  We demonstrate how digital traces of city-bike trips may become useful to
identify urban space attractiveness. We exploit their unique feature -
stopovers: short, non traffic-related stops made by cyclists during their
trips. As we demonstrate on the case-study of Krakow (Poland), when applied to
a big dataset, meaningful patterns appear, with hotspots (places with long and
frequent stopovers) identified at both the top tourist and leisure attractions
as well as emerging new places.
  We propose a generic method, applicable to any spatiotemporal city-bike
traces, providing results meaningful to understand both the general urban space
attractiveness and its dynamics. With the proposed filtering (to mitigate a
selection bias) and empirical cross-validation (to rule-out false-positive
classifications) results effectively reveal spatial patterns of urban
attractiveness. Valuable for decision-makers and analysts to enhance
understanding of urban space consumption patterns by tourists and residents.
