Implementations of spatial regression discontinuity estimation and inference vary considerably in the literature. I show that many commonly used estimators for spatial RDDs, albeit working well in common scenarios, generally do not identify the local average treatment effect at the RD boundary. Second, I propose a way to report heterogeneous treatment effects alongside the RD cutoff. Third, I introduce randomization inference to the spatial RD framework by creating a set of functions that allow to randomly shift borders. These tools might be interesting for other identification strategies that rely on the shift of boundaries. A companion R-package, SpatialRDD, includes all the tools necessary to carry out spatial RD estimation, including the proposed improvements. The package makes such applications more transparent, and, more importantly ensures easy and straightforward replicability of all necessary steps.