn't hadn't really realized that or thought of that so there was no diminishing a value and they had to Google had to pay a fine of a dollar for trespass right so I think personally I think it's drones are going to change all this I think the laws around drones are really causing lawmakers and we formed an association for unmanned systems in Virginia and we're fighting in the general assembly on some of these issues and the challenge is when you're on the ground you don't see a satellite flying overhead right you see a manned aircraft that's collecting some pretty cool pictures but you just assume that's a passenger aircraft when you see a drone fly overhead you don't know if that's your creepy neighbor Amazon delivering a package or you know the government's spying on you right and so there tends to be this visceral reaction particularly in certain parts of the of the country and those people are calling up their state legislatures and they're saying I don't want anyone taking a picture of me or my daughter it's always a daughter at a swimming pool so I don't want anyone taking a picture of my daughter at a swimming pool so you need to stop this and that's and that's that's happening that's slowly happening in Florida right now there's a law that says that you can't use drone aircraft to take an image of someone on their personal property if they have taken steps to not be seen from the road so if they put up a fence now there are exceptions to that and mapping happens happens to be one and other happens to be one as well but that's that's on the books there's they've created a reasonable expectation of privacy and being in your backyard out in out in the common view and it's so it's being whittled away but it it is it is happening in Virginia this was before we formed the association but you couldn't you can't use it law enforcement can't use a drone to go out and collect images for regulatory purposes without getting a warrant essentially they've created a reasonable expectation of privacy to not have a drone collect a picture of you they can go get a satellite a manned aircraft but not a not a drone so it's evolving and drones are really in my mind sort of changing that well David you have oh we have one more questions see if you can get yourself to the microphone here to this mark I was just wondering talking about evolving legal frameworks if you had to take on license plate readers especially in relationship to the recent Virginia Supreme Court ruling on that issue that's a real for me that's a really hard one I I tend to believe that it's not you you you shouldn't have a reason what takes your privacy that your license plate information isn't being collected and used I've actually was at a talk that then Governor McCallup said you know you don't own that license plate that's state licensing that you know giving you a right to use that that's not yours so you should have reasonable expectation of privacy in that so I tend to favor that side but I certainly understand the concerns and I and frankly because for some of the reasons we discussed here without a better way to deal with it in thinking about it it just makes it harder right it's sort of black or white and that's that's the challenge we don't have anything in between right now because this is all also new I think we'll get there but I think we're in that phase where we're still sort of figuring all out that that help yeah yeah thank you isn't there a danger of using the licensing argument for example most software is licensed even stuff that you buy or download and if it does something you didn't want it to do or is the creator of the software protected by the fact that it's a licensed object it's a little bit of a different license unfortunately has a couple of different contexts in the legal standpoint and so the license in referring to the license plate reader issue is simply the state is giving you a right to use that license plate right and so what it's saying is you shouldn't have a reasonable expectation of privacy in that the license agreement that you have with a software provider I've drafted these I'm sure there's also a language in there that says that they're not responsible at all for anything that goes wrong including if the computer blows up on your desk right now whether that's enforceable you know whether you you would have a claim or not I mean there's a lot of issues associated with it but there's all that language that's in that's in license agreements that sort of disclaims also to representations and warranties and I'll point this out I just we've talked about Facebook and Cambridge analytical quite a bit I thought it was ironic that when testifying before the the House of Commons I guess the people at Facebook admitted that they didn't read the Cambridge Analytica terms of use in terms of how they were going to use the data that they were collecting terms of use terms of service they seem so innocuous I have people refer to them as boilerplate and why do we need to look at that and their their contracts their legal agreements and that's what your right is in a lot of instances unless you're a as a consumer you get additional protections you can't waive certain rights usually but in a business environment that's that's what you're signing up for and they tend not to be paid as much attention as I think they probably should particularly in this context okay thank you I'd like to thank Kevin Pompritz and and it's time for us to change gears so now I'd like to invite our other speakers to come sit at the head table and I'm going to ask Grant McKenzie to lead us off in a discussion and let me just say a few words about Dr. McKenzie because we haven't introduced him yet he's an assistant professor in the Department of Geographical Sciences at the University of Maryland where he leads the place time analysis laboratory and he's also affiliated with the Center for Geospatial Information Science he works with big user contributed data sets as well as authoritative data sets to pull out spatial temporal and thematic patterns and he too has a geographically interesting background he holds a PhD in geography from the University of California at Santa Barbara a master of applied science degree from the University of Melbourne and a bachelor's degree in geography from the University of British Columbia so we're going to give him the podium for just a couple minutes and then engage in a in a complete discussion here I would like to mention before we begin that I'd like to remind you that we are we are taping this session and so anything you say might might take on a life and also that that at this point the people who are listening in online if they'd like to can type in questions in the chat box on your screen we may not be able to answer all those but we'll at least receive them and do what we can so Grant Well great thank you so I think it's fault on me to sort of try and summarize this put this in some sort of context as well be very brief I have about five minutes here just to go through some of this some ideas and then turn it over to more of a discussion that we'll leave here there we go so just in summary it's been quite interesting to me one of the things we always talk about is geographical sciences or geography is really being this umbrella discipline I think we've all heard this before and it became very clear to me today and the number of topics that were brought up and the range of topics that were brought up but this is really true right we have this umbrella of geographical sciences where we all hold spatial science or regional sciences are sort of common thread and we vary the topics that we talk about and so I think what's fascinating about this is that we're now because we're in this breadth of disciplines or breadth of domains we're increasingly expected to understand various aspects of these domains much broader aspect of the different components that we're looking at and this could be looking at technology and technology trends and how this then relates back to probably what we're more interested in which is the specific theme or topic that is fascinating to us and this could be things related to health or ethics or privacy or urban planning and so we need to have a better understanding of what all these different components are I think that's something that's a little tricky for us to do especially as we're now starting to realize that these sensors social sensing citizen science whatever you want to call the latest term that we refer to this as and bringing this together is something that we can make sense of I just wanted to focus on a couple of these we've talked about a couple different examples of these but I think there's some issues and some opportunities that we're facing one of the issues that I always talk about and the talks that I give is this example that came out of the London Wi-Fi bin sniffing you may have heard this five or six years ago where garbage bins had basically Wi-Fi sensors as you walked around the city your MAC address of your phone was being tracked and they could determine where you were going what you were doing and so this became a major issue and we talked about sensors and they able to track people and understand where they're going but you can also at some level in the gray area of all this see the relevance of having some kind of technology that does this right there's certain things you could track certain people that were doing things that maybe weren't legal for example so there's there's issues that go along with it and some of the bigger problems that we face with it as well but then there's also these bigger opportunities that we're faced with now graphic scientists and I often say geographers are bridge builders right we're not just the people that study the space itself but we're the people that bring people together in space being our common domain that we focus on so we have to be able to speak a language of computational sciences the humanities social sciences looking at a range of environmental science and what we have so it's fascinating to me that there are very few other disciplines that are faced with this ability to be able to speak these different languages when we're starting to be able to do that some of the examples we talked about I spent a lot of time in in Christchurch University of Canterbury you may remember the series of earthquakes or ongoing earthquakes that are still happening there some of the opportunities we're facing when we think about sensors are when you have devastation like this you also are given an opportunity to roll out things like new sensors right sensors into environment to be that through cell phone coverage through water main coverage traffic cameras all the things that go along with that so with some of the issues and problems the major just catastrophes that we're facing we're also presented with these opportunities to understand how a city operates in the smart city environment or the settings we see and I think we as