Ravi Bhavnani provides a summary of his article, “Group Segregation and Urban Violence,” co-authored by Karsten Donnay, Dan Miodownik, Maayan Mor, and Dirk Helbing, and appearing in the January 2014 issue of the AJPS:
Researchers from the Graduate Institute of International and Development Studies (IHEID) in Geneva, ETH Zurich, and the Hebrew University of Jerusalem have developed a computer model to better understand the sources and patterns of violence in urban areas, employing Jerusalem as a demonstration case and seeding their model with micro-level, geo-coded data on settlement patterns for each of the city’s seventy-seven neighborhoods. They focus on social distance—be this religious, ethnic or ideological, class or gender-based—as a key mechanism to explain violence. All else equal, higher levels of social distance increase the likelihood that day-to-day contact between members of nominally rival groups leads to violence.
Using the model, the research team examined the distribution of violence under four proposed scenarios for the future status of Jerusalem: “Business-As-Usual”; “Clinton Parameters”; “Palestinian Proposal”, and “Return to 1967”. Findings from the study suggest that settlement patterns associated with the “Return to 1967” scenario would most dramatically curb violence in the city, although the team remains agnostic as to whether such a fundamental reconfiguration of the urban space in this city or any other is necessarily desirable, even leaving aside issues of feasibility. They further stress that reducing violence in the individual scenarios depends critically on the state of intergroup relations—characterized by social distance—and that these relations may change as a result of the political wrangling behind the adoption of a particular policy for the city’s future status.
In contested urban areas like Jerusalem, this research underscores the notion that there are various possibilities for peace, all highly contingent on the nature of group relations. The strength of the approach pursued lies in its ability to compare various alternatives or “futures” in a manner that is amenable to calibration and validation, with real-world plausibility and application.