USGIF Member Releases GEOINT Textbook

We spoke with Renny Babiarz and Aaron Jabbour about their new textbook “Geospatial Data, Information, and Intelligence.”

Necessity is the mother of invention, or in this case, the authors of a new GEOINT textbook. After realizing that there was no textbook covering geospatial communications, Renny Babiarz and Aaron Jabbour decided to write their own.

Babiarz, Vice President of Analysis and Operations at All Source Analysis and a USGIF member who also teaches a class that focuses on geospatial communications as part of the Geospatial Intelligence program at Johns Hopkins University, and Jabbour, who conducts trainings on how to use geospatial analysis as part of his work at the Department of Justice, had worked together at the National Geospatial-Intelligence Agency (NGA). When they started talking about the textbook gap, they asked “why can’t we just write our own?”

We spoke with Babiarz and Jabbour about their new textbook “Geospatial Data, Information, and Intelligence,” their thoughts on teaching GEOINT, and the relation between analysis and communications.


Aaron Jabbour  00:06

I’m Aaron Jabbour. I’m the geospatial information officer at one of the components of the Department of Justice. My career started in the 90s in the Marine Corps, I was in the United States Senate in the early 2000s. And then I moved over to NGA, the National Geospatial-Intelligence Agency in the 2002, 2003 timeframe. I spent about 15 years at NGA, and then I moved over to the Department of Justice. And that move for me was driven by wanting to conduct a lot of the spatial and network analysis that we did at NGA for law enforcement. So moving it from the Intelligence Community, and mostly overseas to domestic and law enforcement, and seeing if we could apply it to, you know, some of the gangs and cartels and networks in the United States that were causing a lot of harm to our citizens.

Renny Babiarz  00:54

Hi, my name is Renny Babiarz. I have an academic background. I have a Ph.D. in Political Science from Johns Hopkins. During that time, when I was studying to get my Ph.D., I joined NGA and worked together with a lot of fine people, including Aaron. So we go back a little ways. Since leaving NGA, I have now gotten work at All Source Analysis, I’m Vice President of Analysis and Operations there. And I also teach at Johns Hopkins University in their Geospatial Intelligence program. And then along the way, I’ve reengaged with Aaron, and we’ve talked together about, you know, where we are in our different career stages, and the need for spreading more knowledge about how to understand geospatial information, sort of from the ground up from the everyday experience all the way to more technical analysis. And that was really part of the genesis of this book.

[How did you become co-authors for this book?]

Renny Babiarz  02:00

In teaching at the Johns Hopkins program, I didn’t have a textbook. I wanted one but there was no textbook out there for the class that I created and thought it was most important to teach in terms of getting, you know, the just the right message to students. And so I developed my own materials along the way, talked to our director, our program director, Jack O’Connor about this, he agreed, there was no textbook out there for the work that I wanted to do in the classroom. We started talking about the idea of a textbook. And I started talking to Aaron about it. And he let me know he was doing some trainings as well, also did not have a textbook to rely on. And from this, we saw that there was a gap that we were experiencing as practitioners as trainers and educators in the geospatial intelligence industry. And so from there, we just started talking about the idea of why can’t we just write our own textbook? And so that’s how it started. And two and a half years later, here we are.

Aaron Jabbour  03:05

Yeah, I think it was great, because this this really started as Renny trying to chase down geospatial communications and teach more about geospatial communications. And as we started collaborating on the project, we realized that communications was just sort of one step and maybe even the last step, but obviously iterative in this process of orienting yourself, analyzing information, and then finally communicating that information. And, and then we realized that actually the market need wasn’t just communication, it was also analysis and that you know that that basic orientation, observing something. I see something, I describe something, I understand something, I analyze it, and then I communicate it. And so we started expanding on all three of those things, and we ended up calling it the OAC framework: O obviously standing for observe, A – analyze and C – communicate. And we found a lot having to do with analysis. So there are a lot of books written on analysis, a lot of them are very specific. So they get very much into the weeds on one type of analysis. But what we found was analysis was really a bridge, from observation to communication. Because analysis has to start somewhere, it has to start with observing something first, as the scientific method and all scientific processes start with observation. And communication was really the missing link. Because once people conducted this analysis, it’s really no good unless you can successfully communicate that to an organization, to leadership, to your peers, to someone else. So we realized that analysis was sort of the bridge between the two. And we need to expand more on observation and communication. So we decided to just, you know, the whole kitchen sink, basically, like expand our research, and write more about what we already knew about best observation practices, best analysis practices, and best communication practices.

