Accurate Predictions: A New Technique for Spatial Prediction Problems

Should you grab your umbrella before you walk out the door? Checking the weather forecast beforehand will only be helpful if that forecast is accurate. Spatial prediction problems, like weather forecasting or air pollution estimation, involve predicting the value of a variable in a new location based on known values at other locations. Scientists typically use tried-and-true validation methods to determine how much to trust these predictions.

A Flaw in the System

MIT researchers have shown that these popular validation methods can fail quite badly for spatial prediction tasks. This might lead someone to believe that a forecast is accurate or that a new prediction method is effective, when in reality that is not the case.

A New Approach

The researchers developed a technique to assess prediction-validation methods and used it to prove that two classical methods can be substantively wrong on spatial problems. They then determined why these methods can fail and created a new method designed to handle the types of data used for spatial predictions.

How it Works

Their new method assumes that validation data and test data vary smoothly in space. For instance, air pollution levels are unlikely to change dramatically between two neighboring houses.

Evaluating Validations

Broderick’s group has recently collaborated with oceanographers and atmospheric scientists to develop machine-learning prediction models that can be used for problems with a strong spatial component.

Conclusion

The researchers’ new method provides a more accurate way to evaluate spatial prediction methods, which can be applied to a range of problems, from predicting sea surface temperatures to estimating the effects of air pollution on certain diseases.

FAQs

How does the new method work? The new method assumes validation data and test data vary smoothly in space.

What are the limitations of traditional validation methods? Traditional methods make assumptions about how validation data and test data are related, which often break down in spatial applications.

What does the future hold for this research? The researchers plan to apply these techniques to improve uncertainty quantification in spatial settings and find other areas where the regularity assumption could improve the performance of predictors.