Inverse Distance Weighting (IDW) is a common method for spatial interpolation. Still, its accuracy decreases when there is a cluster of measurement stations or when some measuring stations are hidden behind others. This paper introduces Clusters Unifying Through Hiding Interpolation (CUTHI), a simple approach to enhance IDW accuracy. CUTHI calculates a weight for each station that considers its visibility from the interpolation point, reducing the influence of clustered or hidden stations. The method is tested in three cases: elevation data, rainfall measurements, and a mathematical function. Results demonstrate that CUTHI consistently outperforms traditional IDW, especially in areas with clustered measurement stations. CUTHI also treats the bull’s eye problem. This improved accuracy makes CUTHI a valuable tool for various applications requiring precise spatial interpolation.

Solving the hydrodynamical equations in urban canopies often requires substantial computational resources. This is especially the case when tackling urban wind comfort issues. In this article, a novel and efficient technique for predicting wind velocity is discussed. Reynolds-averaged Navier–Stokes (RANS) simulations of the Michaelstadt wind tunnel experiment and the Tel Aviv center are used to supervise a machine learning function. Using the machine learning function it is possible to observe wind flow patterns in the form of eddies and spirals emerging from street canyons. The flow patterns observed in urban canopies tend to be predominantly localized, as the machine learning algorithms utilized for flow prediction are based on local morphological features.