[Renny, for what course did you develop the curriculum  that became the foundational concepts for this book?]

Renny Babiarz  05:02

So the Johns Hopkins program in Washington, D.C., the advanced academic program is the main organization. And then within that there’s a program called the geospatial intelligence program. And I teach for that, currently, I began teaching a communications class that focuses on geospatial communications. And in developing the materials for that class, I began teaching students first how to do the work, so do the analysis, and then how to communicate it, because having a class about only communications really is as Aaron laid out, really the end of the process, and it becomes a writing exercise and a graphic template exercise. And so to make the class more meaningful, and true to the overall process of working in the field of geospatial intelligence, I started working on developing analysis techniques, and then communication. So that’s how the course started. And now it’s evolved from just a pure communication course, to a full scope course, that takes students from observation to analysis to communication. And that was really one of the preludes to the book. And the class developed further as we developed the book. And so now, it really fits well with the textbook and vice versa. 

[How would you characterize the challenge of clear and concise communication in a technical field such as geospatial analysis?]

Aaron Jabbour  06:41

Yeah, I’ll take a stab at this. It’s an epidemic. This is all across the world for all of time. Public speaking, speaking, producing, writing, communicating, is probably, you know, people’s biggest fear. It’s people’s biggest downside, usually. Most of the analysts that we worked with did great research and understood amazing things, but just could not properly communicate it. And, you know, not only were they either afraid to publicly speak, which, you know, Renny, and I agreed, somewhere halfway through the book, that actually getting up on stage and presenting, you know, doing a public presentation with an audience was actually the tip of the spear, because that’s where you get immediate feedback in your work. That’s where you get immediate peer review. So we started orienting, of course, obviously, Renny and I work together, and so much of our work, when we work together was oriented towards these big public presentations, where you present in front of your organization, you present in front of your leadership, because ultimately, we would have to go to Congress or we would have to go to a policymaker or someone with whatever we found. And we had to be perfectly polished when we did that. So we did a ton of practice and peer review. And it just became second nature to go over these presentations, you know, 25, 35 times before we went live. And we just realized how much work it took to bring an idea from the analysis phase where we finally supported a thesis and developed it and solidified it to actually writing it down, practicing it speaking at briefing it putting slides together. So that all comes together. So, I guess the answer is yes, like communication is a huge gap with most people, not just technical people, all people. And that’s why, as an aside, you’ll notice that politicians that publicly speak, aren’t always the sharpest people in the world, but they were just the ones that were willing to get up there and speak. It’s incredibly powerful, the power of speaking, of communicating, of writing well, and getting your ideas out there. So we saw that as an area that we definitely wanted to help people improve. And because we had spent years training people and practicing with people on our team, we had a lot of great tips and tricks that were these little industry secrets that we had kept for so long, that we felt it was time to share with our colleagues and with the rest of the world.

Renny Babiarz  09:11

Yeah, and I’ll just add two small points to that. First, we saw it firsthand and continue to see that people who are able to write effectively, create good graphics, but then also present effectively rise up in their organization. And so it’s professionally an important skill to practice to at least be comfortable doing those things. But then, secondly, it’s also important for the analytic process, because bringing your work to a community will give you feedback. And that feedback, then can feed your work on that same topic. And so it becomes a type of peer review. And this is something that we cover pretty extensively in different parts of the book, the idea of bringing more people in to look at the work that you’ve done to test it. And communication is a key part of that.

Aaron Jabbour  10:05

Yeah, and I think at the philosophical level, we reinforce, basic concepts that everyone knows like bottom line up front and economy of words, avoiding the dangers of passive voice, and the importance of using caveats to emphasize uncertainty. We go into a lot of depth on uncertainty, because we feel, especially when you’re communicating to leadership and decision makers, communicating uncertainty is one of the most important aspects of what you’re communicating. And getting it right, getting it perfect, using your caveats properly is super, super important. So we went into depth on that. We also added different styles of communication for different audiences and different levels of leadership. So obviously, you’re going to talk with your peers and your colleagues in a different way than you talk to your managers and supervisors. And the key leaders in your organization, and, as you’re briefing, your briefings probably get shorter and shorter and more and more precise as you rise up through the chain of leadership. And that was another thing that we wanted to emphasize in the book, how to brief different audiences in different ways.

