With the fast-growing public transportation and increasing socio-economic activities, population mobility has become a concerned problem in the prevention of infectious diseases. Ever since the outbreak of SARS, the central role that Geographic Information System (GIS) plays in the early detection and rapid response to infectious diseases has become evident to researchers as well as policy markers [14, 15] . However, how to integrate GIS into the traditional epidemiological model is a key challenge [16] . Use of GIS in our analyses in this study showed that the incidence of H1N1 infection in the 2009 pandemic in mainland China was higher in the coastal provinces in the southeast as well as in Beijing and Shanghai, two of the most populous cities in the country, where the infection occurred in clusters in the initial phase, followed by spread to adjacent provinces. Moreover, the analyses also demonstrated that within some of the provinces with confirmed cases, the number of H1N1 infection varied widely as well. In most cities and districts, there was still no H1N1 confirmed cases, the overall spread level of H1N1 virus was still regional. Nevertheless, our analyses on the H1N1 spread and the geographic distribution were qualitative only; further quantitative analyses such as correlation or Poisson regression are needed to test the hypothesis that the H1N1 virus spreads more quickly in the crowd area with more convenient travel system.