Big Data has been effectively mined to understand behavioural patterns in cities and to map large-scale trends predicated upon the repeated actions of many aggregated individuals. While acknowledging the vital role that this work has played in harnessing the Urban Internet of Things as a means to ensure efficient and sustainable urban systems, our work seeks to recover a scale of behavioural research associated with earlier, empirical studies on urban networks. UrbanIOT dataexpands the depth and precision of intimate behavioural analysis; small-scale analysis lends insight into important anomalies not explained by large-scale trends. The three case studies at stake here combined empirical journaling with data from mobile devices, tracking both automatically and through user reporting. Each produced diverse information and visualizations for describing the interaction of individual citizens, resources and urban systems. These are: a description of behaviours relative to food stores and shopping habits in New York City, US; a description of the correlation between mobility and food waste likelihood in Providence, RI, US; and a study of mobility patterns and personal choices in Copenhagen, DK.