[Who is the intended reader of the book? Is it for a broad audience or a more specific one?]

Aaron Jabbour  11:16

Yeah, so I’ll take that first. We started by thinking of the citizen scientist, just a basic person with no education at all, that needs to every day in their life, orient themselves and basically observe things in nature, analyze them, and communicate it. And we realized that this whole like OAC framework was broadly applicable to any person that needs to make a decision in life, all the way up to very technical people, imagery analysts and spatial analysts, who perhaps they’ve just graduated from college and they know some of the basics about intelligence from a classroom environment. But now they’re in a workforce, and they’ve taken their basic training in that workforce. But they need a book from industry professionals that will get them you know, that next level or that next layer of understanding of their work role. Also as geospatial expands from government to private sector, and from federal government to state and local government and starts to expand its marketplace, I think more and more people are going to need this book, they’re going to need to have a basic understanding of what geospatial is, how to define these terms, how to become literate in basic geospatial, how to brush up on some basic things that maybe they learned long ago, let’s say you’re a journeyman in the workforce, your mid career, you’re changing jobs, as people tend to do in the Intelligence Community, you need to brush up a little bit on imagery analysis, geospatial analysis, they’ll find some great tradecraft and tips and tricks in this book. So we really saw it as broadly applicable to citizen scientists with no educational background in this, who are just encountering this for the first time, educated people who have just entered the workforce and need a little extra boost, and mid-career journeyman that are perhaps, either they need a refresher on what they learned in basic training, or they’re changing jobs and need specific understandings about areas where they haven’t worked before within geospatial analysis, imagery analysis, and spatial analysis, of course.

Renny Babiarz  13:08

Yeah, and connected to that, when we began framing about Well, first of all, we did a tremendous amount of research during the writing of the book, because while we have a lot of experience and wanted to bring that to an audience, we also did some additional research. And we found the field of psychology has aspects of research that connect very closely to this book. We looked at the work of Barbara Tversky and how people use spatial perception every day. And we realized that this could be a way to connect to a broader audience. And so we began structuring the beginning of the book to connect people to their everyday experience, navigating the world, visually locating themselves in one location, and then remembering previous locations and mapping that spatially, internally in the brain, understanding that there are mechanisms in our brain to do that. But then we also just experience it without thinking about it all the time, every day. We also connected to the everyday with technology. So the use of cell phones and how much of a cell phones rely on geospatial data to allow us to do all the things that we do now with them. And then, at the very beginning of the book, we connected it to the everyday by showing people how even technical geospatial analysis can address individual daily concerns. So for example, we open with a true story of geospatial analysis that solved a crime. Someone was murdered and the person was found through careful geospatial analysis by people on Aaron’s team that we mentioned in the foreword of the book briefly. And this work, this technical work that identified a suspect was brought to a jury, and the communication of that work, that technical work in basic language to a jury allowed the jury to carefully consider it and then ultimately deliver a conviction due to how thorough the work was on the basis of very solid evidence. So these are some of the ways that we’ve thought about how to take this technical field and connect it in the everyday to people.

Aaron Jabbour  15:18

Just to dovetail on what Renny was saying about the innateness of location and visualization. I mean, that’s why it’s so powerful to get these things in front of a jury is because when you see these precise locations, and you know they’re measured by very precise instruments that can be peer reviewed and vouched for or corroborated, and you pair that with these very compelling visualizations, you’ve got this one two punch that is irresistible to lay people all the way to very technical people who understand it. And so, like Renny said, we’re trying to appeal to that innate sense of location and visualization in everyone, and then expand on it and capitalize on it to make you even better at both.

[What will a reader know more about or understand better from reading your book?]

