(This is an example of a Digital Elevation Model, made in-house at East View Geospatial.)
At East View Geospatial, much of the work we do is project-based and involves helping our clients better understand the physical terrains of their areas of interest (AOIs).
Digital Elevation Models, or DEMs, are frequently vital data inputs in that process. We asked our Senior Account Manager, Jerod Fink, to break down what DEMs are and to go over how we utilize them here at EVG.
“A Digital Elevation Model (or DEM) is a 3D representation of a terrain surface. DEM data is displayed as a grid, usually represented in meters. This data utilizes an elevation or Z value at the center point of the grid cell to indicate height above or below sea level (in bathymetry data), or an ellipsoid. Grid cells can vary from 1km square to less than a meter square. The lower the grid spacing, the higher resolution, and thus more accurate and detailed a dataset becomes. High-resolution DEMs have many uses across all different industries, especially for different types of planning.
DEM is an overarching term for elevation data which can be further defined into two types: Digital Terrain Models (DTM) and Digital Surface Models (DSM). DTM heights represent bare-earth terrain. With corrected hydrographic features, rivers flow downhill, and the user can get a very accurate representation of all-natural terrain features.
Digital Surface Models (DSM) incorporate the built-up environment above the terrain in their values. For example, buildings, bridges, tree canopy and other vegetation heights.
DSMs are used widely in pipeline planning, mobile network development, and population movement studies. DSMs are of particular use to the airline industry (both civilian and military) for obstacles and terrain avoidance systems.
DTMs, on the other hand, are used widely in the planning of smaller areas where terrain details are of utmost importance. A few of the specific areas where DTMs are widely used are road development, ski resort construction, oil and gas pad construction/maintenance, and particularly flood mitigation.
Accuracy in planning is key- businesses need extremely detailed and specific data to ensure that time and resources are utilized in the most efficient way.”
At EVG, we provide both off-the-shelf elevation data as well as produce custom elevation models in-house so they can be tailored to our clients’ exact specifications. We are your GeoConsultant and will work with you to find the right product or solution to meets your needs. Contact us to see how we can work with you today!
(Image courtesy of The Atlantic)
In any domain, a strategic advantage is a prized asset. On fields of sport, especially at the professional level, where strength and skill are often equally matched, a strategic advantage can mean the difference between a winning record and a losing one.
Athletes, coaches, and team owners are finding ways to empower their missions with geospatial data. Some of their uses are to be expected, like tracking distances and speeds of runners or dogsled racers. Others are used to uncover hidden advantages.
One of the best examples of this is travel planning for teams. In general, the more a team travels, the more prone to injury their players are. In the 2015-2016 NBA season, for example, the most travelled team, the Golden State Warriors, reported more injuries than the least-travelled team that year, the Cleveland Cavaliers. With geospatial information aiding decision-making by league executives, this is the kind of problem that can be smoothed out league-wide, evening the competitive field.
Speaking of fields, a player’s activity on them during a given game or match can reveal much about their strengths. Detailed break-downs of an athlete’s habits and successes, like this one conducted by Esri for a match between Roger Federer and Andy Murray, can help hone specific skills. The data linked above, for example, combines court position, ball bounce, point outcome, and opponent responses to give an incredibly detailed analysis of what occurred.
In team sports, like soccer, that kind of information can be used to predict how opponents will react to a player in each in-game scenario. Coaches can use this information to add an element of psychological advantage to their strategies, by deploying players at advantageous moments. They can also use this to gain an accurate statistical picture of who the most valuable players are. These kinds of micro-data points lead to far more understanding of a good performance than mere point totals.
Of course, GIS data can be used to examine far more than a few individuals on a sports field. Team and league owners are using cross-referenced geospatial and population data to determine where to build stadiums, which stations to air games on, and even which cities to move teams to.
Finally, sports use the kinds of applications more traditionally associated with GIS in distance-based events, like the Tour De France and Iditarod, to track and map participants. This allows viewers worldwide to track their favorite participants in real-time. The more accurate the data, the more nuanced a picture of their performance we can see.
- GIS data can help athletes and coaches with small-scale strategy and predictive models for opponent behavior, as well as their own.
