13 Conference Report #techonomy13

Mapping as Platform: Are We There Yet?


John Brownstein
Co-founder and CEO, Epidemico, Inc.

Andrew Gold
Chief Executive Officer, RF Spot

Walter Scott
Founder and CTO, Digital Globe

Richard Tyson
Principal at Special Project Office, and Advisor, UNOCHA, Data Analysis Project (DAP)


S.J. Camarata, Jr.
Director, ESRI, Inc.

Mapping used to be a simple, literal, two-dimensional exercise. Today it’s a four-dimensional adventure that allows us to capture specific data points and sets in never-before-seen-detail. It lets us navigate data, understanding and predicting in ways never before imaginable and realms never before reachable—geographic, molecular, neural. Will new mapping technologies open new vistas of opportunity and progress, or just help us find new places to get lost?

Camarata:  I’m director of a company called Esri. We’re a software technology company, actually been in the business for over 40 years. We started in 1969. We’re a privately held company, which is kind of rare, I think, these days, about over $1 billion revenues and a global company. And we’re in the mapping and geographic information system business. That’s what we do.

We’ve got an interesting panel here. I’ll just kind of quickly run down the people. Walter Scott is the founder of a company called Digital Globe. For those of you who don’t know them, they are the largest commercial satellite imagery company in the world; been around for quite a while, doing some amazing stuff. All the things you see on Google Maps and Bing Maps and Apple Maps, most of that data is from these guys. That’s their satellite. They have their own commercial satellites.

John Brownstein, professor at Harvard in epidemiology, also the founder of Epidemico, a spin-off company, and the head of that company, and also has done some really interesting work with a group called healthmap.org, which tracks disease outbreak incidents around the world using some very interesting technology.

Richard Tyson, from—currently working on the advisory board for the United Nations, doing some interesting work on disasters around the world and what’s happening and how to deal with big data sets. You must be wrapped up in the Philippines stuff as we speak, I would imagine.

And then Andrew Gold. Andrew’s the head of a company—founder and CEO of a company called RF Spot. RF Spot does some very interesting things, initially on indoor location, indoor mapping, but they’ve gone beyond that now to keep track of where everything is, where things are, how do you keep track of that, how do you use it to monitor it. And he’ll be going through some of those sort of things. So I’m going to start by just making a couple comments about—the title is mapping, are we beyond mapping, the platform, to sort of bring up some notions of what maybe people think about mapping or don’t think about mapping. In our world, mapping is more than just a map. And then each of the panelists will give a couple minutes on what they think, and then we’ll just open up and hopefully have an interesting discussion here.

So some people—when we talk about mapping, we are talking about digital mapping. Maps have been around forever. If any of you have been fortunate enough to visit the Vatican Library in Rome and see the mapping displays and stuff there, it’s—do it. If you like maps, it’s really incredible, but mapping—some people call it location-based services, some people call it geographic information systems, some people call it location intelligence.

There’s a whole bunch of terms, but really it’s not just dots on maps or not just maps anymore. Really, what mapping is, it’s in some ways a communication language, it is a way for people to visually display and get context and understand things. It’s also really importantly, today’s maps, digital maps have become the basis for an analytical platform.

It’s one thing to have a map of one—like a street map and you have a map about demographic; you might have a map about sewer lines and a map about water lines and a map about soil and zoning, but when you combine those, you can start to ask some very interesting questions. So the analytical aspect of mapping becomes really fundamentally important.

It’s also a media type that’s universally recognized. People understand when you see a map, you see maps every day in the newspapers and web sites about things. It’s also something that integrates disparate information. If you think of all the types of information that are out in the world today, sometimes they have nothing to do with each other, except for geography or a common location. So the mapping component, the spatial or location component is a way that we can bring all kinds of completely, in some cases people thought irrelevant, but synergistic information together.

At this morning’s talk, there was a very interesting—I don’t know if you were at the breakfast thing, where Tom Malone made a comment about collective intelligence. That was really intriguing when they talked about the collection of the brain power and intelligence of individuals together and how that actually creates kind of a 2 times 2 equals 8 equation. Mapping technology and integrative nature does that. It’s a collective intelligence platform that lets you do things you couldn’t do before. And it really lets you leverage relationships and lets you do things that previously may not have been available for you.

There’s been all kinds of numbers thrown at you. You may have heard some people say 80% of all the information in the world has a location component to it. I don’t know that’s real or not, but I use it. It’s interesting, that you can kind of do that, but the—I guess the main thing to think about is everything and everyone is somewhere at any given time. And if you think about maps, maps become the framework to keep track of that.

And so there’s been a lot of talk the last couple days about things, and everybody also can become an instrument or a sensor. Every one of us that has a smartphone, whether we like it or not, we are being tracked and we can be tracked. So we had this notion of something called an instrumented universe, where everything is tracked and located, and you start to fuse information together. And that’s kind of what we’re—what’s exciting about this.

So with that, let’s go around. John, if you want to give a little bit about your background, what you do, then we’ll go in a circle.

Brownstein:  Great. Thanks. Great to be here. First time at this conference. So my background, I’m an epidemiologist, so I study disease at the population level. And so the basic tenant of epidemiology is what, where, when. And the where part has never really been that much of a focus until maybe about ten years ago. The where was sort of how do you place a disease or a case on a map and basically leave it at that; but of course, geography underlies so much around health risks. So whether it’s chronic disease, like asthma, environmental contamination, infectious disease, which is sort of my background, location plays such an important role. And despite that, there’s not really been a lot of focus, a lot of understanding of the spatial drivers of disease risk.

And so when I went to do my Ph.D. work, that was sort of the main focus, how do we create risk maps for disease, and it was difficult to get that kind of data that could drive those kind of risk maps. I did my Ph.D. on lime disease to start, and I have to go out in the field to collect my own data, because there wasn’t a lot of data out there; quickly realized that collecting your own data is difficult. And I actually got lime disease, and so it was like I’m going to take a step back from field work, and then sort of think well, I could ask the government for data. And that was also a problem.

It was either data out there—I was also interested in West Nile virus. No one would share information, even if it was there. And so we sort of came up with this concept that there’s tons of data online through various forms, communications among clinicians, public health professionals, online news, that if we could sort of organize that mass amount of content, we could provide insight into the population, into the health of the globe in a way that had never been done before.

And that’s when we created healthmap, which was this global online disease tracking system that’s based on, of course, world map. And we have—if we had more time, I could explain sort of how we were able to detect SARS early or track H1N1 or H7N9, more recently. And the incredible, vast amount of data that’s being generated now, unlike sort of when we started the project seven years ago, where we had incredible amounts of social media, people describing illness, we worked on a project called Google Food Trends, as an example as well, and now we’re sort of moving that forward to the—you mentioned the collective intelligence.

