September 14, 2017
PlanetRisk hearts maps. You might say we are map nerds. As students of cartography, geographic information systems (GIS), and location-based science, we are both fascinated and inspired by the rapid evolution maps have gone through in our generation– from the folded, paper maps we bought at gas stations, to the interactive smart phone maps we carry in our pockets.
This evolution of maps is an inevitable reflection of a broader trend; the proliferation of mobile and other IoT devices. This sensor-rich environment we find ourselves in is great for us as data scientists, but overwhelming to most consumers. As a CEOs, Security Managers, or Field Operators, it’s nearly impossible to keep track of what’s happening, where, and which areas need your attention (now, or in the future) without some kind of visual aid- especially as your assets are now less fixed and more mobile. The beauty of geospatial visualization (aka maps!) is that they quickly convey complex information in a language we can all understand.
Certainly, maps won’t always be the best way to tell a story, but they are indeed very effective when trying to convey the ‘atmospherics’ of an area, fundamental know-before-you-go information like crime rates, economic flows, climate dynamics, and mobile device traffic patterns. Further, this kind of data can be fused and analyzed to produce a risk score or predictive outlooks regarding future areas of conflict or concern. Then that scored data can be put right back onto the map you started with!
These visualizations don’t have be to one-dimensional either; time, for example, is one of space’s best friends. Showing how the crime rate in a particular municipality has risen or fallen over time can be critical to raising funds to hire more police officers. Elevation is another dimension well-served by geospatial analytics. Now we could use both space, time, and elevation to display of annual water levels to make crucial evacuation planning decisions.
Multi-dimensional visualizations like these are possible when they’re backed by the right processing technology. Using s a discrete global grid system (DGGS) allows for geospatial predictive modeling and comparative insights at varying resolutions. Think of a golf ball with equally small or large dimples, except the dimples are hexagonal, instead of round. Each dimple contains a package of data characterizing those atmospherics we just described; what that area looked like 40 years ago, what it looks like today, and what it might look like three weeks, six months, or five years from now.
We can also help users identify other areas with similar characteristics to learn what drivers were in play to influence the outcome. Did that municipality see a fall in crime due to the increase in officers, or did they experience an extreme weather season that drowned the criminals out? Perhaps mobile movement data would suggest the criminals are now dwelling in adjacent areas? These are the types of questions that can be asked of a platform using a DGGS. The brightest minds simply cannot measure up to technology when it comes to processing so many variables concurrently.
This doesn’t mean an analyst is cut out of the risk management process. Our models are self-learning, but we make sure our users can always trace back the assessments we make with our tools and tweak them to fit their needs. We believe this is the next generation way of thinking spatially- and are excited to bring our analytical solutions to the industry so others may adopt the same love for maps as we have.