  Temporal contact networks are studied to understand dynamic spreading
phenomena such as communicable diseases or information dissemination. To
establish how spatiotemporal dynamics of nodes impact spreading potential in
colocation contact networks, we propose "inducement-shuffling" null models
which break one or more correlations between times, locations and nodes. By
reconfiguring the time and/or location of each node's presence in the network,
these models induce alternative sets of colocation events giving rise to
contact networks with varying spreading potential. This enables second-order
causal reasoning about how correlations in nodes' spatiotemporal preferences
not only lead to a given contact network but ultimately influence the network's
spreading potential. We find the correlation between nodes and times to be the
greatest impediment to spreading, while the correlation between times and
locations slightly catalyzes spreading. Under each of the presented null models
we measure both the number of contacts and infection prevalence as a function
of time, with the surprising finding that the two have no direct causality.
