The aim of this study was to develop and validate a robust algorithm for indoor and outdoor wandering pattern detection and to analyse the relationship of these patterns to other clinical measures. Much of the previous work in this area addressed the measurement of wandering indoors or outdoors and to the best of our knowledge, there has not been a unified algorithm proposed which can deal with both scenarios. We present a novel grid-based layout representation strategy to identify the patterns, which is applicable to both indoor and outdoor scenarios. The algorithm is sufficiently robust to identify interleaving and hybrid patterns and performed with identification accuracy of 90% on a real-world sample.