Imagine having a grid of categorical outcomes, and each subject is assigned a position each year. You want to analyze movement patterns across the grid over multiple time points. Beyond basic transition matrices, there are more advanced statistical approaches to explore.
One potential approach is to use Markov-style models, which can help capture the probability of moving from one state to another in a discrete 2D space. Another option is to apply sequence alignment or clustering techniques to movement paths, allowing you to identify patterns and group similar movements together.
To capture directionality and variance in movement, you can utilize statistical tools such as spatial autocorrelation or network analysis. These methods can help reveal underlying structures and relationships in the data.
If you’re looking for more resources on this topic, I recommend checking out literature on spatial analysis, Markov chain models, and sequence alignment algorithms. These can provide a solid foundation for analyzing movement patterns across categorical 2D states over time.