deletions | additions       diff --git a/Methodology Complimentary Data.tex b/Methodology Complimentary Data.tex  index c9c0565..d9ed6a3 100644  --- a/Methodology Complimentary Data.tex  +++ b/Methodology Complimentary Data.tex 
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Trajectory clustering is the first step in scenery interpretation. Trajectory clustering is an abstract representation of movements along prototypical paths through a scene, called alignments. This is the foundation for relating spatial position with road geometry and, in particular, position of moving objects in relation to lanes and sidewalks. This process introduces a new coordinate system mapping a position in cartesian space to curvilinear space.  (x,y)->(S,\gamma) \(x,y)->(S,\gamma)  Many approaches exist to trajectory clustering, some are produced manually, some are learned automatically. Manual trajectory clustering is labour intensive and potentially a source of bias, but allows for tight control of scenery description and analysis oversight. Larned clustering is systematic but naive as this form of clustering can only make use of trajectory data to infer position. The methodology is primarily based in manual trajectory clustering (called alignments), although a hybrid approach, which refines spatial positioning of manually defined alignments through traditional clustering approaches, is proposed for future work.