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We propose {\delta}-MAPS, a method that analyzes spatio-temporal data tofirst identify the distinct spatial components of the underlying system,referred to as "domains", and second to infer the connections between them. Adomain is a spatially contiguous region of highly correlated temporal activity.The core of a domain is a point or subregion at which a metric of localhomogeneity is maximum across the entire domain. We compute a domain as themaximum-sized set of spatially contiguous cells that include the detected coreand satisfy a homogeneity constraint, expressed in terms of the averagepairwise cross-correlation across all cells in the domain. Domains may bespatially overlapping. Different domains may have correlated activity,potentially at a lag, because of direct or indirect interactions. The proposededge inference method examines the statistical significance of each laggedcross-correlation between two domains, infers a range of lag values for eachedge, and assigns a weight to each edge based on the covariance of the twodomains. We illustrate the application of {\delta}-MAPS on data from twodomains: climate science and neuroscience.

{\delta}-MAPS: From spatio-temporal data to a weighted and lagged network between functional domains