Aaron Jabbour  16:07

First of all, geospatial is a technical realm, and it involves all kinds of jargon and technical terms. If you Google the term geospatial, you’ll find four or five different definitions, all of which are very, very long. If you read the congressional documents, they’re super, super long. I mean, it’ll take them two pages to define geospatial, because everyone makes it a little bit too complicated. So Renny and I just wanted to start with basic literacy in the field, and simplify everything. So for example, the term geospatial we just came up with a hyphenated two word definition and that is Earth-referenced. And we thought that would be a lot easier than all of these like three or four or five sentence definitions that we come across. Geospatial essentially means Earth-referenced. And then we add to it geospatial data information, intelligence analysis, and all these other terms that follow geospatial, we define all these terms very clearly and concisely. And then we expand on each one, and we show how they all fit in with each other. For example, data is a part of information, data should be transformed into information, data is what the analyst receives that is opaque to some extent. And it’s up to the analyst to transform that into transparent information for their leadership, or for the public. Intelligence is specialized information that’s maybe gathered clandestinely or in a secretive fashion. And it’s processed in a specific way. And then it’s delivered to leadership for action, usually a government entity. So all of these terms have specific definitions. What we wanted to do is first, define all of these terms, simplify them, and then show you how they all fit in with each other just to help people gain a basic literacy. Then what we wanted to do is break it down into three buckets. And this is what I teach when I do my trainings: mindset, toolset and skill set. And I always figure if I can break this down into these three buckets, people will understand it better. So it starts with mindset, and what we call it is the location mindset mindset. And that’s just simply prioritizing location, understanding the importance of location in everything you do, trying to collect better locations, everywhere you go, prioritizing it, as I already said, and just basic spatial orientation, understanding where you are, where other things are, where entities are, where objects are, and always just having location on the mind as a priority for understanding. The second bucket is toolset. So the toolset we see as the sensors, the systems, software, hardware, and even to some extent, the people that bring all of that data to the practitioner. So the toolset is kind of like the backbone or the architecture that collects and brings all of that to the user’s fingertips. And then finally, you have the skill set. And the skill set is the training and the mentorship and ultimately, the tradecraft. It’s how you conduct all of that analysis and over time in your career, grow and expand how you handle that information or how you handle the data and transform it into useful information for leadership. So we wanted to use those three buckets: mindset, toolset, skill set, to help people better understand.

Renny Babiarz  19:24

Yeah, and to follow on with that. So as Aaron is speaking and speaking about the field that’s full of all this information, what we’ve experienced and what we continue to see among other analysts is something that we described in the book as the big data deluge. There is a lot of information. And an analyst that gets dropped into this world faces a lot of different kinds of information, and then in just one kind of information, a lot of volume. And so how do we handle that? So mindset, toolset, skill set is one framework that we provide. But we also provide several other frameworks throughout the book, that provide a format for the analyst to quickly have a method to say, ‘Okay, I’ve got all this information, let’s go back to this structure and just work through the structure.’ And then that takes some of the organizational pressure off the analyst as they’re doing their work. For example, earlier in the conversation, we talked about the OAC process, that’s a framework, observation, analysis, communication. When faced with information, the analysts should understand, okay, I’ve got this big process to work through here, it begins with observations of some kind, then there’s an analysis phase, and then I need to know that by the end, I’ll have to communicate this to an audience in some way. Understanding that allows the analyst to say okay, now I know generally how to orient myself and all this data, all this information. Further, another framework that we have is something called the four cornerstones. And that’s something that carries through this process of, okay, when you’ve got information in front of you, and it’s visual information, think through in a structured way, how you’re going to work through it, how you’re going to look at it. Think about location, think about color, think about shape, think about size. This is a framework for understanding how to attack visual information. You’ve got a lot of images to look at, if you’re an imagery analyst. How do you start doing that? Here’s a structured way to do it, that allows you to work with the data without thinking about how to organize, you’ve got the organization in front of you, you’ve got the structure. So this four cornerstones is something we carry through all stages of the book to help structure all this data and information that analysts have to work with. By the end, what the analysts will walk away with from the book is a set of structures that will help them navigate a world with a lot of data now, that’s increasing all the time.

