Outdoor surveillance cameras have become prevalent as part of the urban infrastructure, and provided a good data source for studying urban dynamics. In this work, we provide a spatial-temporal analysis of 8 weeks of video data collected from the large outdoor camera network at UCSB campus, which consists of 27 cameras. We first apply simple vision algorithm to extract the crowdedness information in the scene. Then we further explore the relationship between the traffic pattern observed from the cameras with activities in the nearby area using additional knowledge such as campus class schedule. Finally we investigate the potential of discovering aggregated human movement pattern by assuming a simple probabilistic model. Experiment has shown promising results using the proposed method.