Abstract:
The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone
across the world. Reduced mobility was essential due to it being the largest impact
possible against the spread of the little understood SARS-CoV-2 virus. To understand the
spread, a comprehension of human mobility patterns is needed. The use of mobility data in
modelling is thus essential to capture the intrinsic spread through the population. It is
necessary to determine to what extent mobility data sources convey the same message of
mobility within a region. This paper compares different mobility data sources by
constructing spatial weight matrices at a variety of spatial resolutions and further
compares the results through hierarchical clustering. We consider four methods for
constructing spatial weight matrices representing mobility between spatial units, taking
into account distance between spatial units as well as spatial covariates. This provides
insight for the user into which data provides what type of information and in what situations
a particular data source is most useful.
