Extrapolative simulation of neighborhood networks based on population spatial distribution: Do they predict crime?

• A spatial interaction function is used to simulate the network of ties in a city.
• Cohesion and information diffusion measures are computed from the simulated network.
• These network measures are related to the spatial distribution of crime in 5 cities.
• The relationships are stronger at the block level than at the block group level.
• The presence of more within-area ties is the strongest predictor of low crime rates.

ObjectivesPrevious criminological scholarship has posited that network ties among neighborhood residents may impact crime rates, but has done little to consider the specific ways in which network structure may enhance or inhibit criminal activity. A lack of data on social ties has arguably led to this state of affairs. We propose to avoid this limitation by demonstrating a novel approach of extrapolatively simulating network ties and constructing structural network measures to assess their effect on neighborhood crime rates.MethodsWe first spatially locate the households of a city into their constituent blocks. Then, we employ spatial interaction functions based on prior empirical work and simulate a network of social ties among these residents. From this simulated network, we compute network statistics that more appropriately capture the notions of cohesion and information diffusion that underlie theories of networks and crime.ResultsWe show that these network statistics are robust predictors of the levels of crime in five separate cities (above standard controls) at the very micro geographic level of blocks and block groups.ConclusionsWe conclude by considering extensions of the approach that account for homophily in the formation of network ties.