geographical scientists are well situated to discuss a lot of these problems as well and then the sort of issue or opportunity depending on how you want to look at it of our attachment to mobile devices right or obsession or addiction as that's been defined of mobile technology so we're all carrying around as we've heard before cell phones that have an incredible amount of sensors available to them I do a lot of work looking at detecting place types coffee shops cafeterias these sorts of things purely based on noise sensors and altimeters and trying to determine what that can be used for one of the things I always think is really a fascinating problem that we're looking at right now is that the cell phone really is the frontier of a new battle that's happening right a battle for our attention so a lot of companies are vying for our attention through the use of these technologies and this has moved itself into the geo arena right geo fencing has had a large role to play in this as I walk past grocery store on the way home it notifies me need to pick up milk right so we have benefits of that but maybe as I walk past the vending machine I don't need to know that coca-cola is being advertised based on where my location is so location is increasingly being targeted to me with my wearable devices are my my cell phone so we we have this new sort of frontier that we're having to wrap our heads around technologically I make sense that from a sensor perspective as well I want to leave it a couple of this major questions here that I think are important in looking at this what do we as bridge building geographical scientists need to do to further facilitate this discussion do we need to look outside our fields we need to look inside our field who do we need to speak with or bring together to further focus a lot of these questions and answers one of the their questions I think is increasingly relevant to us as well is what existing theories can be tested with these new sources of data but also what new theories can be designed right so a lot of the technology a lot of the social media data a lot of the things we see out there are doing a great job of helping us answer some of the existing questions we had sort of referencing the theories that we originally had but where are we finding new theories what's what's being produced from this new data we're finding right other than confirming what we've seen previously what are the next steps were the opportunities for researchers in this area merging industry government agencies and researchers to do this kind of research and then lastly what are the limitations facing this broad umbrella discipline what can we do to bring this together to bring people together and what are some of the hurdles that we need to overcome to get to the next step so with that thank you very much I'll leave it there I'd like to open us up to discussion which could be asking a question of a specific person at the head table or it could be asking a general question and we'll let them respond I might kick it off by asking about the sensors themselves and asking in general what's coming next we have cell phones we have fit bits we have traffic cameras if we look into the future I don't know it's hard to say because it's happening so fast but what might we be anticipating that's different than the next five or 10 or 15 years I think that data derivatives is also of image recognition so we talked about driver's license but I think videos are going to be on everything and then derivatives of that data then you've mentioned a couple in your talk which are data from autonomous vehicles it's going to be significant the sensors and cars produce tons of data about the urban environment and that what do we do with that data it's going to be important I mean it's partly there as I pointed out but we're going to see a lot more biophysical monitoring going on and that's being used for things like AG in place so I was just on a PhD committee where the engineers are trying to work on algorithms from the cell phone so you can figure out if somebody's gate has gone off if they're about to have a stroke I was talking to Nancy at lunch there's facial recognition that's being done on highways because they're trying to figure out what happens right before someone has a traffic accident or they text him or they distract him there's a lot going on and mood recognition both facial and voice that's really developing very rapidly so that's being used to predict potential onset to depression or suicidal behavior and then merging all this together what we didn't really focus too much on that today but the remote sensing field and the array of satellites that are going up now and how that is being used with traditional ground data with some of the new sensing technologies that's going to really come on stream and what we're seeing is sub meter resolution with much higher temporal resolution and then the products that are being derived out of some of the specialized satellites they're going to give us capacity to predict but we're fairly small areas things like air pollution and greenness that are important environmental exposures but at much higher resolution of what we've seen in the past with much more detail I think looking toward the cell phones I show that there was quite an array of different sensors they're rapidly moving to the point where they're all going to have to have the capacity to embed all those sensors so there's going to be a lot less heterogeneity between the phones and all likelihood and the quality is going to go up and that will drive development of apps that use many more of those sensors in combination and then context specific recognition is important so the sensors are able to understand the context they're in and that's going to I think lead to we're going to see a merging of this with potential virtual reality glasses and other devices so you might be walking by and something on your sunglasses is going to say hey there's a sale at this store maybe you're interested or maybe you've just done a Google search on your phone looking for a restaurant and you're going to pass by one and it's going to flash the menu so that kind of connectedness is definitely evolving very quickly so this is Marilyn Brown speaking we haven't talked very much about the data that can be obtained from equipment that's now installed in many of our homes the information from the thermostats and our computers IP identified devices that will tell people what we're using when whether we have expensive medical devices in the house whether we're home or not sort of reminds me of when I used to live in a suburb and go out as a kid and my mom would always say before you go out during that light on and then no one will know that you're not there right so we're going to have to figure out how to override all of you know receive the deceptors or something to figure out how to not clearly convey what it is we're doing in every moment when we're home or in our business all of that data too we've not really talked much about that I might bring up two more points in terms of actually I think these more sort of data practices than maybe the actual technology one I think the technology of blockchain is actually really interesting there's an interesting use case for these sort of shared data I mean going beyond the whole sort of Bitcoin cryptocurrency blockchain as long as we're not using a proof of work kind of concept but a more sort of shared kind of easier less computational intensive concept is actually a pretty good structure for sharing this data among you know a lot of distributed users distributed applications so this is more speculative I'm not sure if this is going to come to pass but I just want to pass that out as a possibility and then also building on your point I think I think this really is this other data practice of resistance that you know this is not this is not a set of technologies it's going to come and be imposed on us though it might feel like that technology comes out it's a social process and part of that social process are people taking whatever efforts to resist the technology hide from the technology confuse the technology in various ways and I think that's going to be a really interesting step moving forward as well and actually big implications for actually using it as a research research tool as well Can I just quickly add to that sorry I think sort of the continued on from this direction we're seeing not just in geography or geographical sciences is the fake side of it right so location spoofing is obviously a big deal a part of this as well and you talk about the please rod me dot com or whatever the website was where you can determine when people were home or not but we've also seen this with with the amount of data we're attributing in terms of voice data video data location information the ability to then turn around and fake somebody right to be able to mock to mimic their voice we've seen the videos of Barack Obama saying things that he never actually said where is that going from a location perspective I think is going to be quite interesting too having shown where people are but we absolutely know that they're not at that location are they able to spoof where someone's been where they've gone all their devices that they were in the house but we know they weren't in the house so where is that acknowledgement of where someone's going where they've been and what we're actually able to prove with the data I think it's going to be an extra question as well one point about that and sort of the flip side of that is there's a concept in the GDPR and in Europe the right to be forgotten and you can ask that information about you be removed in a location context that gets really interesting right so that if you have the ability to have someone your cell phone provider or someone say you weren't there even though you were I mean you're changing the truth right and but that's that's that's out there and you know so you could see a situation for instance if there was a bank robbery or something but the only person who was smart enough to call and have them their location changed was the person who did the rob the bank right so there's there are these concepts that are developing not around location that become really interesting when you try to think about them from a location standpoint thanks we'll wide open now for all kinds of questions that so this is something that I've been toying with for quite some time in starting when I read the sensible city lab Dr. Williams might know about it obviously they did the partnership with Yelp right and they scraped the the ratings on Yelp and Boston and developed an algorithm and they passed on to the Department of Health Sanitation in Boston and they were able to do predictive check-ins on health code violations of restaurants right good use case the Yelp users typically are technically self-disclosed their preference to this situation right they said no one-star bad