Unseen species models from ecology have recently been applied to censored historical cultural datasets to estimate unobserved populations. We extend this approach to historical criminology, analyzing the police registers of Brussels' Amigo prison (1879-1880) using the Generalized Chao estimator. Our study aims to quantify the `dark number' of unarrested perpetrators and model demographic biases in policing efforts. We investigate how factors such as age, gender, and origin influence arrest vulnerability. While all examined covariates contribute positively to our model, their small effect sizes limit the model's predictive performance. Our findings largely align with prior historical scholarship but suggest that demographic factors alone may insufficiently explain arrest patterns. The Generalized Chao estimator modestly improves population size estimates compared to simpler models. However, our results indicate that more refined models or additional data may be necessary for robust estimates in historical criminological studies. This work contributes to the growing field of computational methods in humanities research and offers insights into the challenges of quantifying hidden populations in historical datasets.