Reproducible quantitative research is research that has been documented sufficiently rigorously that a third party can replicate any quantitative results that arise. It is argued here that such a goal is desirable for quantitative human geography, particularly as trends in this area suggest a turn towards the creation of algorithms and codes for simulation and the analysis of Big Data. A number of examples of good practice in this area are considered, spanning a time period from the late 1970s to the present day. Following this, practical aspects such as tools that enable research to be made reproducible are discussed, and some beneficial side effects of adopting the practice are identified. The paper concludes by considering some of the challenges faced by quantitative geographers aspiring to publish reproducible research.