As location sensing devices are becoming ubiquitous, overwhelming amounts of data are being produced by the Internet-of-Things-That-Move. Though analyzing this data presents significant business opportunities, new techniques are needed to attain adequate levels of processing performance. One example is the recently introduced geohash geographical coordinate system that is mainly used for indexing. While geohash codes provide useful inherent properties such as hierarchical and variable-precision coding, traditional spatial algorithms operate on data represented using the conventional latitude/longitude geographical coordinate system, and as such do not take advantage of geohash coding. This paper tackles the evaluation of spatial predicates on geometries defined in the geohash domain, as an alternative to the standard Dimensionally Extended Nine-Intersection Model (DE-9IM). We present the first hardware architecture to efficiently evaluate «contain» and «touch» (internal, external, corner) relations between streams of pairs of geohash codes, in a high throughput (no stall) fashion. Employing FPGAs for exploiting the bit-level granularity of geohash codes, experimental results show (end-to-end) speedup of more than 20X and 90X over highly optimized single-threaded DE-9IM implementations of the contain and touch predicates, respectively. Furthermore, the PCIe-bound FPGA-based solution outperforms a geohash-based multithreaded CPU implementation by &1.8X (touch predicate) while using minimal FPGA resources.