deal gave a sentiment analysis on the text of the review something that's been bugging me and did my graduate research in social media and it's probably why I don't use social media applications anymore is this concept of utilizing active and passive digital footprint with the disclosure if you are self-disclosing or if you are just being disclosed at the same time and how where the ethical consequences of such things and when you can do the ask of the user instead of shifting the onus of the responsibility of the data control onto the licensing maybe between like Facebook and Cambridge and Atlitica you can ask the user how they want their data to be disclosed and how they want it to be utilized so it's a topic I've been like mulling around in my head right I used to use Facebook but I never gave Facebook permission to Facebook tracking pixels right and watch me peruse the internet and what my shopping patterns were so it's kind of it's kind of muddle and weird but to I think Kevin's point it's people have locked it inside the bucket of saying it's hard but maybe we can take it out of the bucket and look at it and examine potentially why it's hard and what to do with that geospatially if you'd like but that's all so I I think there's a it is very hard to give users under the current wave privacy policies in terms of user developed it's hard to both describe all the different ways that you might use the information and put it in a phrase that in the terms that a reader can understand who doesn't have the background and technology that the folks here do and make it short because that's how privacy folks complain about I mean people complain that the Apple Apple terms privacy is you know 42 pages or whatever it is so there's this real tension in terms of how you go about doing that and give users the control and right to do that now the GDPR is trying to do that it'll be interesting to see how that develops because I think that's probably a big step forward in that area but you know it is hard and part of it is I think and the question was asked how what can this community do I think part of what this community can do is maybe break the privacy concerns around geolocation into several buckets and then prioritize those buckets which ones are the real privacy concerns and which ones are just ones that make you feel uncomfortable because some of them we're just going to have to get used to I mean the world has changed but some of them are real are real concerns and I think the geospatial community is particularly well suited in maybe interacting with others to help figure out what those real concerns are and then ways maybe to mitigate them but still allow the data to be used in important ways so I have a question I think Grant you kind of voiced a little at the end but you know we've talked about big data and social data and streaming data and sensor embedded data and a lot of the techniques you all talked about were probably traditional analysis techniques right or in some way I'm curious where do you see like what are the open research questions for new methodologies and new theories that might need to be developed given potentially new sources or types of data or also as I asked or about you're actually asking this the the sensor questions and it's responding to you you couldn't use to do that for trees or land slope but you can the person so curious what are your thoughts on new research opportunities for people now that these data are available or could be obtained that's a tough question I think from my perspective one of the things that I find most fascinating is this and we've been having this discussion for years well before I started my graduate degree on place and space based research right so the what what places and what spaces and what the the multi-dimensionality of places and how do we represent this concept of abstract concept of place in the kind of research that we do and I think we're starting to get to that point where historically place has been you know space and still the meeting by the people that go there or visit the place but we're starting to be able to say well we can actually capture a lot of that meaning right the contextual information surrounding the place the time of day you go to a place the what you eat the photos you post what the temperature is in that place starts to encompass this idea of what a place actually is and so we can start to ask their broader questions in terms of what are the similarities between the different places we see right and so there's I don't know exactly what the theoretical questions we're asking there are but we're starting to be able to approach these concepts that have historically asked as more theoretical questions from a more empirical standpoint and I think that that's going to be a big push continuing forward as we move sort of place-based GIS systems if you want to call them that too just I mean I've been thinking about this since you asked that when I in my after my talk and and I I think the the thing that seems like we might be able to do now I'm not quite sure about the how the logistics of it but is engaged in something sort of akin to sort of a clinical trial sort of do a B testing ask ask people in the same sort of area for thinking about how people are are proceeding a particular location ask people you know a different set of questions or do or impose some sort of different kind of stimuli when they're based in a location and run sort of a real-world world kind of experiment now that sounds really odd to say because that's very that's not how we've done this sort of stuff in the past I think there's lots of sort of issues and lots of things but in terms of what might be a novel sort of methodology that might answer new kinds of questions and build new theories I think that would be one way forward yeah I kind of took a very technical perspective on your question and I think you know I started out in my career doing remote sensing when we had 30 meter pixels and like you know we spent a lot of time trying to identify the noise in the pixels and like figure out this kind of mixed pixel what does it mean is it an urban place or is it a deciduous forest right now we know what every pixel is and that's overwhelming as we heard earlier from your talk right and so I think a lot of the research and technology are like on this very technological level is about how to re-aggregate the data so how to group it into objects how to create structures in which we can derive new information because like when we know every pixel like it's it doesn't make an object anymore right like it's it's you know part of an object so how do we build it back into different kinds of object points whether it's the GPS data or its image recognition data and so that's why I think a lot of the research that's going to be coming out is really about this like re-aggregation of data which is why aggregation of data as a study is also very important for the kinds of things that I was talking about with data standards right so like as we what are the standards for certain kinds of objects and this is already happening with the autonomous vehicles right like we you know they're sensing an object that looks like a human and they're sensing an object that looks like a bike right and then these are going to have data standards that go with it so I think this is a huge area of research that you know one of the reasons I think about is with your data set like with with the stuff that we did in digital metaches when we made the app on the onset we made it so that it snapped into objects so that like as they were collecting it I didn't have to deal with this huge mess of points and that rather I was dealing with objects from the onset so that would be one yeah so I just to round it out you know I think that getting being able to sample out of the data in a way that is going to reveal the meaningful patterns that we're interested in whether it's human activity behavior or exposures or ludes without having to deal with all of the data all of the time so I think that's a really big question you know people have especially time activity very regular patterns for the most part they may deviate when they go on holidays but you know most people are getting up going to work do we need to sample of every single day for every 10 seconds to know that probably not right so how do we start to extract that useful information out and and then I think an ancillary question is how do you determine the quality and what's acceptable and whether whether you can get to that point through sort of digesting it with a Bayesian algorithm so that you're continuously probing the data to see what's credible and what's not I don't think we're there yet but I think that's ultimately the goal and then you know taking into account the multiple sensor data sources that are available and I saw a really interesting talk about oh you know trying to determine the you know nighttime populations nighttime daytime populations all over the globe by somebody at Oak Ridge National Laboratory and you know they're they're using everything from menus off Yelp to transportation data so it's this massive amount of a simulation that's going on from numerous different sensors and information sources so that they can come up with some reasonable estimate and that's an important thing to be able to estimate because if there's a disaster or something like that you want to know how many people are likely to be on site where are they at certain hours of the day also as huge implications for transportation planning for environmental exposures you name it so being able to take that multiple array of huge sensor data and another data that's coming out of the the web and bring it all together in a meaningful way is another big step I think that we have to take when we start looking at the sensor data so and going back to this what can we get in terms of theory and no understandings of human behavior some of what I think we might be able to address the behavioral economists have been moving toward and to understand why it is that actors don't behave rationally and a lot of the explanation for the shortfall and rational behavior is because they don't have information and in fact there's a new term that's been created called rational inattention it's rational not to bother to try to find out the exact costs and benefits because it's so expensive to do so and so people buy what's convenient and they copy their neighbors and they make suboptimal choices and this has been really important in my field which has been about how people make choices for energy consuming equipment because keep buying the same old crap even though a lot of improvements have been made and they're not aware of them and so we could now imagine a market in which you could make a major shift toward greater market efficiency and see it's how much of the gap and irrationality can be explained by providing information because it can now be so it's not so cheap to