Enhancing geospatial precision in conflict data: A stochastic approach to addressing known geographically imprecise observations in conflict event data

The proliferation of large-scale, geographically disaggregated data on armed conflicts, protests, and similar events has opened new avenues of research, but has also introduced significant data quality challenges. A notable yet often overlooked issue involves observations with “known geographic imprecision” (KGI), where event locations are unknown and instead arbitrarily assigned by dataset authors. Although this issue is widely recognized and accounts for up to a quarter of observations in datasets like UCDP GED, it is rarely addressed by users. This paper presents a stochastic method derived from the multiple-imputation literature, employing spatio-temporal Gaussian processes and leveraging latent actor-conflict features in the data to enhance location accuracy. Extensive Monte-Carlo simulations demonstrate that this approach substantially enhances the accuracy of these observations and improves predictive performance beyond the state-of-the-art when applied out-of-sample. Additionally, an adapted version of the UCDP GED dataset that employs this new procedure is provided, showcasing the practical application and benefits of the methodology.