So we’re spending a lot of time thinking about crowd sourcing and how we engage the crowd in terms of providing health information. And that engagement does two things:  It provides data in rapid realtime form that we can then generate back to everyone else and provide realtime surveillance, but it also provides awareness to people and makes people more actively engaged in their health.

And so we have a number of different projects. Our app, Outbreaks Near Me, which was like the first public health app, which told people of the outbreaks in their neighborhood, also engaged people to contribute—or Flu Near You, which is sort of global watching, now national crowd sourcing for influenza surveillance. And so that kind of data’s now benefiting the general public. It’s benefiting clinicians, who actually help improve decision-making.

And there’s a number of great examples around the house of the geo-localized information on infectious diseases, helping decision-making, of course; is helping the government as well. So we have a number of different efforts in this domain. And just because we’re in Arizona, a few different examples, like there’s an E. coli outbreak from salad that’s happening nearby.

From another one of our products, if you were interested in purchasing morphine in Morana, $10 for a 30-milligram tablet. That’s from another project called Street Rx, where we’re crowd sourcing black market supply and demand of opioids and other products.

If you go down ten miles to the nearest Walgreens, you can get access to four different types of flu shots, shingles vaccine. So the idea is that—and actually, 1% of the population of Tucson has flu-like symptoms right now. And so the idea is that we’re sort of tapping into the interest of the crowd to engage, provide data, and then providing some reasonable amount of information back. And that’s sort of the idea that geography underpins our health, can get—that sort of summarizes the work that we do.


Camarata:  Andrew?

Gold:  My name’s Andrew Gold. Thank you for having me. I spent the last six years working almost exclusively with Google on building their public facing wireless networks. And I realized that one of the challenges that we’re having in building networks to try and bring people resources like the Internet is that the indoor world is very obscure.

So people spend—at least in the United States, they spend about 90% of their time indoors. So what we realized, mobile devices don’t have the same functionality outside that they do inside. And the only reason—the only way that was really going to change is that there became services or companies that took on the challenge of going indoors and truly trying—companies that would truly try and understand what dynamics exist there versus the outdoors and how to extract information on those environments, to leverage those dynamics to enhance and provide new types of services indoors, or specifically services which could be on par with outdoors.

And so we set out on our own challenge to try and build specialized equipment that we could take indoors, which would allow us to extract meaningful information from those environments, and that information could be used, A, to create new types of mobile services for mobile users; and B, they could be used to create new types of efficiencies for enterprises or new types of efficiencies for any type of vertical which has large-scale indoor environment.

And so it’s been a very interesting ride for us, and I would say it’s accelerating. We’re working really hard to create very inexpensive sensors that we can hand to every building owner across the world and have them collect their own data. We geo-spatially orient that data, and we provide them with different types of analytics and different financial feedback to help them improve infrastructure, improve various business processes, and so on and so forth.

I think what’s particularly interesting about mapping to me is that mapping is really a kind of diagrammatic view of an area, and the spatial representation of relationships between elements, any types of things indoors—for us, at least. Outdoors for many others—but what we’re finding, though, there’s really a time element to that as well.

So even indoor environments change over time. What changes—we try and understand what changes are occurring in these environments, what are the relationships of various assets, our people to their environment, and we’re starting to embark upon ways to improve working conditions for employees, just simply by looking at spatial representation of how desks are placed, what are the lighting conditions in an area, what are the RF conditions.

Do you get cellular signal on this side of the building? What network do you get cellular signal on? If you use a mobile application, how’s that mobile application function across an environment on a spatial basis? Where do you have excessive latencies and how does that affect productivity? For retailers, where are your products located? How do you get people to the products? How do you get products to the shelf?

So it’s really grown into a system that will allow the general public and any enterprise to collect data very quickly, and then to look at the financial impact of all the dynamics that are going on in any particular environment. It’s been a very exciting, very interesting ride for us and, as I said, things are accelerating. So I’d be happy to answer any questions, if you have them.

Camarata:  Walter?

Scott:  Thanks. Also my first time at this conference. I’m Walter Scott with Digital Globe. And as S.J. says, we operate a constellation of five satellites, collect the largest amount of high-resolution earth imaging data that’s available commercially. To put that in perspective, we do about 1 billion square kilometers a year, which is something like six times the surface area of the planet. Some places are more interesting than others, so we look at them more frequently. Others, probably less interesting.

In addition to the collection of the data, I think we’re in the middle of three trends in mapping that are accelerating:  One is mapping is becoming more realtime. It’s much more interesting to know about how the world is than how the world was. And the how the world is changes very rapidly.

A very good example, current events, is the typhoon hitting the Philippines. We’re involved in a crowd sourcing effect. In fact, if you go to the web site, digitalglobe.com, you can participate in the crowd sourcing of identifying the aftermath of the typhoon. Second trend is that mapping is slowly eliminating the white space.

We heard an example of indoor white space, which is being eliminated. The other part is the white space outdoors. The spaces between the roads and the buildings contain lots of useful information. For example, John and I were talking earlier, last night about the habitat for lime disease as something that you can determine based on looking at the landscaping in a particular area, or where are the tires that are sitting on the ground that might be breeding grounds for mosquitoes.

Those are things that previously nobody thought to map; or if you thought to map, you would realize that you needed to map it only after you realized you needed to map it. Where we are in the middle is starting to collect data that is being converted into information that is well in advance of necessarily knowing what you’re going to use it for. And that really leads to the third trend, which is the ability to mine stacks of geospatial data that are hundreds or thousands of data items deep that are there mostly because you can create the data.

And you will find patterns that are useful afterwards, predictive geospatial analytics. Digital Globe has been involved in, for a number of years, looking for patterns like where are copper thefts likely to occur. We were supporting an east coast power company to help them manage their copper theft problem by looking at stacks of geospatial information and finding correlations that helped them reduce their copper theft by about 50%.

Camarata:  Richard.

Tyson:  My name’s Richard Tyson. I have a small consulting concern called Special Project Office. And for about the last, I guess, three years, I’ve worked mostly in public sector area for the UN, starting with Global Pulse, which is a realtime data lab; but also in Washington, D.C. for a group called Caerus Associates, which works in conflict environment.

I want to add a kind of fourth trend to mapping, and I want to sort of draw a distinction between—so when we think about the platform. I think we’re in this kind of weird place where the crowd, people, are coming into mapping in a way they’ve never been able to before. So open street map, the ability to do, with Yolanda, with Esri, GIS Mappers, Standby Task Force and the digital humanitarian community, you’re able to actually crowd source information in a really interesting way.