do so so one of the areas that we're looking at it in Atlanta is there's a very significant energy burden that's being paid by low income households whose bill can be 10 or 15 percent of their household income their energy bills and they may get you know monthly balance billing where they get the same bill every month and they have no idea what it's how they've used it or they now they can get billing like you do with your cell phone where two weeks in it says you've got you know 10 days left or you'll be over your last year's use in the month of May so now there's real time feedback and so lots I think there's a good potential research agenda to determine to what extent people will really bother with this new information how much do they will they how much attention will they pay someone or maybe a couple of you mentioned the attention economy I had actually not heard that term before who said who talked yeah yeah I think that that's a really fascinating area to explore the attention economy how much you will you be willing to pay for more information how much of your attention will you direct to acquire information even though it may be very easy but you still have to pay attention because it may be a bit complicated I forgot to throw that out that was not a very good question what did you have in mind Matthew when you mentioned the attention economy so nice not to be put on the spot no I know I think it's actually a great question because I mean in some ways the building on your or actually both your points but this aggregation I mean we have so much data thinking about how do we aggregate it up either categorically or spatially I think this is also you know a really good you know question but also this thing from the individual user perspective or so the consumer's perspective you're talking about that you know will that you know it will that the new information actually be put to use or you know does it have to be you know be aggregated in some way that's that's easier to actually to consume it's sort of almost almost like the terms of service you can get all the detail but if you're actually not trained to use it you're just going to ignore it and maybe just sort of you know sort of tying this back to some of the other things we've been talking about that you know the kind of attention to which you know to we have as we move spatially what kind of what kinds of things are we paying attention to not that we really should be trying to optimize as spatial advertising or anything like that but you know what are the kinds of the things that we noticed as we move through through a particular urban space or you know natural space and maybe tie in some of these kind of questions as well but I think we'll just end there there was one less there was a review of the book that I read which talks about how much we're how much information is being thrown at us to the extent that our creativity is being compromised because there's a link between creativity and having time when you have nothing to do but think So I'm Lee Schwartz from the Department of State and thanks very much for all the presentations and I want to direct this question towards understanding communities a little bit when we try to have sort of a decade long effort to mainstream human geography and the defense and intelligence community a lot of that was because of the failures to to understand communities and in places like Afghanistan and Iraq and we're talking a lot about understanding space and understanding location and I heard sentiment analysis mentioned a couple of times and in the world I operate in we're in data poor environments and I'm wondering in your fields of research if you could talk a little bit about you know using sensors to understand more how people feel tied to a place and their community values whether it's proxies for religion or the identification of certain of certain structures so getting behind just the way people are located to understanding how communities are organized which is really a major part of of human geography and from the academic perspective to talk about behavioral geography and the likes so maybe a few thoughts on that moving away from from space and more towards communities as a way to think about human geography thanks and not anthropology as the urban planner I'll attempt to take this question I mean I think in some ways the work that you saw in the ghost cities was attempting to understand what makes a vibrant community and that we need places to eat healthcare was one of the amenities that we looked at bang schools right and that having like a certain access to these different levels of basic services is what makes a thriving space and I think what's interesting about what you I mean you know one of the things that we've thought about during that project was like well what's important to somebody in China versus like what's important to us right so that was part of the asking questions of the urban planner so like one of our amenities is KTV which is basically a karaoke bar um and like malls was another one of the amenities that like malls malls are very important just like in Chinese culture like because well they're also important to our culture but also schools were a big issue for them they'll drive across the city to make sure that their kids they're going to proper schools and really like one of the number one reasons um that some of these areas were ghost cities because they didn't have access to proper schools but um what I think is important about what you say is well how do you do that in a data poor environment right like in that how when you don't have information can you start to set up these different parameters and this is something I think a lot about in my work that I've been doing in Africa and I would actually argue that the data is there as I said but it's about accessing it in ways to understand these books say like a different amenities a set of amenities or yeah I mean we don't have their data poor because we don't have the data this is because governments are not doing the surveys that they do in other areas right or we don't have Yelp which is you know like I use Yelp in this case in China the Chinese version of Yelp and um but there are other kinds of data sets that do do exist but then I would also further argue that missing data tells us a lot so where data isn't is very informative about the types of access to services that a certain kind of community has in the way that they operate right if they don't have access to the internet then they're communicating in much different ways than they are they did have access to the internet or they're using other kinds of social networks and so knowing that there isn't access to those things this is important to understanding community structure as well yeah I guess you know the mean able to understand the spatial dimensions of where people are at different times is incredibly important because that's going to tell you a lot about their activities what they're doing individually or as groups so that you know and most of the places in the world do have a lot of cell phone penetration may not be smart phones but you're going to get a lot of information from that I think that the the capacity to actually survey people while they're in the field while they're in different environments through these ecological momentary assessments it's another powerful tool for trying to understand where they feel connected what they feel like what their mood affect is while they're in different environments that's going to tell you a lot about the community structure and then you know the cell phones all have capacity to determine proximity to each other so we've used that in studies of parents and children trying to figure out what their joint activity levels are when they go to parks or something like that so you can start to see how these social networks are forming in terms of regular contacts through the proximity of people on their cell phones so those are just some you know initial thoughts and then you know combining that with very high resolution remote sensing imagery is going to give you a lot of information on these points of aggregation points where they are coming together where they're frequenting regularly being able to classify that with with such high precision now is I think another major advance so it's another case where we're going to merge the the sensor data with some other observed data yeah I'll also add to that I think there's some really interesting opportunities to look at the variants we start to see within our own communities and extrapolate that to variants outside of our community so we did some work early on looking at we call semantic signatures or temple patterns to see different place types like coffee shops bars police stations the times of day the habitual behavior people have towards these different place types right through hours of the day days of the week seasons and then you start to to study those you know Los Angeles to Chicago or Chicago to London or to Sydney or even some some other other other parts of the world where you say what's the regional invariance that you start to find these temple patterns right so Chicago and Los Angeles and New York tend to be similar in a lot of different ways but there's some things like theme parks where they tend to be very different in their temporal patterns of how people actually visit different theme parks or or these sorts of things what we found is like about half of the different place types if you're looking about 700 different categories of places gastropubs you name it parks what we have this this millions of check-in behaviors or what you see on Google that popularity time you can see but half of them are regionally invariant right so people go to drug stores in North America always the same time right there's no variance you find between Los Angeles and New York but the times of days you go for dinner very considerably between Los Angeles and New York so it's a very small difference you see within the United States for example but as you start to have better contextual information about people in different cultures and different cities you can extrapolate that to be different patterns in different parts of the world as well and that's just sort of looking at time as one component but there's all these additional emotional components you know spatial components obviously the thematic components you get from the natural language or the language that's used to actually build these signature models that can be used as proxies in different places I think as well thanks great presentations and this is not my area of expertise so it's really been really particularly interesting one of the things that I was sort of struck with you know a lot of my work involves cities a lot of it is sort of climate change related but you know more broadly defined is this issue of the process of urbanization and you know if you look sort of into the future we know that most