GIS: More than a map
Geographic information systems are powerful mapping tools that we use on a daily basis without realizing it. Apps for navigation and weather are just a few examples of GIS in our everyday lives. But the applications of GIS span far beyond consumer apps. These complex systems hold massive amounts of data that scientists can use to solve big problems. In this episode, you'll hear how Oak Ridge National Laboratory has used its expertise in GIS to aid in disaster relief; find missing populations for a polio eradication campaign; and map communities using social media.
BRELSFORD: I want to do science that matters to the world. I want to do science that has the potential to have an impact.
BHADURI: We measure our success in terms of how many lives are being impacted, and how many lives are being saved.
TUTTLE: We are confident that the data that we provide is truly helping.
JENNY: Hello everyone and welcome to the Sound of Science, the podcast highlighting the voices behind the breakthroughs at Oak Ridge National Laboratory.
MORGAN: We’re your hosts, Morgan McCorkle.
JENNY: And Jenny Woodbery.
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MORGAN: Think about your morning routine – you wake up, reach for your phone and use apps like the weather and navigation to plan your day.
JENNY: With the tap of a finger, you have access to detailed information about the day’s forecast, as well as instant traffic updates for your morning commute.
MORGAN: While we use these apps on a daily basis, have you ever stopped to think about the science that’s driving them?
JENNY: Apps like these are fueled by geographic information systems, or GIS.
MORGAN: A GIS is a tool that captures, stores and manages large sets of geographical data. All of this data is visualized as a map.
JENNY: But these systems are so much more than a simple paper map. They contain massive amounts of data through what are called “layers.” Each layer represents a different kind of information.
MORGAN: To give you an idea of the power of layers– think back to your navigation app. You start with a basic map that shows the roads where you are. You’re interested in seeing what the traffic will be like, or what the road conditions are like, so you select those features.
JENNY: When you add those features on to your map, you’re adding layers of information. And all of those layers together help you form a better picture of what your drive will be like.
MORGAN: Of course, the applications of GIS span far beyond consumer phone apps.
JENNY: Researchers use these systems to solve problems through the study of geographic information science – also known as GIS.
MORGAN: Not confusing at all, right?
ERIC WEBER: So, GIS does refer to both. Geographic information systems are just the tools that we use to deal with geographic and spatial data on computers. Anything that helps us deal with spatial data, make maps is geographic information systems. Now behind that is geographic information science, there is a science behind how do you deal with spatial data and all of the things that makes spatial data unique and different from other kinds of data.
MORGAN: That’s Eric Weber. He’s a research scientist at Oak Ridge National Laboratory.
JENNY: Eric uses geographic information science to study population distribution and movement, also known as human dynamics.
MORGAN: Human dynamics looks at how populations organize and change over time.
JENNY: Having a clear picture of where people are is very important for many reasons like urban planning and disaster relief, but shouldn’t we have a pretty good idea of that based on census data?
MORGAN: Not quite – census data can tell us many things, but it doesn’t give a complete picture of where people are at any point in the day. The census reports where people’s residences are – so we know where they are at night, but what about for other portions of the day? Where they work, where they shop or go to school? Plus, in some areas of the world, census information is unreliable or nonexistent.
BUDHU BHADURI: There are still parts of the world that do not have an official census. And some of our recent work has revealed where people truly are that individual countries did not know themselves. So that's what we call, you know, finding the missing millions on the planet.
JENNY: Budhu Bhaduri has been a pioneer of geographic information science at ORNL for more than 20 years. He currently leads the National Security Emerging Technologies division at the lab.
MORGAN: He helped create the lab’s flagship GIS-based model and dataset called LandScan, which has become a community standard for global population distribution data.
BHADURI: Sometime starting from the ‘80s into the ‘90s, we started getting a fairly detailed understanding of how people are using, you know, what sort of land use are around the planet. And in the late 1990s is when we combined the resources of that abundance of satellite images along with census data and our knowledge of doing distribution of population and coming up with a model that allowed us to distribute people into finer cells then what census typically reports.
JENNY: LandScan divides the Earth’s surface into a grid of square cells that are about 1 kilometer across. Scientists then can estimate the number of people in each of those grid cells.
BHADURI: The simplest example is, you know, if you can identify where all the buildings or structures are, which are only made by humans, they don't occur in nature organically. It gives you an idea about human presence on the planet. So that's a foundational data layer that helps us understand or model the distribution of population.
MORGAN: In developing nations, LandScan’s ability to identify people based on where structures are can help fill in critical gaps in the census data.
JENNY: Over time, better satellite imagery and faster computers have improved LandScan’s resolution. Instead of looking at a 1-kilometer cell – which roughly gives you a view of a neighborhood in a city – scientists can now look at a cell that’s about 100 meters across. At this scale you can view a single city block.
