  In this paper, we address the problem of uncertainty quantification for
cellular network speed. It is a well-known fact that the actual internet speed
experienced by a mobile phone can fluctuate significantly, even when remaining
in a single location. This high degree of variability underscores that mere
point estimation of network speed is insufficient. Rather, it is advantageous
to establish a prediction interval that can encompass the expected range of
speed variations. In order to build an accurate network estimation map,
numerous mobile data need to be collected at different locations. Currently,
public datasets rely on users to upload data through apps. Although massive
data has been collected, the datasets suffer from significant noise due to the
nature of cellular networks and various other factors. Additionally, the uneven
distribution of population density affects the spatial consistency of data
collection, leading to substantial uncertainty in the network quality maps
derived from this data. We focus our analysis on large-scale internet-quality
datasets provided by Ookla to construct an estimated map of connection quality.
To improve the reliability of this map, we introduce a novel conformal
prediction technique to build an uncertainty map. We identify regions with
heightened uncertainty to prioritize targeted, manual data collection. In
addition, the uncertainty map quantifies how reliable the prediction is in
different areas. Our method also leads to a sampling strategy that guides
researchers to selectively gather high-quality data that best complement the
current dataset to improve the overall accuracy of the prediction model.