Aaron Jabbour  22:01

Yeah, and Renny, it’s interesting, you went back to OAC, and the OAC framework, because at the beginning of the conversation, we were talking about how much detail there is already in research done on analysis, but how we saw that gap in observation and communication. Once we started exploring our minds, and thinking about what we already know, and what we were taught about observation and communication, we realized that we had all these little tips and tricks in our brain after decades of doing this work that nobody ever had ever codified. Nobody had ever written this down. For example, we were using the four cornerstones for 15 or 20 years, we just didn’t call it that. And we hadn’t perfectly sussed it out into location, color, shape, and context in a way that we could easily relay that to another person or to another analyst. So, we realized that we had so much material in our brains in the observation category, and in the communication category, and it just needed to be codified. So the four cornerstones was one of them

Renny Babiarz  23:00

Yeah, it starts with something as abstract as observation, analysis, communication, gets more concrete with something like the four cornerstones, location, color shape, and then moving back outwards to context, it broadens out again. And yet, always understanding that there’s a degree of uncertainty. We’re providing all these frameworks for the analysts to navigate and orient themselves, to develop knowledge, to develop understanding about the world, to improve everyone’s understanding in an objective way. But then also, never forgetting that uncertainty will always be part of any analysis. And it is a window to the next step in analysis. Each piece of analysis answers the question, but it doesn’t answer everything. There’s always something that needs to be further understood, understood a little better, refined, perhaps. And so that uncertainty is key to the whole process, understanding that we’re providing a framework to help develop knowledge, but that there’s always a limit to that knowledge. And we need to understand that and communicate that effectively to our audiences.

Aaron Jabbour  24:13

Yeah, great, Renny. And I want to pull on that thread a little bit. You mentioned subjective to objective. And I think we spent a little bit of time in the book teasing that out and explaining that. And I think one of the powers of geospatial is that a lot of the world is subjective. But we find more often than not, these precise locations and visualization are a way to bring things into the objective world. So you can always bring a colleague over and say, let me get a second set of eyes on this. That’s making something objective, and visualizations really excel at this. That’s why the jury loved our murder case in Danville, Virginia, so much.We brought the subjective to objective, we provided precise locations and that beautiful visualization, so they could see and understand what was going on. And I think this book just highlights all the ways that we can move from the subjective to the objective by including these precise locations, by including visualizations, by doing more peer review. And by exposing it to broader and broader audiences so it can be tested and better understood, get more feedback, close that loop.

Renny Babiarz  25:23

Yeah, and really move away from that very, very fast reaction to visual information that many of us see on social media every day, where a picture is shown or a short video is shown and then there’s suddenly a very quick emotional reaction to slowing down, thinking carefully, using processes like the four cornerstones to think through carefully and look through in a systematic way visual information, tie it to location, think about time, what happened before that picture, what happened after that picture. And then let other people come in, look at it and think about it with you. So share the experience, share it through, in many cases, a number a geo-coordinate, so latitude and longitude, for example, so that somebody can look at it later, so that members of the jury could take the information in the Danville murder case that you mentioned and if they wanted to go and look up some of those locations themselves to see on on different systems to understand it and look at a Google Maps, for example, something that everybody uses and has access to on their phones. So yes, this move from subjective to objective. And then also part of that move, Aaron, I think, and we’ve talked a lot about this is slowing down and carefully looking at information, bringing other people in to share the experience and see, I had this reaction, do you think that that’s right? It’s all part of this process of being a little bit more careful, a bit more considered in our thoughts about what we’re seeing, and moving from just an emotional reaction to something more careful and rational.

Aaron Jabbour  27:06

Yeah, that’s absolutely right. And, you know, I think you hit on a really important factor here. This is why we brought in Kahneman and type one and type two thinking because we thought it was so important to slow these processes of decision making. And to employ a structure like the four cornerstones. When you employ a structure like the four cornerstones, it takes time. So you’re permanently inserting time into the equation to stop your body from emotionally reacting and jumping to a conclusion about what you’re seeing or what you’re thinking, and going through a series of steps. And just the process of going through the steps location, color, shape, context, provides you with more time, even if it’s three to five seconds, enough time to take one step back from whatever your brain or your subconscious wanted to react with. And so we see this as very helpful, especially, we talk about the informational age a lot, and Renny talked earlier about the big data deluge. So we’re in this information age now, where data and information is just pummeling us every day in our lives. And it really takes an effort to not react and to not become emotional and decide that everything you see in here is true and your hair should be on fire everywhere you go because of it. And so these steps and these structures and these processes, and these workflows, allow the analyst to approach data in a very specific way, or even information, to approach information in a specific way that slows them down and allows them to apply a process that will give them the time and the structure so that they can have the best possible outcome when they go to transform that data into useful information.

