Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics.

Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes.

Multi-scale heterogeneity in vegetation and soil carbon in exurban residential land of southeastern Michigan, USA.

Exurban residential land (one housing unit per 0.2-16.2 ha) is growing in importance as a human-dominated land use.

Exploring agent-level calculations of risk and returns in relation to observed land-use changes in the US Great Plains, 1870-1940.

Land-use change in the U.S. Great Plains since agricultural settlement in the second half of the nineteenth century has been well documented.