Spatial-temporal analysis of social disintegration

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This project addresses the relationship between civilian harm and the deliberate destruction of sites, monuments, and objects having group, religious, and artistic significance. Theory and evidence are unclear on this issue. In this project, time-space analytical techniques will be trained on a new dataset of incidents from a region in which both kinds of events occurred prominently at high rates. The case is valuable for study because of the strong evidentiary base and because destructive incidents included many that existing theory would not have predicted. Specifically, many incidents had neither apparent strategic advantage or ramifications for the civilian population. Variations in technique, target, and timing will yield important insights into the intensity and location of civilian targeting and will assist scholars and decision makers to protect both humans and cultural sites and objects.Newly available information from these events will be coded to support spatial and temporal analysis of two key categories of disruption: civilian harm and deliberate cultural destruction. Sociological theory has tended to view cultural destruction as an aspect of psychological hostile actions taken against a population. This needs to be verified, however, by systematic analysis of the co-occurrence of the two kinds of disruption. This project capitalizes on the dense and unusually detailed record of cultural destruction generated by official actors, less sanctioned groups, and civilian witnesses, including cell phone footage, films, print materials, and satellite images. Data come from a three-year period (2014-2017) that saw a popular movement that fractured into multiple (often opposed) factions, interventions from the outside, intervention more locally, and overwhelming levels of the incidents in question. The resulting dataset will include georeferencing information in order to analyze the clustering of civilian harm before, during, and after cultural destruction events. Analysis will proceed via appropriate statistical techniques including autoregressive distributed lag models (ARDL), clustering, vector autoregression (VAR), and event history analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.