Renny Babiarz  28:44

I’m reminded of being in workplaces where there’s pressure because there’s a lot of information to work through in a professional environment, and pressure to get an answer quickly out to someone else in that community. And it’s important to realize slowing down in that environment is necessary as well, and just saying, Look, I need a little more time to do this, I need a little more time with my team to think through this. So it’s not just day to day, at home or on social media, it’s in any kind of environment where you’re facing a lot of information and pressure to do something.

[The concept of slowing down through the application of an analysis framework to mitigate emotional response to imagery could be useful in other arenas as well, for example, with social media.]

Renny Babiarz  29:19

It’s a business model of social media companies to design their platforms in that way.

Aaron Jabbour  29:34

Right, and that’s not only to shock, and all you into giving them your eyeballs for one to three seconds longer. As deepfakes become more and more popular and the actual pictures that you’re seeing are just misinformation or whatever, or the videos that you see are just completely false, I think time will become more and more important of an element to just take a step back from all of this data and visualization, these things that you’re being inundated with, and to go through a structured process of understanding who prepared that information? And why are they presenting it to you? And how many filters did it go through before it got to you? And what is the desired intent of the person that’s presenting you with that information? I think that’s why a lot of us rely on data more than information. When somebody does a study, and they come out with the information, you read it, and then you say, let me have a look at the data because you want to get a second set of eyes on that data so you can do some quality control on it, run it yourself, see if you come up with the same result. And that’s going to be the same thing with visualizations. Yeah, you’re showing me a picture. There was a Border Patrol incident a couple years ago where there was a famous photograph taken of a bunch of Haitian migrants that were trying to cross the border and a Border Patrol agent was trying to stop them with his horse. The initial photograph came out and just lit the world up. And it was decided immediately that this Border Patrol agent was whipping the Haitian migrants. And that was even repeated by President Biden. He had a big press release and he said the Border Patrol guys were whipping them and that’s wrong and they shouldn’t have done that. And then they interviewed the journalist that actually took the photo and the journalist was like, no, no, they weren’t whipping them at all. That’s how they maneuver their reins. I didn’t see any whipping. And then Border Patrol did a full investigation and they found that there had been no whipping at all. But it’s just the power of that one photo from that one angle and people’s preconceived notions or narratives of wanting to see what they believe that led to that perfect storm, and social media, of course, presenting it all over the world. These are some of the dangers of fast thinking and emotional thinking and reacting to data or to visualizations too quickly without using a slow structured method.

Renny Babiarz  31:55

I’m reminded, as you’re saying that, of another small structure we introduced in the book: find, link and layer. Find an observation, link it to other pieces of information, and then layer it over time with more and more information. In the example that you just gave, okay, you’ve got the photograph. But now who took the photograph? Find the journalist, talk to the journalist, what did you see when you were taking this picture? Layering, taking that information, layering it together over time, leads you to a more objective and clear and accurate understanding of what was actually going on, instead of just that one second in time.

Aaron Jabbour  32:38

Yeah, and we have some critical thinking tools in the book, and we have some structured analytic techniques. But at the end of the day, a lot of people are familiar with these, they’re very popular, there’s been a lot written on these. They are important to what we do, but we actually wanted to break it down all the way to the moment you encounter the data, or the information, and the decisions you make, and the structure, and the emotion and all the stuff that goes into that initial encounter so you can start on the right foot. I know you’ve heard garbage in garbage out, if you’re not putting the right pieces of data into the pot, then you’re not going to get the good information out on the other end. So we just wanted to start from the very beginning with the data or with the encounter, the visual encounter with information, with the observation, and get people on the right track from the very beginning so that as they collect more and more details about the entity that they’re collecting on, that they can be more closer to the truth and more closer to reality. And therefore, once they conduct their analysis, they will be more likely to find out what is true or correct about whatever they’re analyzing, and communicate that properly.

