  The availability of charging infrastructure is essential for large-scale
adoption of electric vehicles (EV). Charging patterns and the utilization of
infrastructure have consequences not only for the energy demand, loading local
power grids but influence the economic returns, parking policies and further
adoption of EVs. We develop a data-driven approach that is exploiting
predictors compiled from GIS data describing the urban context and urban
activities near charging infrastructure to explore correlations with a
comprehensive set of indicators measuring the performance of charging
infrastructure. The best fit was identified for the size of the unique group of
visitors (popularity) attracted by the charging infrastructure. Consecutively,
charging infrastructure is ranked by popularity. The question of whether or not
a given charging spot belongs to the top tier is posed as a binary
classification problem and predictive performance of logistic regression
regularized with an l-1 penalty, random forests and gradient boosted regression
trees is evaluated. Obtained results indicate that the collected predictors
contain information that can be used to predict the popularity of charging
infrastructure. The significance of predictors and how they are linked with the
popularity are explored as well. The proposed methodology can be used to inform
charging infrastructure deployment strategies.
