  Modeling and estimation for spatial data are ubiquitous in real life,
frequently appearing in weather forecasting, pollution detection, and
agriculture. Spatial data analysis often involves processing datasets of
enormous scale. In this work, we focus on large-scale internet-quality open
datasets from Ookla. We look into estimating mobile (cellular) internet quality
at the scale of a state in the United States. In particular, we aim to conduct
estimation based on highly {\it imbalanced} data: Most of the samples are
concentrated in limited areas, while very few are available in the rest, posing
significant challenges to modeling efforts. We propose a new adaptive kernel
regression approach that employs self-tuning kernels to alleviate the adverse
effects of data imbalance in this problem. Through comparative experimentation
on two distinct mobile network measurement datasets, we demonstrate that the
proposed self-tuning kernel regression method produces more accurate
predictions, with the potential to be applied in other applications.