[Can you talk more about the path from subjective observation to objective analysis?]

Aaron Jabbour  33:53

The framework is just step one because you’re still in the subjective mode when you are perceiving. The goal is to move it into the objective world. So the framework applies to how you perceive and handle the data. The objective part is peer review, bringing over a mentor or a peer or sharing it with a colleague, or just bring another human being into the equation, to help make whatever you’re encountering more objective.

Renny Babiarz  34:22

Yeah, and one specific way that I think we ran into all the time in a professional environment, is that involves communication, but communication of a certain type. You’re bringing over a peer to look at what you’re looking at, or to review something that you’re considering or reviewing. You do it in a way that’s a little careful, you don’t reveal all your own personal interpretations upfront. You say, I’m looking at this, or I’m considering this topic or idea, here’s the source. If it’s an image, here’s the image date, here’s the location. If it’s an article or some other piece of data in a dataset, here it is. What do you think of this? And then getting their full reaction, absent from your own interpretation first. So that’s something that we do mentioned in the book and it’s something that we practiced a lot, day to day and that is this way of making sure that you are controlling your own individual interpretation on a topic and not letting that affect a peer review process along the way.

[Do you have any final thoughts to share?]

Aaron Jabbour  35:34

Yeah, I’ve got I’ve got something else. I want to talk real quick about the expansion of geospatial from the federal government to state and local governments to the private sector. Recently, so President Trump in 2018 signed the Geospatial Data Act (GDA). And that piece of legislation is going to completely expand geospatial capabilities in the federal government, which is obviously going to have that trickle down effect of state and local and then the private sector. The Geospatial Data Act is super powerful and it basically requires every federal agency to have a geospatial capability, and there’s certain reporting requirements. They have to cough up a certain number of geospatial datasets each year, and send those datasets to and then the geo platform, so the public world can build models, and analyze, and stop FOIA-ing the government so much, etc. So this Geospatial Data Act, I think, is really powerful. It flew under the radar, not a lot of people are talking about it. But if you read the letter of the law, if you’re a policy person, you read that policy and you see that this Geospatial Data Act is going to echo and expand geospatial capabilities in the federal government, which obviously has that massive trickle down effect.

Renny Babiarz  36:53

Yeah, and we’re seeing it internationally as well. So different countries that don’t have nearly as developed a legal and technical framework for geospatial data want to start developing it. Places like Taiwan, going to Taiwan and advocating to educational institutions there saying, you need to start investing bringing in geospatial data into your programs. Start educating people about this. It’s something that everybody uses, and there are really wonderful applications of that day to day. So for example, disaster management. If there’s an earthquake, that’s an ever present danger in a place like Taiwan, for example, and just being able to have clear location foundational data allows you to better coordinate all kinds of reactions to any kind of humanitarian issues that could arise in a location. And so advocating for that, and teaching people about that outside of the US is another aspect of this that we’re seeing as well.

Aaron Jabbour  37:52

Yeah, it’s funny because the GDA, actually, the genesis of the GDA, was the Federal Aviation Administration Act because we needed so badly to have a better air traffic control system because location is so important to aeronautics and to everything nautical and to public health, disasters, you name it, emergency medical services, public safety, the list goes on, and on and on. It turns out locations important.

Renny Babiarz  38:24

Yeah. And for something like aviation and navigation, but maybe aviation in particular, it really connects to other locations around the world. And it requires these other locations to also have clear locational data at their airports to make sure aircraft can land and take off effectively.

