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This paper focuses on measuring the spatial correlation among functional data streams recorded by sensor networks. In many real world applications, spatially located sensors are used for performing at a very high frequency, repeated measurements of some variable. Due to the spatial correlation, sensed data are more likely to be similar when measured at nearby locations rather than in distant places. In order to monitor such correlation over time and to deal with huge amount of data, we propose a strategy based on computing the well known Moran's index on summaries of the data.