Given the variability in snowfall across Massachusetts at this time, to obtain a complete snow depth map, the challenge here is to interpolate the observed data to unobserved locations. This is the specialty of spatial statistics, and to solve this problem, we can draw from many of the sophisticated methods that have been developed in this discipline.
Before jumping to more complicated measures, it’s important to look at simpler methods for comparison. Simple averaging techniques are one attractive choice because of their intuitiveness. Unfortunately, many of these methods are inadequate to make an overall estimation. Here is one such example: a map of estimated snow depth values for January 31, 2015, where estimates of snow depth are weighted averages of the nearby observations. In this case, the weights are based on inverse spatial distances.
We can immediately see that these estimates of snowfall are unrealistic. In cases like this, simple averaging techniques suffer from various mathematical side effects. For example, the locations of maxima and minima in the above plot correspond directly to the weather station locations. This phenomenon, of course, does not correspond to a physical reality.
Without adding any more data to this scenario, we can make some positive steps by using more scientific—though also more complicated—spatial models.
Visually, this map gives a more convincing representation of snow depth. The locations of the weather stations are much less prominent in this map (though several can still be identified), and overall, the features in this image are more complex. Most importantly, prediction of snow depth using this model is more accurate. When we have better measures of snow depth, we can better account for travel times and how machinery will be affected.
Real Problems, Real Solutions, In Real Time
This spatial model is far from perfect. By adding more data and data sources to this estimation—for example, temperature data—we can find better measures of snow depth. At Uptake, however, it’s quite clear how useful these methods are for our goal of best accounting for the environmental impact on the machines we help monitor.
The power of spatial statistics, and the power of predictive modeling in general, doesn’t stop with weather prediction. A component that fails on a machine will damage other components, depending on its spatial location. Temperature in the hottest part of an engine—which indicates wear—isn’t always measured directly, but it can be inferred based on bordering temperature sensors and the kind of interpolation methods used in spatial statistics. Tracking sensor movements through time requires approaches similar to spatial methods.
At Uptake, we have assembled an elite team of technologists, data scientists, user experience experts and more, and we are building the most sophisticated platform to provide actionable insights in major industries. The breadth and depth of our data science team is allowing us to stop at nothing to create the most complete and actionable models addressed at our target industries. Put more simply, we are seeing opportunities in this data that have never before been seen. And as Uptake grows, our predictive models learn and grow—with time, we become even more valuable to our customers. We are at an inflection point in major industries where predictive insight has become an extremely valuable tool that will differentiate great companies from the rest. Our data science is at the center of this inflection—and just like data isn’t just numbers in a database, data science isn’t just a practice secluded away from the world. Uptake is solving real problems, and creating real solutions, in real time.