Recent years have witnessed much work unraveling human mobility patterns through urban visitation and location check-in data. Traditionally, user visitation and check-in have been assumed as the same behavior, yet this fundamental assumption can be questionable and lacks supporting evidence. In this paper, we seek to understand the similarities and differences of visitation and check-in by presenting a large-scale systematic analysis under the specific setting of urban revisitation and re-check-in, which demonstrate people's periodic behaviors and regularities. Leveraging a localization dataset to model urban revisitation and a Foursqaure dataset to delineate re-check-in, we identify features concerning POI visitation patterns, POI background information, user visitation patterns, user preference and users' behavioral characteristics to understand their effects on urban revisitation and re-check-in. We examine the relationship between revisitation/re-check-in rate and the features we identify, highlighting the similarities and differences between urban revisitation and re-check-in. We demonstrate the prediction effectiveness of the identified characteristics utilizing machine learning models, with an overall ROC AUC of 0.92 for urban revisitation and 0.82 for re-check-in, respectively. This study has important research implications, including improved modeling of human mobility and better understanding of human behavior, and sheds light on designing novel ubiquitous computing applications.