Abstract: Systems and methods that infer and classify user activity based in part on routing data. A storage medium can store raw data collected, and such acquired data can be subsequently be analyzed or distilled to generate abstract qualities about the raw data (e.g., velocity of user during route, level of difficulty, and the like). Various machine learning techniques, artificial intelligence methodologies, decision trees, and/or statistical methods can be employed to supply inference regarding the acquired raw data and/or the abstract qualities.

Abstract: Space-time trails are annotated automatically and/or manually with meaningful information. Mechanisms are provided for receipt of implicit and/or passive data (e.g., provided by sensors) as well as explicit and/or active data (e.g., provided by users). Extraction techniques are provided to either or both types of data to produce useful information to annotate or otherwise interpret data associated with a space-time trail. Persisted annotated trails can subsequently be employed to glean valuable information. Furthermore, goal-directed recommendations can be generated with respect to annotated trails, among other things.