And what this revealed, at least in the work that—and there’s sort of three interesting examples that I’ll bring up, which is to say what I care about or things I think about are not—let’s call it the way the formal instrumental, you know, power of mapping is increasing, which is absolutely true.

We can know where things are. In the Iraq War and in the Afghanistan War, we could follow every single vehicle, 24-7, in and out of any particular place; but what’s interesting about that was what it meant when cars were inside a wire or outside the wire, and when they were inside the wire and when they were outside the wire, which was actually social information about the safety of their environment. So there was this proxy stuff to learn.

And what’s powerful and interesting about the work that I think about, such as disaster or city building or informality is the way in which mapping platforms are changing people’s access to their own agency, how they see and situate themselves in their place, and how they get power.

So I’m going to sort of talk about three quick examples. Obviously, the first we’ve already talked about, Yolanda. So now we have, for the first time, a crowd sourcing effort that isn’t just being done at the edge by crisis mappers. It’s actually UN OCHA, and Andrej Verity is actually coordinating that effort at OCHA with the edge.

So the edge is now formally being connected in order to use this crowd, in order to map on the reference. So that’s brand new. And we’re really excited about that at OCHA, we’re really excited about that in disaster and disaster response.

Second I’m going to bring up is the Libyan conflict or the Libyan Civil War and how that particularly happened. What was really interesting about that, in the same way that we had a proxy mapping of crisis, we also have proxy command and control, you know, in resistance.

We have the ability to use Skype and anonymous re-mailers and other kinds of things to move people around just in time in order to prosecute a resistance of some kind or to organize in some way. What’s really interesting about that is that to reinforce how maps have always been used, very geostrategic at the beginning.

They’ve always been a very strategic asset, like who—in the 18th Century, 17th Century, having a map was a very powerful thing to have, and it continues to be a very powerful thing to have, and it continues to be illegal in some places to actually have them. And within that, that fear of being able to have control over where you situate yourself is also contested.

Even if we can know that there’s a well in a place, what that well is called by the UN or by the country or by the people who were defeated who still live there and still call that well by another name, that contested space, the contested social space of; what we call a place and what we belong to, what these sort of social mapping tools are teaching us is both the power of maps and the fact that the maps don’t converge on a truth about the world. They converge on some facts that we can then fight about.

Or in conflict, a line is a terrible thing, because a line, you can fight over who’s on one side of it and who owns what on either side of it, whose well is it, et cetera. We have been thinking about what it would mean to make a fuzzy map. Could you actually make a map where the boundaries were deliberately fuzzy so that there was—there’s negotiated territory over the line, it’s somewhere in here; therefore, we won’t fight over it, because it’s fuzzy.

So that leads me to the next and last sort of two related things:  One is some work we did in Liberia using leaflet and open street map and an application to be able to print physical maps, paper maps; because one of the things that people—we were working in the West Point area, which is the slum in North Monrovia, and with the police.

And we were trying to figure out how do you get the population to understand or to say where things are happening, where are things, where are the reference points, because these were completely unmapped areas, because nobody goes there and has no reason to ask.

And so we would then have to use paper maps to go in and do collaborative social mapping of where people feel safe and when they feel safe, and how they feel safe and where those boundaries are; at the same time, working with the police, so that when things happen, they can actually know when and where they happened.

Then we can begin to correlate what the population thinks and what the police think, and then go through that in a paper-based way, and then turn that into essentially shapefiles that could then be downloaded in Esri and managed, which is all to say that we’re entering this age where making the invisible visible through maps is a super-powerful and super-interesting thing, and it is places—I won’t even say names, but physical things that we can see and argue about the name; but it’s also movements, social patterns, beliefs, where one person feels safe because they’re from an ethnic minority and there’s an edge, we can map the edge now in a way—not from space, but from the perception or from the motion of the humans.

And as we get these powerful tools that will now make the social visible in new ways and collectively visible in new and interesting ways, we have a whole bunch of new challenges. The military tried to do with HTS and HTT, so the human train system, human train teams, where they were literally sending out social mapping teams to try to figure out where people—what languages people spoke, where they were.

And because of sort of the social diversity in many of the places we have to operate—I say we. At that time it was with the client, the U.S., but I think one of the things that mapping will teach us is how much more there is to be mapped and how, even when we get to the perfect globe, all of the perceptions and movements and patterns and Internet of Things flowing through that space is going to continuously call for more analytic attention and people are going to be more a part of that.

And I think that’s really interesting and powerful and also terrifying and dangerous to some governments and also terrifying and dangerous to some companies who don’t want their effluence measured and managed, et cetera. So informal—the platform providing access to informality is a tremendous shift that we’ve seen, and I think it’s going to amplify all of this here.

Camarata:  So it’s great hearing these different things. I’ll make a comment, then we’ll pretty much have a discussion going forward. So what—the other thing I wanted to point out is you can think of mapping, again, digital mapping on a continuum.

At one end, there’s simple consumer maps. Pretty much everybody that has a smartphone can look at maps on their smartphone and do cool stuff. You can find pizza parlors or how do I get to—from my house to my—drop my kid off at school, a whole bunch of very simple, incredibly powerful mapping tools.

And the continuum kind of goes all the way up, but it’s a very powerful, high-end enterprise; analytic tools that help run wars or disaster relief and things that involve complicated infrastructure, complicated maps, complicated data streams, all that sort of stuff. And there’s a whole bunch in between.

And what’s exciting, at least from my perspective, is that that continuum is allowing for lots of things to happen. There are literally hundreds and hundreds of what we call vertical sectors where mapping becomes important. And in fact, pretty much everybody that’s spoken here at this conference the last couple—yesterday and today, my mind is thinking can they be using maps or map analytics, that sort of thing.

And the other is that—because mapping platforms are becoming relatively ubiquitous, there are going to be thousands and thousands of people that are going to start to develop—and it’s currently happening, thousands of applications and people building things we never thought about. So again, I think it’s a great time for all the things we have talked about.

So I just wanted to get that out. So at this point, let’s open it up, and comments or questions? I think we have to hand the microphone, and because they are recording this, if you would please just state your name and your organization, then we’ll go ahead.

Strum:  David Strum. I’m a freelance tech writer. So S.J., last night at the reception, you were mentioning all sorts of weird-ass corporate applications for maps, and I’m wondering if you could share that with the audience.

Camarata:  Can’t remember. We were drinking a lot of wine last night. Yeah, I mean, we’ve got a lot of Fortune 1,000 customers that do some very interesting things. For example, the OnStar sat thing, if you ever rent a GM car and you push the—OnStar keeps track. It says I’m here, unlock my doors. My keys are in there. That’s all run by a back-end mapping infrastructure cloud-based system we have, for example.