Aaron Jabbour  38:42

Right. We covered the Geospatial Data Act and the United Nations. So broadly, we looked at the entire world through the lens of the United Nations, and the United Nations actually has a geospatial organization within it and publishes geospatial papers. And then we looked at our country, the United States and the Geospatial Data Act and the effect it was going to have on the United States. And from that international organization, to our federal organization, we just realized there’s something changing very fast here in the world. And more and more of these massive organizations that have massive influence are starting to adopt geospatial practitioners, starting to hire geospatial practitioners, starting to underscore the importance of geospatial analysis, starting to bring the tools – we talked about the mindset toolset and skill set earlier. They’re starting to develop the toolset and the mindset and the skill set. And it’s really exciting to see this happen, I think, when we started in the early 2000s. So I started in 2003, I believe. And when I started at NGA, it was really just an IC federal thing. It was very under wraps. It was only in the IC. Of course, there was geography, geography is much older than that. And of course, there was mapping, mapping is much older than that. But really, the imagery side of the house was in its infancy, and rather quiet. That has now also exploded with commercial imagery. And I think everyone is seeing the value of commercial imagery across the world for so many different functions. But also mapping. Mapping and imagery have been combined in a new way. Of course, they were combined in NIMA in the 90s. But this explosion really started happening in the 2000 10s, I’ll say, with social media and cell phones, where it has just become institutionalized in almost everything we do. Everywhere we go, we have that little geospatial device in our pocket. And people are just waking up to the idea of combining imagery and locational data or spatial data to create this power packed thing called geospatial or geospatial data information and intelligence, which really helps us understand the world much, much better.

[With the increasing recognition of the utility of geospatial analysis and the broad opportunities for employment, how do we raise awareness to get more students into the pipeline for the future workforce?]

Renny Babiarz  40:59

I know Aaron does a lot of training and has a lot to say on this topic. And I do as well. I was on a panel at GEOINT, a couple years ago that talked about this very issue. And you mentioned the acronym STEM. And I’d just like to pause that and suggest that, while there are very technical aspects of the field of geospatial data, information, and intelligence, it’s wider than that. We should have a wider scope for introducing new people into the discipline. For example, I did not have a technical background and I started as an imagery analyst. I took to it and that background I had was more humanities. We can welcome people with a variety of backgrounds into this field. There are places for pretty much anyone who is smart and interested and has some aptitude for visualization, spatial thinking, and the technical side. So including other kinds of technical disciplines. We should have a wide scope, and we should be able to introduce the field in an everyday way to new practitioners. And we should really be doing it, I think, at the high school level, if not even earlier, but high school and undergraduate level, thinking about developing educational programs, that supplement the master’s degree program that I teach, at an earlier level, to start building a pipeline earlier and earlier. So those are some of my thoughts on that.

Aaron Jabbour  42:36

Yeah, I was thinking that from the very beginning, I was thinking high school. So every single course that I teach, I open it up by saying, has anyone here taken a geography class? And rarely do I get a hand that goes up. And that means no one had it in high school, no one had it in college, no one learns geography. So the earlier we can start with geography, the better. I think there’s been a great push to get kids outdoors and make them hike and camp and do things outside. I think that’s where you’re gonna get kids interested in geography. You like going outside. Why? Because there’s stuff outside. Well, let’s look at that. What’s outside? There’s stuff, there’s trees, there’s mountains, there’s rivers, there’s all kinds of amazing things. The more inquisitive and curious you are about all those things across the Earth’s surface, the more that little spark will will happen. And you might just want to take a class on it and learn a little bit more about space and the space of the Earth’s surface and what’s on it and how it’s related. So getting kids more interested in geography from a young age, and creating that pipeline in middle school or in high school of kids that are interested in studying geography in college. And then once they get to college, understanding the different sides of geography. You’ve got a technical side, where people are doing mapping and data models and statistical analysis and stuff like that. And then you’ve got a cultural side of people that just want to do that type of stuff. So figuring out which kids have that technical side, and maybe also the the exciting part of working national security, working intelligence. I’m excited with foreign policy stuff, or national security and diplomacy stuff. And I’m also interested in geography. So how do I pair those two subjects in college where I can bring together some political science with some geography. And maybe that’s that perfect pipeline into some place like NGA or CIA or a place in the Intelligence Community where you can do this.

Posted in: Individual Member Spotlights, Spotlights   Tagged in: 


USGIF Member Spotlight: Riverside Research

Steven Omick, PhD, president and CEO of Riverside Research, told trajectory how his organization is helping government agencies meet GEOINT challenges


USGIF Member Spotlight: CGI Federal

Errol McEachron, Senior VP of Consulting Services at CGI and leader of the space and intelligence sector of the company’s Defense, Intelligence, spoke with trajectory about the organization.


USGIF Member Spotlight: Arete

We caught up with John Wilson, Director of IC Programs at Arete, to discuss the organization’s role in the advancement of geospatial intelligence and what the future holds