of population growth over the next several decades are going to be in small and medium-sized cities in low and income countries and I was particularly struck by your work Sarah and others about this issue of sort of bringing the informality into the formal or making things known and obviously in some ways that they're kind of a very the the whole issue of data and the capacity to organize that sort of you know is very transformative in that in that sense and I'm just wondering just a quick comment and then maybe a larger one but what were the implications from you know and I think you highlighted that a little bit folks sort of at the board but you know can you collectively speculate as to what is going to be the implication of transferring you know the informal to the formal or you know that that that boundary you know even Yelp you know estimate I mean we have departments of health giving A's and B's and C's on on you know and all this sort of health inspections and then Yelp is sort of a parallel so I guess that's a broad question the the the connection between the informal the formal and the informal and informality as a as a process in urbanization but then sort of kind of squeezing out and going back a little bit to geography you know I was always struck you know geographers have spent you know different chunks of the past century trying to understand what is urbanization and what are what are cities as well as urban planners and I guess to push you guys a little bit can you speculate you know as the full flowering of this approach and the data and the capacity to manage it emerges how do you think our understanding of cities might change what new questions might come up and what what might be the geographers analytical understanding of cities be 20 years from now or 10 years from now or what what do you see that's emerging so there's two parts the formal informal and then sort of the understanding of the city I'll try to take on the formal informal I think obviously whenever you visualize an informal system like we did in Nairobi or you wonder could you have adverse effects right like could the police come and now shut down the system because it's the visual eyes or does it change you know like how people have basic access to these kinds of systems with the government is not providing right these are market based systems the government is not providing public transport these are providing an essential service every day like what is that effect of that I think something we definitely thought about and that's part of the reason that we included the government in the process from the beginning I definitely think there's certain areas that let's say like visualizing this type of informality could be problematic I think in the case of transport there's a number of benefits one is the Matatu system does run as one organized system they have ahead of the system his name is Kima Tai they have an organization they plan routes together as a private based system that is formalized and by actually visualizing it in this way we allow we are creating a way for the government to have a conversation with them previous to that point the conversation about the Matatu with the government is it's too many people to talk to there's hundreds of operators and hundreds of drivers and hundreds of representatives and like we can't manage this system we can't regulate this system we can't like work with them to do safety things so so one one thing that's happened from this is it's been seen as one system which very much needs safety procedures I mean they're very dangerous they're like number one cause of death in Nairobi I like lack of like problems with this system so that's one thing is that to be able to have that conversation with the Matatu is about how they provide safety and other things has now been established because and and I'm not just with the government I would say more with the NGO community who did not see it as one system that they could fund or regulate or think about or have safety procedures for it's probably more the NGOs that we're talking to here in that case the other thing that I would say in the the case of many developing cities that have this kind of informal transit is that the big push right now is for BRT to come in and to formalize the system and by visualizing that there is an existing system that needs to be worked with is really important because you can't replace a BRT will be hopeful in certain areas but you still need the feeder system and they need to work together so by visualizing the system I hope that as these BRT systems and every city that we've worked in like there's a discussion about having this more formal system and then getting rid of the other one and what we're trying to do is have a conversation that this system exists and they have to work together well you know I think one of the the big challenges when you think about understanding the city is that oftentimes when we observe a relationship between place and say health or other attributes like physical activity you always have to be concerned about what the economist would call t-boo sorting or self-selection bias so this was revealed even you know 35 years ago by Michael Deere where he did all that groundbreaking research on schizophrenia and the city and you know what was happening there wasn't the city was making people schizophrenic because that's where all the mental health facilities were so the people that needed the treatment were sorting themselves so they were close to the facilities the same could be said today if you look at solute and eugenic exposures like green space that people who have the income can afford to buy into those places they may already have a healthy environment so there's a big movement of foot and public health and urban planning to look at so-called quasi-experimental designs natural experiments and the big problem with those experiments is that we don't have a lot of baseline or ongoing information about people's activity levels about before and after you install whether it's a bus rapid transit line or a bikeway or a park or any type of new facility that could be health promoting and you could say the same about detrimental health conditions where poor people get sorted into neighborhoods with low rents because there's too much pollution so I think when we look at the embeddedness of the sensors and what that's going to do for really increasing our ability to understand human movement and the environmental conditions that are there before and after we're probably going to have a much better grasp about how to conduct these quasi-experimental evaluations of the natural experiments that are happening all the time and that's going to increase our confidence and our knowledge base on what really works in terms of urban planning for achieving a healthier more active and vibrant population and economy when we get that kind of information so I think that's going to be a huge benefit and then our ability to take action on those results is going to increase immensely too because we're going to see that the micro scale of the data that we have is so much more refined that when we do want to intervene and we do want to you know take measures to either protect pedestrian safety or reduce air pollution exposures or reduce heat due to climate change increases that we're going to have that information on a scale that we're going to really be able to pinpoint and target the areas where it's going to be most effective when we do intervene my experience in the informal to the formal is that there are often stakeholders stakeholders who benefit from the informal and so when they when they figure out what's going on sometimes they push back on that and because they're not sure they're going to have the the benefits continue to have those benefits right so that's that's one aspect of it the other is and I think Sarah you know mentioned it there are benefits when it once becomes formal it's easier for government agencies to regulate or impose taxes or to you know develop legislation whatever's needed and in a lot of cases that's good but it also tends to increase costs and you know does away with some of the the reasons that it wasn't formal and the benefits associated with that right so you know there is a good and a bad and and maybe the good outweighs the bad but when you think about you know doing that there there have seen that and I've seen that in lots of different industries different technologies that are out collecting data and trying to make it more formal actually the follow-up with this as well in terms of how understanding cities we talked a little bit that's over the break I think this actually gets a bit to the way of identifying communities I think more and more it's when people talked about sort of relational networks and then sociologists have been talking about the network of community rather than geographers who tend to think of as a more continuous space now we have both we have the data to sort of to see this and really a sort of understand a city as a relation rather than a space in their spaces nodes are really important but there are connections of the relationship between the relations between these nodes I think this is some of the stuff we're trying to do we're trying to study gentrification through sort of looking at a social network analysis not not between people but between locations in the city and if people who only went from here to here start going to another location that's an indication that there's some new thing there maybe it's a new shopping mall maybe it's a some sort of gentrification process going on but you can use that to sort of understand how a city might be changing and that's actually I mean again we're still trying to figure out this works but since it's more real-time data than we see at the census we can sort of detect those kind of changes and again for for the community detection some of this can be done quite quote gun down with fairly simple data in terms of call data records I'm thinking some of the work that the people I work with in Estonia do looking at differences between Russian speakers and Estonian speakers in that context they're very different communities they actually tend sometimes they're like living right in the same spaces but we look at where they're where they're going in the evenings or the weekends you can see that they're very different communities they're physically right next to each other but we start looking at some of their leisure time they're moving in very different ways sorry about that this is very very enlightening I want to take a trip to the dark side now though and Sarah in your one of your slides it said you know big data cannot be an agent of change unless it's applied for the public good well that's only half of that there's only one side of the coin it cannot be an agent of change unless it's applied to the detriment of the public which it could be right and I'm I'm thinking about London I'm thinking about