Ripley's K-function is used to analyze the spatial pattern of point data. It can detect global spatial clustering in individual-level data. In essence, you can use it to compare the observed pattern of cases with that generated by a homogenous Poisson process.
A K-function is estimated for the observed data, and then it is compared to an expected K-function for a Poisson distribution using a scaled metric, L(h). Additionally, you can visualize the observed L(h) to Monte Carlo randomizations of the data.
