  Urban conditions are monitored by a wide variety of sensors that measure
several attributes, such as temperature and traffic volume. The correlations of
sensors help to analyze and understand the urban conditions accurately. The
correlated attribute pattern (CAP) mining discovers correlations among multiple
attributes from the sets of sensors spatially close to each other and
temporally correlated in their measurements. In this paper, we develop a
visualization system for CAP mining and demonstrate analysis of smart city
data. Our visualization system supports an intuitive understanding of mining
results via sensor locations on maps and temporal changes of their
measurements. In our demonstration scenarios, we provide four smart city
datasets collected from China and Santander, Spain. We demonstrate that our
system helps interactive analysis of smart city data.
