UC Irvine

Defining “neighborhoods” is a bedeviling challenge faced by all studies of neighborhood effects and ecological models of social processes.  Although scholars frequently lament the inadequacies of the various existing definitions of “neighborhood”, we argue that previous strategies relying on non-overlapping boundaries such as block groups and tracts are fundamentally flawed.  The approach taken here instead builds on insights of the mental mapping literature, the social networks literature, the daily activities pattern literature, and the travel to crime literature to propose a new definition of neighborhoods:  egohoods.  These egohoods are conceptualized as waves washing across the surface of cities, as opposed to independent units with non-overlapping boundaries.  This approach is illustrated using crime data from nine cities:  Buffalo, Chicago, Cincinnati, Cleveland, Dallas, Los Angeles, Sacramento, St. Louis, and Tucson.  The results show that measures aggregated to our egohoods explain more of the variation in crime across the social environment than do models with measures aggregated to block groups or tracts.  Results also suggest that measuring inequality in egohoods provides dramatically stronger positive effects on crime rates than when using the non-overlapping boundary approach, highlighting the important new insights that can be obtained by utilizing our egohood approach.