- Leagues and owners use the information to ensure their teams play to the biggest and most enthusiastic markets.
- Non-athletic advantages, like a more moderate travel schedule, can be identified and mitigated, ensuring as fair a competition as possible.
Humanitarian crises come in many forms. But whether violence, water scarcity, famine, viral outbreak, or a natural disaster, they all share a common enemy: Geospatial data.
In a humanitarian crisis, knowing the lay of the land is critical, especially for field teams coming from outside the affected area. The CDC’s field epidemiology manual even lists generating generic maps as a primary step in crisis response set-up.
Understanding natural and built environments is crucial for setting up operations and delivering supplies. Meanwhile, deeper data integration, like population data, helps determine need and aids logistics.
GIS data is already a proven necessity in matters of supply logistics, given its ability to cross-reference diverse data like weather, road closures and conditions, and population data.
This becomes increasingly important in areas where non-government actors control swaths of territory. When there are no ‘official’ sources to turn to, satellite imagery and heat mapping, not to mention cell phone use tracking tools, can shine light into information dark zones. This capability can help supplies from falling into the hands of agitators rather than helpers.
Post Crisis Evaluation
In the aftermath of a crisis, geospatial data helps tell the world what happened. A grim example – GIS data has been used to map unmarked execution sites and mass graves by cross-referencing soil readings and plant growth patterns from similar sites in the past.
In her 2018 speech to GEOINT, Linking the World CEO Mina Chang put forth a clear vision of ways the geospatial and big data industries can empower humanitarian causes. The results, she postulated, could help prevent crises from arising in the first place.
The Future of Crisis Management – Prevention
Right now, organizations react to events that occur and funding goes toward fixing the situation, which Chang says is similar to treating a symptom but not the underlying disease. With the predictive power of geospatial data, this is a paradigm that could shift toward crisis prevention, rather than crisis response. The captivating visual nature of interactive maps may also spur donations toward preventative measures – much the way images of disaster inspire people to support relief efforts.
Analyzing a story map like the one, or the WHO’s ebola map, provides an invaluable window into the spread of a virus. This data is now used as a teaching tool for those who must respond to the next humanitarian crisis. Data-rich models can, as Chang says, give helpers a look into the future by analyzing past human and viral behaviors over landmasses.
Current Day Use Case
GIS logistics planning is already being used to combat the spread of COVID-19.
On March 30, 2020 at Mayo Clinic’s Jacksonville, Florida campus, autonomous vehicles began delivering COVID-19 tests and medical supplies.
To address the current pandemic on a global scale, there are tools like Esri’s COVID-19 Tracking Dashboard, which provides data to researchers, governments, aid workers and the general public in real time.
- In current day, the use of geospatial data during humanitarian crises is critical for proper evaluation, tracking and response
- When there are no official sources to turn to, different forms of geospatial data, such as satellite imagery and population data, can be your only source of truth
- The focus needs to shift from humanitarian response to humanitarian prevention. By leveraging the ever-expanding geospatial databases and the many tools available, this vision of crisis prevention could become reality
- Interactive web-based maps can be an incredible tool for educating the world about the crisis whether that is real-time tracking or post-crisis evaluation and fundraising
Image Courtesy of Johns Hopkins University.
Drivers need maps to get where they’re going. As cars learn to drive by themselves, they’ll need maps, too.
As transportation responsibilities transfer from humans to machines, clear, reliable data is essential. A human can differentiate between a map and its surroundings, but to the Artificial Intelligence running autonomous vehicles, the map data is their world. The more data-rich the map, the safer the vehicle’s cargo, whether commercial or human. Standard maps cannot give daily updates on construction zones or changes in laws and driving regulations, but maps powered with specific geospatial data can. Even information like parking ramp usage can be used to improve the user’s experience. The geospatial information industry, with its diverse data handling ability, is poised to empower autonomous vehicles to take the wheel.
