Risk Assessment of Crowd-Gathering in Urban Open Public Spaces Supported by Spatio-Temporal Big Data

The urban open public spaces are the areas where people tend to gather together, which may lead great crowd-gathering risk. This paper proposes a new method assess rank and spatial distribution of risk in large area. Firstly, crowd density estimation based on Tencent user (TUD) data is built for different times spaces. Then, reasonable threshold delimited detect critical situations find out key that need have intensive prevention. For estimating spaces, quantified assessment approach conducted classical theory simultaneously considers probability an accident occurring, severity consequence, aversion factor. A case study area within Outer-ring Road Shanghai was determine feasibility method. thematic maps describe ranks were generated. According maps, government can control measures reduce prevent dangerous events.