What are some metrics I can use to measure the similarity/distance between a pair of variables - each variable consisting of two different types of data(i.e. each variable has spatial and temporal data)?

EDIT: Just to clarify, the basic solution I can come up with is to do a comparison between each time series of the two different items and a comparison between the spatial values of the same items and measure the similarity between the two items as a some average of the two similarity results. However, I was hoping to see if there was anything more sophisticated, for instance, I think mutual correlation coefficient might be applicable.

Edit: Added much more specificity.

I have a list of N components, X_1 ... X_n, for each of my K subjects, S_1...S_k. Each X component has two features: feature1 = a time series consisting of ~200 points of data. feature2 = a set of continuous variables. Given this setup, I would like to compare two components, X_a and X_b, to each other by comparing X_a.feature1 against X_b.feature1 and X_a.feature2 against X_b.feature2. I would like to use these two comparisons to produce a single value measuring the similarity between component X_a and component X_b. The goal of analyzing similarity between components is to say that out of all components from subject S_m, component X_x best corresponds to component X_y from subject S_n. I hope these details help.