frogs right now that may seem strange to you but you know they say if you put a frog in the water and slowly turn up the heat it will allow you to boil it the United Kingdom now has about six million CCTVs for a population of about 65 million it's about one closer could television monitor for every 10 people and originally these were put in of course to start for a crime right I think if you went back to the UK in the early 2000s the late 1990s and said we're going to put one TV monitor for every 10 of you and we're going to monitor that there might have been an uproar right but it's that's become accepted now with Facebook yeah there was 68% people didn't trust them but the numbers of people actually dropped Facebook after the Cambridge analytical thing it wasn't as though they lost 68% of their user base and so in other words we could over time put into place these technologies these sensors and things like that they wouldn't necessarily maybe in every country maybe even in this country be used for the public good so what I'm going to ask all you guys is what is the dark side what is the darkest thing you could see happening in your particular area with the widespread use of this type of sensing and and you know what are the things that we would need to guard against it and I can think in each of these areas of things that could go really off the rails but I throw it out the committee here yeah I mean I think when we see what's what's happening in China and I'm not sure what it's called but it's basically like a social capital index Sesame yeah so basically they're installing even more video cameras I mean they have a lot more people in England but the rate of installation is is astounding and I'm only going on media reports but you're talking about thousands of new cameras appearing every week and they're actually trying to come up with an index that's going to track you when you you know J-Walk you have socially unacceptable behavior in public that you know I think is a very scary prospect because having those types of everyday behaviors tracked in a way that's going to allow the state to penalize you whether you're applying for college or for loans or maybe even have you considered for mental health evaluations those are that's that's such a gross incursion on our rights as individuals that I think that's a very dark prospect and you know I mean it's Orwellian in a sense you know I think in England they have been able to use in some instances to to show that it's helped them during terrorist attacks it's helped them to track when they put in new bicycle lanes so they're having beneficial uses to that but I I think this probably goes back to a lot of the legal protections that are really not in place right now so that's that's a big factor another factor from in my field that's a particular concern is you know being able to track people's mood and their their mental health and even their physiological there are a lot of private things that make people very differentially susceptible to those those factors and that they may not want revealed that they might not feel comfortable with other people knowing and that I think again that information can be used against them in so many ways for employment for insurance for medical care access even for mortgages that that is a particularly dark specter that some of this health information particularly the kind that could be gleaned from the sensors that are coming in the next few years until a medicine if that was abused you know we have in place HIPAA and other ethical review boards when we go to use these data at hospitals and universities but I don't think those protections are in place for data that's going to be out there in the public so that that again raises real specter for abuse I'll follow up a little bit on this and this is also sort of based on some news reports of some of what has been reported about what's going on with some of the social networking sort of almost so let's think it's referred to as a credit score within China that it and the thing at least the way that it's proposed I don't know it's to extent to which this is actually been operating in China but the idea is out there so you know this you know if it doesn't happen China might happen someplace else is that essentially you start your your score and your sort of which might allow you access to a visa to go travel someplace or a higher speed internet or other sort of amenities that you might want is based not only on what you're doing where you might be going what you might be saying but also what people are doing people in your social network are doing and again I I don't have a firsthand experience or sort of direct knowledge of this but that idea where you're leveraging someone's social network to essentially keep people in check and you tell your friend your crazy friend don't don't post that conspiracy theory because that's going to reflect badly on me I mean that's a really insidious power because it's no longer the state doing something directly to an individual it's it's basically maybe weaponizing one's social network to keep someone in line and the extent to which it actually ever comes to be I don't know but that when I think dark side I think stuff like that so I have I have a couple points one when I was getting up to speak I was thinking that I always feel bad as the lawyer that I have to do the gloom and dark and all the bad things that could happen so it's kind of nice to have the have the question another thought I have is that I've heard and and read about all the things that being discussed in China and I don't have any firsthand knowledge either and how far it's going to go and what limitations they'll put in place from either technical or legal or regulatory operational standpoint I do know that one of the reasons I think one of the reasons that in the UK why the CCTV cameras have become so accepted is because they do have a pretty vibrant data protection law around the data that's collected and people feel fairly comfortable that it's being used in a certain way in no other way now I'm sure there are leaks and things along that you know that where it happens and but but in general there's this sense that data is being used the reason that it's being collected and they see the benefits that they don't see maybe with some of the social media and other companies the point about the social networks and how China is you know may use it again I don't have any first-hand knowledge but I was a Soviet analyst during the during the Cold War and I know social networks were a way that people were were punished and didn't get jobs because how who they operated with it wasn't because of who they connected with on Facebook but because of where they went to church or who they hung out with so I would argue that that's been you know been around for a long time this is just a way to deal with that not saying it's right I'm just saying that's that's where we are my greatest concern is that geospatial information is not used to benefit in a way at during an emergency or famine or some other way because the laws and policies that we put into place that make it too restrictive to use and that to me is frankly the greatest threat you know at least in the United States because there are so many benefits and if we don't think about it the right way we're going to we're going to go up too far off a course and we're not going to be able to do some of the things that this technology allows I just want to add I mean I think data has always had this possibility to be used for good and evil I have this talk that I say data for good or evil I couldn't have given that talk but you know like even the million dollar blocks data that I put up at the beginning of my talk you know you could turn it around and say oh where those those are all where the criminals are we're not going to give any mortgages there you know like so I also think like one thing that's important for us to do is how we frame analytic like you know as an academic of course I think about this a lot and it's like how do you frame what you're talking about in order for people to hopefully use it in more positive ways but obviously I mean you asked what are all the negative ways I think almost every single data that could be used for negative and we could probably put one on the wall and come up with like a possible malfeasance and so it's I guess exactly what you'd say is how you put protections around those data sets are the way that it's used that's important I was thinking as you all were talking I don't know if you know the TV show Black Mirror so you guys know if you really want an investigation and all the bad ways that technology could be used in society that's like a really good one but actually I think some of them have come true like there's an episode in Black Mirror where you get certain you get access to certain kinds of services based on what how likable you are on your social media network and those are happening in China you don't have access so they'll look at your social network and they'll see like whether people whether people like you or not and provide you access certain kinds of car services or and so I think you know you're talking about you know what your social network is in terms of you know coming after you for you know police investigations or those kinds of things but I mean it can apply to lots of different kinds of services that we access and can create a new a whole new marginalized society because you're not liked on your social media yeah I was actually going to mention Black Mirror as well they do a much better job of being imaginative for these kind of deemstay scenarios I think but I also wanted to bring up what I think is quite fascinating from a social perspective as well which is that it's virtually impossible for a 14-15 year old kid not to have a social media account right so we can regulate it as much as we want there can be a hundred more Cambridge Analytica issues that 15-14 year old kid is not going to get off Snapchat or Facebook or whatever the case may be and increasingly these are all location specific technologies right so we actually have Snapchat maps and all those things associated with that but what's interesting to think about is is you know we could determine that there were Russians in Ukraine or Crimea based on the social media feeds because they were stopped from sharing where they were but you could not stop a you know 20 year old guy from sharing his Instagram post so there was that location issue but if you think they know three to four maybe five presidents from now will have had social media account from birth be it like a dark profile or whatever Mark is going so whether they have an account or not there will have been a presence for everything that they've done online right so think of the regulatory implications of that who has access to the data that's not government that's storing that data that's private companies that's private industries that we're