Starbucks locates all their restaurants, McDonald’s locates their restaurants. And when the economy is down, they actually close the restaurants. Kind of like growth and retraction. Same with General Motors. General Motors opened up all their dealers and they looked at cannibalization.

So there’s a lot of corporations now that are actually doing some intriguing things with mapping that just weren’t done before. A lot of it has to do with the integration of different data sets. Andrew was talking about—if you think about it, he mentioned in one of his little points there, integration of data; talked about financial information, people information, inventory information, information about where things are and how they’re set, the interactions going on.

So again, businesses are doing things they just weren’t doing before. Maybe they did them before, but they did them by hand or it took a lot of time, a lot of resource and efforts to do that. So kind of intrigued by the new things they are doing.

Zak:  Paul Zak from Claremont. Ask S.J. or Andrew about analytics that go with that. So take the business example. So I’m going to map extensively where people are, what they are doing, the space they are in. What are the outcome measures you’re looking at? What are the leverage points you’re right to push on and what kind of analytics are you using to assess those?

Gold:  Sure. Particularly, some of the things we’re looking at, just trying to create new efficiencies. And so how assets are moved throughout an environment are important to us. So when and where you use these assets, how these assets move from one part of a building to another part of the building, and then how workers are moving throughout a facility over time.

We’re looking at new ways to create direction for particular employees, or even, whether it’s someone just navigating through a hospital, trying to understand how do you move the patient, how do you move a doctor from one part of the hospital to another part of the hospital. Is that doctor going to be connected when he’s in that particular part of the hospital, and giving them feedback through mobile interface, so that you can continuously work with them to try and create new efficiencies as the day goes on.

As far as the metrics go, I think at this point for us, it’s primarily centered around time. What is the usage of time. And that can be from someone that’s loading shelves all the way up to someone that is a surgeon. How does that person use their time as they’re working within that facility. And so the easiest distinction for us is how that directly correlates to cost.

Unidentified:  Sometimes time’s a very bad metric, so some of the work we’re doing, S.J. knows about in Las Vegas, has to do with providing spaces where people are slowed down or they bump into each other, where they talk to each other. So we actually want to put sort of clogs in, if you will, because we want—measures there are things like new ideas, innovation, patent.

That’s not doing something faster. It’s doing something differently, and I think—so the analytics we’re using which require sensors—you guys must use sensors too. Kind of like everything modulates on the inverted U curve, right. So a little is good, really good and too much or too little is bad. That’s what we are trying to find, kind of that intermediate point, and just wondering if you have done that. Sorry. Only a question I care about probably.

Gold:  That’s a great question, and I think that you’re pioneering some new ground there, to be honest. I think that that’s not well-understood, and I think that as time goes on, there are more people that I think are starting to hint about things like that.

I mean, I think that in some sense, you’re one step ahead of us on the psychology of individuals within a space, as they pertain to each other, between individuals; whereas I think we’re looking at environmental factors like space and light and temperature and sound.

So I think when you take those two elements and you put them together, now you’re really getting somewhere, because you have the physical aspects of the environment and you have the psychological aspects, and the feedback loop that you are looking for from people to really understand well, if we make a change here or if we mash people together, is that making a positive impact on this group of people. So I’d be very interested in talking with you about that further.

Camarata:  The other thing, I think also, the metrics or the measurement, it differs from each individual user of the technology. So everybody—you could have some standardized measurements I think you’re talking about, which could set baselines, but there’s also each individual organization. As they apply the technology and the use of it, they have different outcomes they want to achieve, so their metrics may be different.

Just a simple example, we have been working with salesforce.com, and one of the things they’re interested in is every salesperson who uses their CRM has a mobile device. They want to know—the manager wants to know how close are they to the customer, if they are driving in downtown L.A., getting close to the customer, they want to know about that. Then the outcome is how effective were they. Did they generate more sales and more revenue?

So they tie the location of where people are to where they’re supposed to be, and then they look back at it in terms of keeping track of did that actual workflow and process of the salesperson result in X number of increased revenues. So again, each one can be a little bit different.

Brownstein:  Just to follow up on the—we did a project actually at Harvard talking about sort of mapping professors and faculty across the campus, and we did this research where we had students with GPS units that tagged the location of every research facility across the campus, then looked at the location distance between PIs.

And what we found was incredible relationship between proximity of faculty to the impact of the work. So the closer to faculty people are together, the higher impact the publication, the co-authorship publications that resulted. So we got much more citations by—for those publications. The further off they were from each other, the less impactful the work was. Then eventually it tapers off, because you get into sort of multi-institutional collaborations that end up having more impact, but we’re looking at all sorts of different metrics in co-location.

Yes, it is. I can show you the paper.

Scott:  Yeah, the thought that this discussion about measurement triggered is that it’s sometimes difficult to know what exactly it is that you’re measuring. You may think you’re measuring one thing; you’re actually measuring something else.

An example, if you’re using mobile phones to measure traffic, what you’re actually measuring is mobile phones. You’re not measuring traffic. You are measuring traffic where there are mobile phones or traffic where there happens to be use of this particular smartphone app.

So as we think about sources for mapping, the fact that there can be multiple ways of measuring something is actually a good thing, as opposed to a bad thing. And that gets back to the point you made earlier, which is when you have multiple names for a particular location, that’s—that is, in and of itself, information. It’s not actually conflict. That’s useful information.

So I guess if I were going to add a trend to mapping as a platform, if the proliferation of many different ways of looking at something, that actually gives you a better picture of what it is that you’re looking at than if you look at it from a simple direction.

Camarata:  Here. Go ahead.

Miggins:  Hi. I’m David Miggins. I’m an independent consultant, open data, that kind of thing. I really like Richard’s example, the well with multiple names, because I think it calls attention to something that we sometimes forget with mapping, which is it’s not just providing data for people to make decisions. Mapping reflects decisions.

And one thing I wanted to ask the panel about, being a panel of experts, is how you handle the abstraction of the zoom-out problem. As an amateur, the two ways I can think of to approach that are exclusion, so as you zoom out on a typical map, first Tucson disappears, then Phoenix disappears. Then all you see is Los Angeles.

Or aggregation. So on an epidemiology map, you would initially see where every case is, street by street. You zoom out, you see a big icon showing the number of cases in the city, and so on. Those are both obviously important and political decisions. In other cases, it can really matter if you’re mapping a whole lot of things in a humanitarian crisis. What do you start leaving out when you zoom out? So I’d be interested, what other kind of thinking have there been in the fold about the zoom-out problem?

