Modeling ‘A Sense of Place’: Evaluating the Roles of Knowledge and Reputation in Neighborhood Dynamics via Online Surveys

The processes driving neighborhood change have been of persistent interest to social scientists for decades. While researchers have developed many theories of how neighborhoods change over time – from ecological models of invasion-succession to models of residential preference and neighborhood selection – much of this work has relied on hypothetical neighborhoods, rather than real urban contexts, or made implausible assumptions about individual behavior to draw conclusions. As a result, these theories fail to adequately model the micro-processes underlying neighborhood change. In particular, researchers have under-theorized the importance of (mis)perception and reputation as key mechanisms driving neighborhood evolution.

In much urban research, it is assumed that individuals are omniscient and accurate observers of their environments, possessing full knowledge of local demographics and aggregate occurrences of events like crime and residential turnover. However, there is ample evidence that individuals have limited knowledge of their surroundings and are poor judges of their environments. Given that individuals’ perceptions of their communities rarely match reality, it is important to develop more cognitively plausible models of what individuals know, how they experience, and how they make choices about their neighborhoods. By studying one’s “sense of a place” – their neighborhood knowledge, perception of neighborhood desirability, and the gap between real and perceived neighborhood conditions – rather than focusing on objective features of neighborhoods, one can uncover latent, underlying motivations for behaviors that lead to neighborhood change over time.

This report examines ways in which individuals’ perceptions and gaps in knowledge may influence neighborhood dynamics, using data from a pilot survey developed to capture residents’ knowledge of and experience with neighborhoods in three U.S. cities.