Some of the largest companies in the world are making investments with an eye toward the coming autonomous vehicle boom. Amazon’s recent heavy investment into companies like Rivian and Aurora comes alongside a commitment to begin electric vehicle delivery by 2021. In the automotive industry, companies like BMW are making sure their big data departments, which are used to further the pursuit of autonomous cars, are staffed with geospatial data experts.
The future they are preparing for is coming fast. In late 2019, a Plus.ai self-driving truck transported a load of Land O’ Lakes butter from California to Pennsylvania (2,800 miles). () It navigated the entire stretch of I-15 and I-75 all on its own, without a single intervention from the onboard safety driver.
Interstate trucking routes are typically predictable, long, straight stretches of highway. For a more dynamic environment, enhanced detection and imaging abilities are required. Self-driving passenger cars need to know the size, shape, and position of unexpected obstacles. And they need these details regardless of the weather. Enter: LiDAR.
Light Detection and Ranging, or LIDAR, gives cars a 360 degree, 3D image of their environment, using laser beams to define its surroundings. They usually take the form of a cylinder on a car’s roof. Today, units are capable of 1.3 million readings a second to create an image. While radar beams detect things over a broad swath to determine weather or large obstacles, LiDAR can define things down to a shirt button.
As cars drive around with LiDAR sensors, terabytes of information are generated. This scanning data can be used in pursuit of better mapping information. And, in turn, existing geospatial data can also help determine the accuracy of labels generated by AI.
Once the new data is organized, test divers of self-driving cars are sent back out with a new combination of scenarios, synthetic data, and machine learning algorithms. Then the cycle begins again.
It may seem strange, but cars are about to have the same real-world expectations of what maps must convey as we do.
Big news in the gaming world today- the highly anticipated game, “Animal Crossing: New Horizons” is being released! Though it may not be obvious, the geospatial field interfaces with the gaming world in many ways, even if the game is based in a fictional universe like it is in Animal Crossing.
One of the reasons this game has been so highly anticipated is due to the brand-new landscape and map options. In this version of the game, players are given their own island to explore and develop. Players will be presented with four different map options they can choose from, with each one having slightly different geographic features (different elevations, longer or shorter rivers, etc). Previously released images of the new maps (courtesy of Nintendo) show them to be quite simple, displaying only the necessary features- bodies of water, bridges, and a home base, among other things. Though the world of Animal Crossing is pixelated and filled with animal townspeople, the basic principles of mapping and cartography are still followed to a tee. Certain mapping conventions (i.e. water is blue, forest areas are green) have become so ingrained in what the collective expectation of a map is that there is often an adverse reaction from people if one of those conventions is not followed, even if the world is based on fiction.
For Animal Crossing specifically, the maps are predicted to start very simple and will likely get more complex as the player progresses and unlocks new features. This mirrors what happened with the earliest real-world cartographers- they made maps increasingly more detailed as they discovered more about their surroundings. Moreover, these maps became more complex as people found innovative and improved ways to capture, store, & use data. Nintendo went above and beyond to assure their gameplay mirrored the experience of being a real-life explorer. There have been rumors that there are many geologic features, such as cliffs and mountains, which will be discovered the more you explore the world, just as early trailblazers experienced. In fact, new discovery is still occurring today in places that are only just beginning to be explored, like the deep ocean and space.
There is a subset of video game design that is responsible for designing game maps, known as level design or environment design. While this entails much more than just designing fictional maps, displaying features on a map is crucial for positive gameplay experiences. These maps are often the first thing a player sees when beginning a new game and can guide players during in-game decision making. This is also true in the real world, maps can show a myriad of different features and can provide people with useful data that can aid in key decision making. A great example of this is how geospatial data is leveraged within insurance.
Whether the cartography is based on the real world or a fictional universe, almost all the same principles and conventions still apply. Our real-world expectations for what needs to be conveyed on a map have bled over into the world of simulation and gaming. As the games and features get more and more complex, so do the maps within the game experience, and, as a result, the gameplay experience itself becomes more immersive and enjoyable. It’s quite incredible to take a step back and realize how crucial maps are in our everyday lives. Whether you’re driving to a vacation destination using Google Maps or navigating the world of Animal Crossing: New Horizons utilizing their immersive map experience, there is always value to having a map on hand!