MORGAN: This expertise in using GIS to pinpoint populations drew the attention of the Bill and Melinda Gates Foundation in 2013.
JENNY: The Gates Foundation was working to eliminate the polio virus in Nigeria, but unreliable population data made it very challenging to estimate the number of vaccines that were needed.
VINCE SEAMAN: At the time, Nigeria was one of the three countries in the world that wild polio virus was still circulating. And it was in the northern half of the country. Even though they have been conducting four-day, house-to-house vaccination campaigns every couple of months for the last 10 years, they just couldn't get rid of the virus. The reason was because they were missing too many children each campaign - and these were the same children probably – so there was a large chunk of children that were never getting vaccinated.
MORGAN: Vince Seaman is a senior program officer for the polio team at the Gates Foundation.
JENNY: To stop the virus from circulating, they needed to get at least 90 percent of the population vaccinated, but polio cases kept cropping up from villages that weren’t on their maps.
SEAMAN: So, we recognized that the maps being used for planning were not very good. All they had locally were these hand-drawn maps, which we call cartoon maps. They were not complete – not all settlements were shown, and the ones shown were not in the right place. Using these maps to plan where to send teams and allocate resources put the program in a hole from the start. We realized that an accurate map with all the settlements in the correct locations was needed to start with, which would at least give us a chance to reach them.
MORGAN: Eric Weber worked closely with the Gates Foundation on the project.
ERIC WEBER: When we very first started looking at it, we knew that the census data in Nigeria was not great -- their last census was in 2006. We quickly decided that what we needed to do was not rely on the census data. So, it was the first time we'd sort of just kind of thrown out national census data and said, we're just going to kind of from the ground up, from pixels of satellite imagery, map out where buildings are, estimate how many people are in those buildings and do it all just like from the ground up. We applied that at a huge scale to do all of Nigeria, mapping out where all the settlements are at like a resolution of like eight meters on the ground.
JENNY: ORNL would develop these high-resolution maps and push them out in real time to the Gates Foundation’s team in Nigeria.
SEAMAN: So this collaboration with Oak Ridge was a good example of what we call operational research. The Oak RidgeL team was developing and refining their settlement extraction algorithm, this helped them to identify and locate all settlements visible from high-resolution satellite imagery. They then used these outputs to create population estimates that could be used to predict vaccine requirements and measure campaign coverage and so forth. And we were in a position where we needed this data for a real-world situation, polio eradiation in Nigeria, so it was a good partnership.
MORGAN: As a result of the joint effort, the Gates Foundation was able to eliminate polio in those areas of northern Nigeria by enabling vaccination teams to reach every settlement and boosted vaccination coverage above the desired 90 percent.
SEAMAN: What Budhu’s team did was instrumental in supporting the polio work in Nigeria, which was a key achievement for the polio eradication program. We're not quite done yet, but without the success in Nigeria we would not be where we are today – which on the cusp of global polio eradication.
JENNY: When it comes to preparing for an emergency or providing disaster relief, having access to up-to-date, detailed maps is critical.
MORGAN: Especially for first responders who use this information to provide assistance during an emergency.
JENNY: Because of ORNL’s expertise in GIS, several federal agencies work with the lab on projects that can provide this valuable data quickly whenever disaster strikes.
MARK TUTTLE: Operative word quickly. You do have a slight advance notice particularly in a situation of a hurricane or particularly in a situation of, of maybe a volcanic eruption where you have lava flows. But in the case of, for instance a EF-5, tornadic outbreak, very little advance notice that you have. So yeah, you need it available everywhere at a moment's notice. Quickly.
MORGAN: Mark Tuttle manages two projects at the lab that use GIS to support emergency preparedness and relief efforts.
JENNY: The first is called the Homeland Security Foundation Level Data Sets, or HIFLD, which has been ongoing at ORNL since 2009. This project develops data to map critical infrastructure like the nation’s power grid.
MORGAN: Having a definitive dataset of this infrastructure helps agencies assess hazards and plan for emergency situations.
TUTTLE: Since we've been involved in HIFLD, we've pretty much been involved in just about any emergency that's happened from the 2018 Kilauea volcanic eruptions in Hawaii to Hurricane Harvey and more recently, Maria.
JENNY: Another project, called USA Structures, is supported by the Federal Emergency Management Agency. Researchers are using artificial intelligence to identify every single building in the US from a collection of satellite imagery.
TUTTLE: We are attempting to build the first nationwide consistent stock of structures visible on the surface, all structures larger than 450 square feet. In case you're wondering the significance of 450 square feet? 450 square feet is just slightly under the square footage of a single wide mobile home and mobile homes are a particular feature on the Earth's surface that’s very vulnerable to a wide range of natural disasters, hence setting the threshold for our structures.
MORGAN: Of course, in the age of Google Maps, we take for granted that a lot of this data is already out there, but Mark says that’s typically only true for urbanized and very populated areas.
