Geospatial data analysis could become more environmentally-friendly thanks to a groundbreaking new software model built by a team of scientists from a Scottish university.
Researchers from the University of Glasgow are behind the development of the model, called 'GeoAggregator' , which harnesses the power of machine learning to reduce the computational demands of analysing complex geospatial data sets.
As GPS technology and satellite data become more widespread, massive amounts of geospatial information are being collected daily. However, making sense of this data requires sophisticated modelling techniques that capture complex spatial relationships, which traditional statistical methods and even existing AI models struggle with.
GeoAggregator addresses these challenges by introducing a lightweight transformer-based AI model that efficiently analyses spatial autocorrelation how nearby places influence each other and spatial heterogeneity how patterns vary from one location to another. Unlike conventional deep learning models that demand huge amounts of computing power, GeoAggregator is faster, more scalableand requires fewer resourcesmaking it more accessible for researchers and policymakers.
One of GeoAggregators key innovations is its Gaussian-biased local attention mechanism, which helps the model focus on relevant nearby data points while still considering the broader spatial context. This approach enhances predictions for a variety of spatial data problems, such as forecasting air pollution levels, identifying housing price trends, and analysing poverty distribution.