Camarata:  I can just briefly touch on it, from being a software vendor. You touched on something that we have unbelievable amounts of discussions on in research and development labs, both from a technical perspective; what’s right about map generalization or—but also especially from like a political—I mean, National Geographic is one of our customers, and Google, they are super-sensitive about—the issue about the border line is really critical. The line between Pakistan and India is potentially going to cause wars. So those things become real critical.

From a technology standpoint, you’re constantly looking—data is valuable at certain scales, and so there’s a human factor that gets involved from the science of cartography and when do you generalize maps when you get to a certain level, what threshold. There’s also an artistic component to it from the standpoint of what looks good. You can create a map of information, but it becomes useless because it’s so garbled with so much information and overlapping names and stuff that it doesn’t make sense.

So part of it is there are smart algorithms that are being used more to allow automated labels and to do line generalization and those sorts of things. You were talking about fuzzy maps. We have something called fuzzy logic, where we actually have algorithms that use some different techniques to align lines and do things called compilation. There’s all kind of stuff like that. So it’s part science, part art.

There also is—tends to be at some point you need some sort of curation. That’s where—this is where crowd sourcing gets important. When all these data sets are coming in from things like open street map or different places, how does it get curated so you have in the end the most authoritative quality information that’s there?

So there’s different things. We could spend all day on this subject. Great question, yeah.

Brownstein:  I’ll just be super-fast and just say that to that point about you got to bring the analysts back in. So the map does something, and then the analyst comes back in, and there’s this feedback loop between what the analyst needs, what the map needs to do; in every field, intelligence analysts or et cetera.

And the thing that I think is interesting about this moment in time, social, mobile mapping platform stack is that the human is coming back in in gangbusters, which means that the psychology of decision-making, the psychology of what questions we want to ask, do we want to start a war or not.

Okay, well we need a fuzzy line. And we need the fuzzy line to be fuzzy all the way up until the time it’s not, unless we are giving it to a different analyst, in which case here’s the red line that causes the war if it’s India and here’s the red line that causes a war, et cetera. Here’s what—here’s the outer risk of these two lines. And then you’re measuring the risk of the decision.

So at the end of the day, human—these maps and algorithms are not going to replace human’s ability to analyze the situation. They are only going to inform better-framed questions, and I think we’re at the end of the age where we think—at least I think we think the technology is going to do it for us.

Scott:  So to build on that, realtime is not just about collection of data. It’s also about the interaction of the person using the map with the data. And how many people saw the opening session here where you saw the bubble chart that was moving along with life expectancy and income as a function of time?

And you look at that and say what’s the question you are trying to answer. You might be interested in tracking the trajectory of a particular country, or what you might be interested in is, if you happen to notice, as that chart was moving along, there was some interesting outliers. There were situations where life expectancy dropped dramatically. How would you pick a single way of representing that in a way that was common across all the users?

Well, the answer is you can’t. Instead, what you can do is you can provide mapping tools that allow people to ask questions of the map and get the answer that’s relevant to them. And I think that’s actually one of the powerful parts of mapping technologies and mapping platforms, that they actually allow you to pose your own kind of questions.

So to answer the aggregation question, what’s the question that you’re trying to ask? And if you have powerful enough tools that allow you to effectively describe your preferences for aggregation relevant to the problem that you are trying to solve, that’s the way you are going to get there. There’s no one size that fits all.

Gold:  And whether you’re color blind or not. So quick thought. This concept of human in the loop, I think, is really critical for the mapping space, not only on the analyst side, but even on the data collection side. So one of the things that we’ve done to try and ensure that a human is in the loop is we’ve—we are actually building machine vision software that requires a human to operate it.

And the human is constantly training it, so at the end of the day, you have a much better data set, a much cleaner and high-resolution data set by using a human to inform the computer and inform the software as to what is acceptable and what is not acceptable.

And I think that moving forward, that’s really setting, in our particular business, building the pathway for robotics and moving robotics into these indoor environments. So I think ultimately, having a human in the loop on both sides of the equation, the data collection and on the analyst side, is critical for us particularly.

Krikorian:  Hi. I’m Jason Krikorian, from DCM Ventures. There’s been—it’s been said a couple of times, or a few over the last day, that people don’t care about their privacy. We think they do, then they don’t, but location is one of those things that’s actually particularly sensitive. I’m really interested in it.

I was just, as folks spending a lot of time thinking about location and mapping, if you have thoughts around the evolution of people in social settings, as well as maybe professional settings, like your example of the salesperson being tracked, if that’s evolving and where people’s sensitivity level is now. How has it changed and how might it change going forward?

Camarata:  Yeah, I mean privacy’s a real issue. And you probably read the news:  Google got in trouble for street mapping and that whole thing. It gets real important. There’s a company called AirSage that is based out in the southern part of the U.S., and they’ve got a deal worked out with Verizon and a couple phone companies that every single time a cell phone or a mobile device is used, they keep track of that location. There’s 2 billion transactions a day.

Well, that becomes valuable information, but it’s got to be anonymized. They don’t want, we don’t want people to know every time I use my phone, whether it’s a text message or I looked at a web display or whatever it is, so the —so that’s one thing that people are looking at, is how do you create that—keep that privacy set with anonymization, meaning that they know that an individual was—and you could anonymize like a street level scale geography or different things like that. That’s one way.

The other is how do you actually protect it in the opt-in, opt-out sort of thing. I know we spend a lot of time in the privacy issues and what’s important, and there’s also the challenge between the base information of the map itself and the derived information that results from that, because there’s where it becomes potentially more invasive in the privacy.

When you start to combine all these things and you then make a decision, you have an outcome that is different and some people might want to know becomes real important. And then just privacy. There’s a partner we work with, interesting company. They developed an app called uKnow. It’s about do you know where your kids are.

And they actually have a smartphone app that the kids’ phone—installed in your children’s phone, and the parent on their phone, on their web site keeps track of—create these little geo-sensors. My son’s supposed to be in school this part of the day and he goes to soccer practice here and he goes to his friend’s here. And it knows when they go in and out of where they are.

So that’s keeping track of your most valuable possession, your children, and you don’t want anybody to know about that, except for you. Of course, the 16-year-olds don’t like it, because it’s like—supposed to be at the football game, but they’re partying. Kind of a funny thing, but that becomes real critical. So how do you protect that privacy? And it’s something, from a technical perspective, everybody’s working on, as well as from a sort of a societal cultural thing also.

Gold:  I was going to say, I think first of all, users need to know how their data’s being utilized. And so being opted in is critical, in my opinion; but not only the fact that it’s opt in, but for users to have a deeper understanding of how the data’s being utilized, where it’s proliferating. And really new metrics need to be created to track how your data flows from one enterprise to another and how they leverage that data to make decisions internally.