having a lot of issues with right now and they have access to data that could potentially be used in one of two ways so that's the kind of stuff that scares me okay okay can I actually just add one final one one other fear I have and it's also just opportunity cost because you know the money and the effort and the energy that's spent on working with these sort of data especially in times of austerity especially for government is money that's not spent on other things that have been the traditional way we can have a conversation about which is better but often I worry about that this new shiny sort of data sensor kind of model is pushing out a lot that's actually been proven good you know that and the fund and the money's just not there for for things that have been working fine so I want to add one last evil thing I mean and really I'm not talking about this I think homophily is also a problem like the idea that and this has been in the media a lot but the fact that we will only be provided with one perspective and and one you know based on our social networks and the kinds of news we read and the other people we get one perspective and I think that is really dangerous and we're already seeing the danger of that and last we have no questions back here oh yes my name is Bruce Crawford I'm interested in what happens when this sort of internet of things data gets integrated into official statistics whether it's the United Nations doing it or somebody else like that I'm under the impression that some of these internet of things devices are not exactly precision instruments so I guess the same thing applies when you go to citizen science ideas you've got these people with their homemade sensors you know their raspberry pi projects or whatever and then you have to take a second step and sort of validate or adjust that information to comply with what an appropriate scientific sensor would would read before you put it out as an official statistic or something like that so I guess I'm wondering is that step even necessary should it be something that's necessary what kind of you know biases might be out there in addition to the self-selection bias that people say I'm going to start building these kind of sensors and distribute them to all my friends any thoughts on that you know I guess you know as the example I gave earlier today in the imperial valley we've run into instances where our warning system disagrees with the government's warning system and really you're trying to give people more information not a higher level confusion so we spent a lot of time trying to think about what to do with the data when our lower precision sensors are probably giving a different message now if you've got you know 500 micrograms for cubic meter of pm 2.5 in the air so you're like Beijing on a bad day you don't probably need the most sensitive sensors in the world or during the very large wildfire event we had last December the nearest government monitor was maybe 11 or 12 miles from my house but there were 10 purple error sensors that were much closer I could see when the plume was moving so there are going to be some instances where the the degraded information is still going to supply a useful benefit to the public but I think we have to be careful about the messaging so that we're not putting out two competing messages one that's maybe with more highly resolved spatial and temporal data that's got more air in it and then another one that would be the more conventional government monitors so that's that's a big challenge and but I think you know your your other question about validation and calibration yes if we are going to begin using these for some type of official on decision making we do have to have some understanding of how they would relate to a higher quality instruments and what that degradation of information is going to need you know mean for the types of messages we're going to give out and the types of uncertainty we're going to try to convey around those messages so those are just a few initial thoughts from another comment I don't have any firsthand knowledge about this but I do know that the the UN GGIM which I mentioned earlier actually comes out of the statistical office of of the UN and part of the work they're trying to do is to align the work that's being done within the geospatial agencies at the national level with the statistic agencies and I think you know some of the issues that you've described in terms of nomenclature nomenclature and accuracy and precision and how you can align those are things that they are actively looking so I you know I encourage you to go to the UN GGIM website to check that out because I know they're having meetings and publishing papers and things on that tap so a couple comments then a question so just to find that one exactly like the UN SDGs is another one that right now are being national statistics that they're trying to now get more granular data through crowdsourcing and other sources of you know where the school is actually built what's the reading level to actually then float up and say national reading levels and so on so that's another one to look at so on the first I'll start on the on my commenting question about on dark things I'm surprised no one's mentioned minority reports right it's another really good example of a dark future but I think what what that was demonstrating I'm curious what your thoughts are it was actually more about utilizing all this data to do simulation and modeling and touched on a little bit here I mean there's been a lot of examples where this has been amazing disaster and flood response we're using stream sensors and crowdsource data predict flash flooding inundation putting that out into ways and telling people to route around what might be a dangerous bridge the USGS is doing that now is Earth quick data and I think even coastal erosion data and other things and then using that to influence their predictive models so I'm curious like in urban planning Sarah I don't know if you coined the term or others about social topography about how people kind of you get them getting these these community sheds and where people tend to go at different times of day and then people want to influence that where do we pre-position police where do you pre-position free t-shirts that they give out at parades and things like that to where do we close off bridges if we need to for safety or evacuation so where I'm curious kind of where does it go from here if you have traditional modeling and simulation through traditional sensors you have more real-time data and now you have the social data that can influence that and help you really refine it and let me throw one more buzzword in there about machine learning where you can constantly feedback on those and apply kind of regression algorithms to optimize what role do they have do these fit together are they all kind of do they stay separate in their own little boxes of research and application okay well did you want to did you want to do you want me to yeah so well you know I think the the beauty of the machine learning methods is that they're always based on cross-validation that's outside of your model you know so that they're learning from how well they can predict so when the question is purely prediction whether it's a flood area or a wildfire you know burn that's going to put a plume up they can be very useful and and very powerful and if the information is not informing the prediction that much it will be penalized and not included in the final equation if your cross-validation algorithms are reasonably conservative so that I think is it's a very powerful way to integrate different types of data from numerous types of sensors of different levels of veracity and accuracy so that you are going improve your predictions because basically it's not going to go into the model unless it's able to predict outside of the data that's being used in the calibration equation so I think that there's a huge role for them to play but they're not going to necessarily allow us a greater level of understanding if your intent is to understand urban processes purely predicting where a flood's going to go or something like that is not going to inform you but for these pure prediction problems there's enormous potential and we're already using them in wildfires where we throw in what should be gold standard information and we're always surprised that other variables will pop up and add to that prediction that we didn't think we're going to be that important so I think for those kind of applications that machine learning is very promising Yeah, I think one other thing to remember and I think this is really important when we're thinking about modern modeling is how do we A, think about the missing data sets so I think one thing that happens often as we start to apply machine learning is that the data might not be as comprehensive as we think it is or it's missing a piece so I'm always thinking that when I look at the results what isn't there rather than what is there is like a really important kind of cross-validation technique but I think it also what's important as it goes back to the work that I did in China is that asking people that live there about your modeling results is absolutely essential and we can't miss the cross-validation of human validation I guess I would say and that that would allow us to reiterate our model and I think without it we do have kind of a doomsday we could have a doomsday scenario much in the same way that we have in the 60s in the U.S. where we kind of said okay efficiency says the highway should go here and then we didn't actually ask people on the ground like oh what are these important community factors and so I would say that as we move into these kind of more algorithmic models in order to make decisions about places that we do need to create a loop in which we ask humans to help us edit the model together they don't have to be data scientists but they have to be able to critique the work I think also I mean for me I feel a lot of this it depends on the the application what you're talking about in terms of you know crowd movements or where people are going to be at certain times a day that's a more I guess sort of an objective measure in some ways into the data once it starts moving into I just want to call much more sort of like you know socially constructed data you know really have to start worrying about what kind of you know what kind of biases or what kinds of things that were how that data was built back from whatever however it was originally constructed especially when you're talking about behavioral or criminal criminal activities and things like that are there larger are there larger structural reasons what that might have that might be having the constructed data in certain ways that we want to be aware of when we're moving into that because again it comes back to the old thing that you know data is made it's not just dropped from the sky and that's that's the real