TUTTLE: Emergencies don't just hit built-up urbanized areas. So, one the data does not exist everywhere. Two, where it does exist, it's oftentimes not in a format that's readily accessible by the response community. And one of the very reasons for FEMA being out there and FEMA being involved in geospatial, in GIS, is to make these data sets available.
JENNY: The USA Structures project is only four years old, but the data it’s provided has already made an impact.
TUTTLE: In Hurricane Harvey, the rapid response structure data that we provided enabled FEMA to actually perform damage assessments. They estimated about 600 percent faster than they normally would have, because they had this data in hand, or they had this data readily available. Now, that's a nice big number. But what does that really mean? By allowing them to perform the damage assessments that much faster. What that translated to is in checks being written to individual homeowners who are impacted whose homes were destroyed. But that much faster. That's a real impact. That's the feel-good part.
MORGAN: So, we’ve talked about using sophisticated GIS tools to identify where people are, but what does this geographic data tell us about human behavior?
CHRISTA BRELSFORD: Humans are really fascinating. Like, we talked to them every day. I am one. But we don't, we don't really have good theory, like we all have an intuition, but we don't have we don't have really good empirical theory about how groups of people interact with each other and behave and change. I have always believed that the purpose of science, the scientific career is to do good in the world. I think that there's things that we don't understand about social processes that if we understood, we could build a better world.
JENNY: Christa Brelsford is a research scientist at ORNL. Because she’s interested in finding connections between physical locations and social interactions, she turned to Twitter, to gather geographical information in a social context.
MORGAN: This idea was inspired by a life-changing experience she had while doing humanitarian work in Haiti.
BRELSFORD: I was in the Haiti earthquake in 2010. And I lost my leg. So, I had about 24 hours between when the earthquake happened and when I was evacuated, in which I was a very scared, but conscious observer of an incredible upheaval, both geologically and also socially. And in the news, I kept hearing people being scared about how angry people would be and how much risk of sort of violence and sort of bad things, was the fear. But what it felt like I saw, just as an observer, was it like, "Oh, look, people are, people are really working together, people are doing pretty incredible things together.” And so, it sort of seemed like the earthquake created a shift in how people were working together. And I wondered if I could ask or answer that question from a scientific perspective.
JENNY: She started by examining tweets that were sent during a natural disaster like the one she was involved with to see how social interactions unfolded.
BRELSFORD: And what I what I found was that social systems or communities on Twitter then change they, they got bigger, there was more activity. I started to wonder "can I identify communities in space only from their Twitter communication?” Because if we can do that, then we can then we can start to say, OK, this is how this community has responded to some event that we can locate in space. And that gives us a bridge between the physical information and the social information. That's why I think this is really cool.
JENNY: You may be wondering how you could make a map out of tweets.
MORGAN: If you’ve ever tweeted something and tagged a location – say the town you’re in or the coffee shop you’re visiting, that adds geographic information into your tweet. And if your account is public, scientists like Christa can look for patterns in this open-source data.
JENNY: With this research, Christa is trying to build mathematical models that describe how communities interact and influence each other. This framework could have any number of applications – from helping urban planners understand infrastructure needs as cities’ populations grow to providing insight into a community’s needs during a crisis or emergency.
BRELSFORD: We have good physical rules for how water flows, and how trees grow and these sort of physical properties, but we don't have that kind of theory, that kind of empirical quantitative theory in social systems. I think that it exists, but I don't think we've had the opportunity to find it yet. And if we find it, then we'll have a better understanding of the world we live in. And from my perspective, we can make better decisions.
MORGAN: The work ORNL is doing in GIS is already having a direct impact on people’s lives around the globe.
JENNY: As satellite technology evolves and computers get even faster, GIS offers more opportunities to understand the world around us.
MORGAN: So, we asked Budhu Bhaduri what’s next for GIS at ORNL.
BHADURI: I think we are in a new generation of mapping that I would call mapping in high definition. So, all our maps that we use today, were generated out of data that do not capture enough details. Or even if they did, updating those maps with information was a pretty daunting task on a regular basis. But with these kinds of artificial intelligence techniques, we can not only map the surface with unprecedented detail, but we can keep that map updated almost every day, any change that is going on the planet, we can reflect that on that map. Like how our crops growing, you know, how the floodwaters are receding and how the fires are moving. And that's what we really want to know is how is the planet changing, right? And if we can understand change, well, today, it gives us hope and confidence that we may be able to predict change in the future. That's excites everybody.
JENNY: Thank you for listening to this episode of “The Sound of Science.”
MORGAN: If you enjoyed this episode, please leave us a comment or review wherever you get your podcasts.
JENNY: Don’t forget to subscribe so you get the latest episodes when they’re released.
MORGAN: And remember, next time you use your weather or navigation app – you’re not just using any map, you’re using a GIS.
JENNY: Until next time!
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