So I think it’s—as time goes on, we are going to see much more personalized metrics associated with the data that’s being captured on individuals. And in addition to it just being anonymized, I think allowing us to see the pattern of how that data’s being utilized and allowing us to really understand do we want to participate in that or not.

Camarata:  Other question? Or comment?

Brand:  Stewart Brand, Revive & Restore. Where do I go to find people who are looking at cognition and maps? Actually particularly interested in so-called mind maps, and the zoom out, zoom in question made me realize I need to either read or talk to people who can help me think about how to really inform cognition or give cognition—we’re trying to understand, my sense, I’m trying to mind map a set of complicated issues and evidence related to them.

And I want the ability for somebody to look at it from a distance and sense the whole picture and be able to zoom in, and then focus on individual arguments in a way that gives them a sense of the extent of detail that is being worked out in this area, this area that’s being mind mapped. Who’s good at that and how can I catch up?

Camarata:  There are some very interesting companies. Heard of a company called Applied Minds? Danny and those guys are brilliant people. They are doing some very interesting things. We did some early work—that’s a person we know who worked a lot there. He’s got some brilliant—so they are doing some interesting stuff—I don’t know if they’re doing—those are people who think like that. They have very, very prolific or innovative kind of thinking kind of stuff.

You guys are doing some interesting stuff at Claremont. I don’t know if you are getting involved in that from a mind mapping perspective and the neural aspects of maps and how it applies to human interaction. Is that something you have seen in the neuroscience field?

Zak:  We’re just starting with that actually. The science group is in that heavily for ten years at least, so they would know much more about that than me. We’re trying to do this to map how cities evolve, and so that’s kind of a little part I have, which we think has a big neuroscience component to it; but no, it’s a big group there. That’s all I know.

Camarata:  Yeah. There’s a man here, and I can’t think of his name, who’s spoken on these panels a year or two ago, from the Santa Fe Institute. He did an intriguing presentation. I think he did it last year or the year before, and it was about—he does a lot of work with biology and chemistry and has looked at the kind of biological, biophysical things that happen in nature and how that actually affects human spatial and global patterns of growth and things.

Really intriguing. I think it might be Jeffrey, yeah. I saw him—we talked after the intriguing presentation that dealt—touching the realm is what you’re talking about.

Scott:  I don’t have an answer for your question, but it triggered a thought, which is—we talk about measuring the real world and building maps. There’s actually data that we can collect on how people use maps, and that may be one of the best ways of answering the question, which is now that there are these interactive mapping tools that are around, try to begin answering the question about how do people apply their own abstractions. What is the way in which people interact with maps. So it’s metadata about metadata. There’s some level of multiple abstraction there.

Gold:  One quick thought. I have an engineer that works with me who was a brain research—his prior job was working at a brain research facility. And so I guess I have the lucky insight of having someone internally within our group that understands how the brain actually stores information.

And so I wondered if your question was related to how you extract the data that’s already in your head to a mind map and visualize it, or whether it was reverse, as to how you ingest the data and store it in a 3D volumetric sense in your brain. So it was kind of—I have a question back for you. Which is was it; the first or the latter that you’re curious about?

Brand:  Well, I had not realized until you asked the question. It’s both. I really want to get it going and coming. And I like the idea of online interactive map is to watch how people click around and zoom around and does that seem to lead somewhere. And that may be one way to analyze whether the mind map is actually working, is to see what people do over a short period of time with one, but I want to know people have already been studying this, especially in terms of what the brain is doing with this kind of stuff and what it wants. What the brain wants probably is part of what we’re talking about here.

Camarata:  Have you seen the app called Mind Map?

Brand:  That particular one, maybe, maybe not. I’ll look—

Camarata:  It’s more of a thing that lets you do exploded diagrams, instead of doing kind of typical charts you might do.

Brand:  Used a couple of those, and I like them. And I’m going to be expanding with them, and I’m looking for a really good web application of that kind of thing that’s extremely flexible that I could really conjure with.

Gold:  Not sure if Mind Map is a web app, but it’s excellent.

Camarata:  It’s something we’re thinking about, too, at Esri. We should talk off-line.

Gold:  Also take a look at functional MRI. It’s basically watching how the brain responds to different inputs on a spatial basis.

Camarata:  Yes.

Ortiz:  Thank you. Pedro Ortiz. I work for The World Bank. I am very interested—I worked in Monrovia as well. The lack of information in the society is incredible. $1 a day as GDP. You only think about what are you going to eat tomorrow, not anything beyond that.

And the problem with all these societies—and not only Africa. Latin America—is that 80% is informal. And by definition, what is informal; there is no data about it, because informal doesn’t have the capacity to—so mapping the informal is extremely important, but in a mechanical way, because if you have to start asking in a slum in the north of Monrovia people, and then the next slum and so on, there is no way.

And obviously, there is the informal has—absolutely necessary to incorporate the informal to decision-making, because when a society, 70% is informal, if you don’t take into account the informal, you are losing 70% of your potential for development. You are only working with 30%. So it’s absolutely essential.

And mapping is a way, but there is a problem with informal. There is the aformal and the anti-formal, which is clearly criminal activity. So where you put the limit to that, and then it’s privacy on that; because privacy—many Americans tell me I don’t mind my privacy, because I’m legal, but you are legal in a government which is legitimate, but many governments are not legitimate.

So using that privacy within a legitimate government is extremely dangerous. Checking where the doctor is at every time, depends who is checking that, that I would agree or not. So with all that, what problems do you see, the future of that kind of development of informal, privacy, and so on and so on?

Tyson:  If I only had an answer. So I’ve been—all these things that you mentioned are incredibly important. I think the most important one is in many of these societies, when maps get made, they get used for very nefarious purposes. They get used to hurt people or exclude people or eliminate people or push people away or to control them.

And so I don’t really have an answer, but my instinct says that—there’s some interesting writing being done out of The World Policy Institute by a guy named Craig Lindsay on emergent cities, and one of the conversations that he and I have been talking about is let’s call it layered formality that goes from the parasitic and disruptive informality that is drug networks and cartel networks.

What’s interesting about those networks, they are actually all related. Many of them use the same nodes of transport. If you can hide drugs, you can hide people, you know. Pretty soon, if you are moving pelts, you are moving something else through it because it’s effective.

And that’s a whole other national security question, and not really competent to talk about that; but if you go forward from that and giving local people the ability to generate their own information or their own power or their own kinds of coordination, the power of their own agency, maps can be very, very useful in that regard if you don’t—I guess the question I have is how do you give enough agency and participation for people to be able to map their own information, then they get something for that, that there’s a quid pro quo, there’s incentives to own and control the information reasonably locally with protection, because they get enough local control.

