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We investigate the statistical properties of votes of customers for spots ofFrance collected by the startup company NOMAO. The frequencies of votes perspot and per customer are characterized by a power law distributions whichremain stable on a time scale of a decade when the number of votes is varied byalmost two orders of magnitude. Using the computer science methods we explorethe spectrum and the eigenvalues of a matrix containing user ratings togeolocalized items. Eigenvalues nicely map to large towns and regions but showcertain level of instability as we modify the interpretation of the underlyingmatrix. We evaluate imputation strategies that provide improved predictionperformance by reaching geographically smooth eigenvectors. We point onpossible links between distribution of votes and the phenomenon ofself-organized criticality.

Statistical analysis of NOMAO customer votes for spots of France