sort of care I would take for that sort of approach not doom and gloom but the old although I do some risk and hazards work but you know Glenn McDonald I think just stepped or no this morning we're talking about historical geography and it just I think Michael you're comment there about the data sets and the storage of the data and I guess I wonder you know for you guys to speculate on the value of this data and the accumulation so I guess one is a question of archiving and how it's stored and how it's maintained and and how do you think that will alter or enhance capacity in the future to do historical geographic work to sort of understand what particular moments are because I mean we think about the texture of any moment and so much of that information you know historically has been lost how can we recreate the world of the 1940s how is this data going to help us create the world of the 20 teams in the future I thought it'd be fun I thought it'd be fun for you guys to think about I'll just answer it quick I mean I think it's a great question because I mean it is it's it's something you know the it's the reason that the sort of legislation about the right to be forgotten has been put out there because we we don't have this and I think for me just sort of you know sort of the thing I always struck by is any every time I buy a new computer I copy everything over from my old hard but I guess I smell a lot of it in the cloud now but you know that the whole thing that since I first got my first computer back in the 90s I've I've kept everything and yeah there's a really rich tapestry that one can build from that I mean I think this goes into you start looking at some of the approaches that people are doing into humanities with and well digital humanities more as well in terms of the kind of tools that you know they're starting to you know use well it's not necessarily it's it's not super sophisticated or quantitative but it is looking for keyword frequencies and looking for certain certain references I think yeah it it opens up a whole new realm of kinds of questions and what we can what how we can explore this stuff so yeah 50 years from now it'll be great yeah I guess some of it goes back to this question of being able to pull out those representative patterns so that we're not going to archive all 60 billion data points from the precision medicine initiative every week but what do we need to be able to say over some longer period of time that this is representative of where the people were and what they were doing and and how do we sample out of that huge array and a lot of it's going to go on understanding the variability in these patterns and and what the regularities are so that we are going to be able to represent the longer term with shorter shorter samples because I don't think you know there's any social benefit to store and of course there that does technically preclude your right to be forgotten because you're not going to be forgotten if the if the data are there so I think that's that's a big part of the challenge is trying to come up with these statistical methods that are going to be allowing us to say well what is representative and and how how do we do that we don't have them yet okay well before Bill Selecki or someone else that's another really big question and runs beyond our time frame I'd like to I'd like to ask if any of you would have any kind of last thought or last word or and particularly of interest to us is if there's something what do you see is a is a gap in practice or a gap in knowledge or a gap in research or if anything just jumps to mind here or if you or we even without a gap if you have any parting I'll give you a chance to have a parting comment and then we'll wrap this up I'll just I'll just quickly wave the flag for something that we've been talking about for a while which is making parallels between what we find in astronomy with observatories to geographic information observatories to information observatories where we take all the sensor data that we have access to we curate this data and we start to build these systems where we can actually examine this in either a visual or some sort of information analysis perspective and continue that that analogy to astronomical observatories where we look at all the various patterns we look at what we were able to do with Hubble Telescope and investigate the various patterns there can we build these information observatories urban observatories whatever you want to call them at a much broader scale and bring these data that's together because right now we often tend to look at a small set of these different data sets one group of sensors or CCTVs or whatever the case may be try to extract some meaning from that and they're still missing that holistic view that I think we we should approach as geographic scientists that's life feeling well yeah I think a lot of the sensor technology can be used to make our cities a lot smarter than they are and you know in many ways we're still running on 1950s infrastructure as it's a bus that runs every 20 minutes the traffic light that turns green at this time of the day every three minutes and then it turns sophisticatedly to every two minutes at another time of the day you know there's no real-time feedback that's happening the electrical grid that Maryland's talked about so it seems to me a big gap is in how to translate a lot of what's going on with with the sensors into actionable items that city planners and other government authorities can then say well yeah this is the priority this really works right now and this could result in massive energy savings or more adaptive capacity so I think that's a gap that the sort of translational gap between what's going on and cutting edge of sensors and what what actually works to make our city smarter right now so yeah I'm I want to just go back to what you were saying which was you know I think I mean Kevin was saying you know I think the real disaster is that you know we aren't able to use some of these datasets for public good when we need them because we don't have the regulation the policy in place to be able to use that data and I think that's I think it's a huge issue we avoid it we don't want to deal with the privacy issue it's uncomfortable like we only deal with the privacy issues there's a lawsuit we're not proactive about how this data can be actively used what are we doing with Uber is running on our infrastructure they have this amazing amount of data to make our city smarter but we don't have access to it like what is so that leads me to my next thing which is I think also something that's really important to research is like what are the role of these different private companies in the public forum right so like whether it's an Uber or a Facebook who is acting as a public entity in a way much the same way we think about the Matassus like what is their role in terms of they're acting as a kind of public good in a way but they don't have to act in that way and I think it's something that we need to be considering more and more you know like the conversation around the Facebook data you know I mean we all knew they were using our data right like I mean it's not surprising but I think what we're more surprised about is what is the role of Facebook in that public forum that it's created and Zuckerberg obviously doesn't care about the public forum but I think that's something that we need to research more as they become more more privatized by smart cities infrastructure and those infrastructures working in our communities great thanks I saw a lot of hits nodding at that we're out of time thank you so much thank you to the all of our speakers and to everyone who came and to all those people who are with us virtually thanks


Music Historic events, symbols that capture the energy, emotion, and drama of America's aeronautics and space program have been the essence of 26 years of art, generated by artists who have had an opportunity to experience these events firsthand. NASA's art program continued this tradition recently by commissioning a musician for the first time, along with several other artists. Their assignment documented the return to flight of the space shuttle. Artists sketched, photographed, and even sampled sounds during the launch and landing of shuttle Discovery, collecting ideas for what later became detailed brushstrokes for illustrator Wendell Miner. Died fabric for Angela Mano, or a montage of airbrushed impressions for Michael Kenegan. In all, six artists, steeped in various traditions, took in the shuttle experience and created images that depict America's return to space. The seventh artist, Jane Ira Bloom, captured her impressions in music. Jane is a New York-based soprano saxophonist and composer, and has been a space enthusiast for many years. She enjoys writing music about events that visually inspire and challenge her. How could I communicate the feeling without pointing to it, without actually saying, here's what a launch sounds like, how could I make people feel what you feel like when you hear it? As part of the process, Jane spent time rehearsing ideas with fellow artists in New York. Early on, Jane realized that she didn't want to approach the music as a pure synthesizer school. Instead, she found other forms of expression. I think it's a greater challenge to find new sounds and to find even new uses of live electronics impelled by the players, impelled by the people. Because I started to see when the musicians got involved in experimenting with this Doppler-like spinning idea, suddenly the music took on a life of its own because the musicians embraced it and made it come alive. In jazz, we say the bandstand starts to rise. Jane spent hours writing in her studio, preparing for a live concert at NASA's Chinese Space Center in Florida. It was performed exactly one year after the space shuttle's return of flight and included the traditional unveiling of art from the program. It was a movement of sights and sound, one in which art and technology combine to express an appreciation for America's return to space.


I love firefighting. That's what I want to do. You don't feel the heat, especially with this gear. The outer lining is reflective. I've heard of people's face shield melting before. While we're deployed here in Australia, we go under the title of EFR, Swedish Nairi Fire Rescue. It's an invitation to give you guys more to do. If you guys focus on the fire, we're running back all the way up to the cockpit. Here you go. So say the aircraft crashes. Fuel is everywhere. It's on fire. We'll go and use either foam or we'll use water and PKP. And then after that, after the fire is out, we'll make entry and try and, if anybody is in there after the fire and crash, we'll try and save it. Other times, if it's just like a hard landing, we'll obviously go in there and pull everyone out and make sure they're okay in the system. We're all medically trained. We worry about saving lives and protecting property.