And so I guess all the work we were trying to do—and it’s actually quite remarkable. People who can’t read can read a map pretty—more quickly than you might imagine. And the other interesting thing was when we were doing social mapping in West Point and other places, is you would have a very patriarchal society in the particular neighborhood control, but if someone who had positional power were to point at something on the map and say well, this is that, this is this thing, people who didn’t have positional power would feel perfectly okay saying no, no. That’s not right. That’s actually physically called something else. And you would have this other activity, this other experience of being able to tell the truth or own—have a conversation about what the map meant.

So this is a huge, huge question, and I don’t think the answer—I don’t think mapping is the answer to informality. I don’t think mapping informality is the answer to the informal question. I do think maps play a role in engaging with the informal and making the informal legitimate.

I guess what I feel like is instead of mobilizing the informal into the formal, instead of pulling everybody onto the grid, it’s almost like we need to find a way to legitimize the informal, and so what are the ways we go the other way? So how do we put—it’s a very interesting project, and I’m forgetting, but Jigar Shah and The Carbon War Room, Jigar Shah has started a group that works on complete end-to-end 5-megawatt generation that’s highly local.

So they do the jobs for feed stock, they do the processing, they do the biofuel output and they do all of the end-to-end. The government doesn’t care, because they only care about 10 megawatts and above. So they go to markets where the government only cares about 10 megawatts and above.

So now you can have this distributed network of 5-megawatt generation and some of the things we’re talking about there, okay. Now you layer a cloud, a rugged cloud on top of a 5-megawatt generator, then you layer WiFi on top of that, then you layer health information on top of that, and maybe it’s linked or remote linked to other 5-megawatt generation, et cetera.

So I think this idea of giving agency to informal communities in a way that legitimizes them, I think there’s economic value there. I think that there’s security value there, but it’s about valuing the people of the edge in a way that they can come to that value and have dignity within the system.

It gives them agency, and I think informal agency is the truth of the 21st Century, as opposed to bringing people more onto formal agency. And I think that negotiation will be ongoing for many, many years, and they are—it’s huge. Like a huge experiment, massive experiment.

And the last thing I’ll say about that is I think something like 50% of the population of the Arab Awakening countries are under 25. So if you want to talk about the emergence of a bunch of informal, Facebook-connected, self-organizing communities’ intention with formal governments that were set up in a particular way that are not unbundling, the future of that informal conversation is really upon us, and I think it’s going to be really interesting to see how that happens.

Brownstein:  Yeah, just in the context of health, we’ve seen the power of the informal, and I think the idea of legitimizing informal as opposed to bringing them into the formal key—we work in these networks of health professionals a people on the ground that are providing information in places that have incredible difficulty of getting information.

And what we’ve seen is the kind of data that we collect—and we’ve been doing this for a long time—is actually what like a WHO relies on for the majority of their outbreak investigations, relies on informal data, but has never truly recognized that data as being legitimate; but I think we’ve seen a real change over time where now WHO and other organizations are—I think this is happening at UN, that they’re recognizing the importance of these informal networks.

And I think at the country level that’s also happening. If we look back ten years ago at SARS, we saw little pieces of information from informal networks about the disease that was occurring in Guangdong Province. Only months later did we know that to be SARS. Now we have things like H7N9 in China.

Now the informal networks are pushing the government to be much more up front about what’s going on. So the lag time between cases in a particular area that are put out on Weibo, now the government is confirming that on the exact same day. So we are seeing the power of those informal networks driving sort of the more formal recognition of disease, which is really incredible.

We’re not seeing that as much in the Middle East, unfortunately, with the MERS outbreak, where physicians are exiled as a result of coming clean about it, but we’re definitely seeing that transformation in many parts of the world, which is very exciting.

Camarata:  Yeah, it’s—oh, Stewart, did you—

Brand:  That just raises the point you were making about formal, informal and anti-formal, being some criminal results and anti-informal, when you bulldoze a squatter city, which is sort of the formal crowd, the term isn’t criminal, but it’s not welcome. So anti-informal, I think, can also be a destructive process, and maps maybe can help work around that.

Camarata:  That’s a good point. There’s a lot of things that affect what you’re talking about. Obviously, kind of higher governmental level, what’s the policy of the government. There are a lot of countries in the world that still won’t let map information go out. I mean, China being one, North Korea, but even some of the developing countries in Africa, there’s still a lot of policy about that.

Anyone heard of a company called Ushahidi? Real interesting company started back when the Kenyan constitution got passed. And some people got together and said what was happening; they were having crowds coming together. And then the government was coming and breaking them down and stuff.

So they developed these really amazing, simple text message things that burst out information to crowds and said don’t go there. Go over here. And they were actually getting around the kind of government control, but eventually—it wasn’t the only reason, but it led and helped drive the whole new constitution, that sort of stuff. That became part of the whole crisis, mapping framework.

So very interesting how that—the people, the power of the people actually, to an extent, was counter to the government policy things, but helped drive some changes. So I think the policy level is a big part.

And then there’s the whole thing about—I love your example, the 5-megawatt versus the 10-megawatt. It falls under the radar, so people just go out and do it and they make something happen. I think that’s going to start happening more and more. Not just with mapping, but mapping is part of it, where individuals who have devices are a part of the mapping ecosystem, if you will. They can start to pass information and share it and drive things forward.

Tyson:  But also, want to mention that’s a business opportunity. There’s a number of people here saying that there is enormous amount of money to be made in the informal sector by giving the informal sector the resources it needs to then put stuff back into the system, and that the disintegrated communities that are going to come in, there’s all kind of business model innovation that we need to bring to that sector and amplify it, as opposed to—there’s more to the world than micro-finance. There’s a lot more out there. Anyway, I think things like that are super-interesting.

Miggins:  Hi. I’d like to come back to the privacy and security issue that was raised just a little while ago. One of the grounds I’ve been working very closely with for the last four years is the International Aid Transparency Initiative, IATI, which some of you’ve probably heard of, because it has a location component, like 80% of all data, and we got buy-in very quickly at the high levels.

Governments were happy to come in, international agencies were happy to get involved, but where we’ve run into trouble with it is aid’s last mile. And the last mile, money tends to flow down from governments to agencies, to international NGOs, to national NGOs, who do the work in the field.

They took a look at us and said, just a second, you’re going to let people put a marker on a map in a village in West Pakistan and say we have three people there working on women’s education. Okay. Let’s sit back and have a conversation about this.

