# How to analyze serial correlation [using time series]

I have a set of numeric data that is indexed by Position, from 1 to n. This is serial data in the sense that the data is ordered by the position, which corresponds to a physical structure rather than a temporal one. My question is, how do I infer the spatial scale at which the data points correlate with one another? Data points that are closer together should be related more than data points that are further apart, but to what extent?

I was told that time series analysis should be useful here. I don't have much experience in the topic, but it seems like one difference is that time only goes in one direction, while space does not have that restriction. More general series analysis tools seem like they would be helpful here.

I have been looking into autocorrelation coefficients, which will tell the relatedness of the data series with itself lagged a certain number of units. Is this the right track?

When using the autocorrelation, the values of autocorrelation coefficient should be less than $$\pm 2/\sqrt{n}$$ to claim that the data is uncorrelated. $$n= \text{sample size}$$.