  Human mobility patterns refer to the regularities and trends in the way
people move, travel, or navigate through different geographical locations over
time. Detecting human mobility patterns is essential for a variety of
applications, including smart cities, transportation management, and disaster
response. The accuracy of current mobility prediction models is less than 25%.
The low accuracy is mainly due to the fluid nature of human movement.
Typically, humans do not adhere to rigid patterns in their daily activities,
making it difficult to identify hidden regularities in their data. To address
this issue, we proposed a web platform to visualize human mobility patterns by
abstracting the locations into a set of places to detect more realistic
patterns. However, the platform was initially designed to detect individual
mobility patterns, making it unsuitable for representing the crowd in a smart
city scale. Therefore, we extend the platform to visualize the mobility of
multiple users from a city-scale perspective. Our platform allows users to
visualize a graph of visited places based on their historical records using a
modified PrefixSpan approach. Additionally, the platform synchronizes,
aggregates, and displays crowd mobility patterns across various time intervals
within a smart city. We showcase our platform using a real dataset.