So we’re still struggling with that, because in a sense, that’s the highest value. It’s great to be able to map out the U.S. is giving this much to Pakistan, so you could call it Pakistan red or blue or whatever, but the real value is going to be getting down to the field, and we’re struggling with this.

Some NGOs have been wonderfully helpful, usually ones working in countries that are happy and cooperative to have them there. And some have been very upset.

One of the solutions is like what Richard talked about, fuzzifying geographically, so you roll up and instead of saying we’re working at this latitude and longitude, say we’re working in this country, we’re working in this world region, we’re working somewhere in Central Asia; but that’s not always good enough either, because if you give the name of the NGO and you say they’re working in South Asia on women’s education, and the local Taliban leader says hey, just a second. That’s the name of the NGO that’s working in this village, you are back to the same problem.

So the other thing we’re looking at, and this isn’t a solved problem by any means yet, is fuzzifying thematically. So the equivalent would be taking a map and saying well, maybe this is a city or maybe this is a river and we’re not going to tell you. And that’s what we’re look at here, moving up the taxonomy of what they’re doing.

So instead of women’s education, it could be education or it could be some level higher up there. I don’t know if the rest of you have run into—audience and panel have run into that kind of problem, but there’s more than just geographical fuzziness we have to look at.

Scott:  Yeah. What you’re putting your finger on is a fundamental problem of anonymization, which is that you—anonymization is really about trying to take a piece of data that can be precisely localized and making it be in a large enough population that you can’t derive any useful information out of that. And that’s only as good as, in effect, the weakest link.

So the weakest link could be S.J. working at Esri and lives somewhere else, and if we have a piece of data that regularly shows up at Esri and the somewhere else I know where S.J. happens to live, pretty high likelihood that’s S.J. So the weakest link there is not the one or the other, but it’s a fact of both pieces of information are being represented.

I don’t know how to solve that problem, because at the end of the day, the goal on the one hand of having transparency is fundamentally at odds with the other goal of anonymization, and I suspect there will be a series of case-by-case resolutions and that tension that—protecting people’s lives, but at the same time offing transparency.

Brownstein:  It’s an area we worked on quite a bit in the health domain. You can imagine the difficulty in having address level information and geo-coding it. It’s not a concept that necessarily occurred to researchers.

We went through a bunch of literature on case studies and found all those maps where people had geo-located cases and put out maps like in PDF files, and this is a new journal paper a few years ago, where we showed we could re-identify the location, the address of the HIV-positive patients from paper—this was actually a former CDC director that accidentally put that out, but the concern is that if you start to aggregate the data, then the value of that information becomes less useful.

So for the types of spatial analyses clustered detection, things we’re looking for very specific issues in a population, then you start to aggregate that kind of spatial relationship is gone. So we’ve worked on lots of different methods around how do you move the map around, still reserve spatial relationships, but move a dot enough that you wouldn’t be able to re-identify the patient.

So we have these algorithms that essentially, based on underlying population density, you can move a dot a certain distance, so you’re anonymized to 1 in 100,000 people or 1 million people, but you still have those dots in a way that you can actually do spatial statistical analysis.

So those are the kind of things we think about. There’s a whole sort of body of literature on this space in the health domain that spend a lot of time, because we want health—geocoded health information for the kind of work we do, and it’s just so tricky to get that information without forcing people to aggregate.

Camarata:  Yeah, I think there’s the technical, like you’re talking about, ways you can deal with that, and there’s lots of—there’s tools you can do, you can change stuff and just immediately it’s there, but it’s not there type of a thing; but also, what you mentioned, right after 9/11 happened, there was a huge thrust, especially in the U.S. Government, to lock up all the data.

I mean, everybody freaked out and said oh, my God. We got maps of our utility lines and health centers and our government buildings, and all the terrorists are going to get their hand on it, so take everything and pull it back in. And I remember we were part of a group they had at the Brookings Institute that—they had a bunch of congress people there.

And it was a very interesting discussion, because the intel of the military, people are like you cannot let this data out. We have to get the maps back in. Other people were like, look, this is a democracy and the world has to have information.

So there’s a balance between societal needs and societal requirements versus protecting people, like letting the Taliban know where a bunch of girls are at school and they’re going to go out and try to get rid of them. That’s real difficult, and that’s real life down to an individual scale.

So I don’t have a specific answer. It’s something that we’re constantly trying to deal with, kind of policy levels with lots of our customers and government agencies, but also the technical level with tools exactly like John is talking about, where you can kind of deal with it. So part of it is creating—the tools are there to make anything you want.

You can do—you can corrupt maps real simply, but then it’s a matter of how is it interpreted. And it’s almost like you have to have a code, like the old code breakers. You see a map. If it’s red, it really means blue. If it’s this scale, it actually means green, so maybe a whole new language will come up to deal with that.

Scott:  As you start adding more data, you may have anonymized it for this data set. Now you have added a few more pieces of data, suddenly the anonymization goes away. So this is not a problem that you can solve analytically up front. And to some degree, you could argue—you’re never really going to be able to solve the problem, because with the proliferation of data sources, anonymization buys you time, but it doesn’t necessarily buy you anonymity.

Unidentified:  It’s actually worse when—

Scott:  Exactly. Right.

Tyson:  Come full circle back to humans in the loop, and where your policies matter and being thoughtful. I think a lot of people who treat maps as not necessarily dangerous things, just good things in development environments, disaster environment, you have to be thoughtful about what is possible, because you’re creating a common operating picture.

I mean, the point of a map is that anybody can read it. And if it stops being useful that way, then it stops being a map and it starts being a code or something else. And that collective value of common operating picture versus—or secrecy codes, I think something is really deep about maps and societies and your ability to tell a story about who belongs where or what’s happened where, when.

Every counter-history has a map. When people are telling stories about helping—security policy and privacy becomes not a problem we solve. It becomes a problem we have to continuously manage and be vigilant about and then learn through case studies and through other places. That’s an obvious point.

Camarata:  I think there’s one last point. Privacy is an issue for all information, such as maps. Any type of information that’s out there is—there’s an issue of privacy and maintaining that privacy and what becomes public or not, so hopefully—and we’re students of this. As a software company, we are dealing with maps and special analysis.

We are also looking at what other people—somebody may come up with some really intriguing algorithm that deals with privacy anonymization that we didn’t, and you could take advantage of that. So I think there’s a lot of different fields of computer science, if you will, that are looking to these sorts of things. And hopefully, collectively, someone will kind of figure some of this stuff out, but mapping’s part of the whole thing.

Well, I think it’s 12:30. I guess it’s time for lunch outside. I appreciate you guys sticking around. We don’t have a nice, beautiful view outside, but thanks again. And hopefully we’ll keep the dialogue going back and forth this week.

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