Creating Maps of Risks and Opportunities on Moving Direction Entropy for Urban Redevelopments

As in the lessons learned by the authors so far, moving direction entropy (MDE) represents the risk of COVID-19 infection spread as well as the interactive activities of people from different contexts. Here, the MDE is computed from mobility data for each mesh of 100m square in Tokyo to visualize the heatmaps for times in a day. In comparison, the number of moving people and areas of planned redevelopment, such as the extension of train transportation lines, are visualized. As a result of the comparison, we found that the high MDE meshes correspond to the planned areas, which implies the latent risk to enhance the infection expansion in those areas, as well as the expected prosperity of economics. Thus, our message is to combine the policies of urban redevelopment and care for the spread of infection and apply our simple method to compute the MDE.