  Without sufficient preparation and on-site management, the mass scale
unexpected huge human crowd is a serious threat to public safety. A recent
impressive tragedy is the 2014 Shanghai Stampede, where 36 people were killed
and 49 were injured in celebration of the New Year's Eve on December 31th 2014
in the Shanghai Bund. Due to the innately stochastic and complicated individual
movement, it is not easy to predict collective gatherings, which potentially
leads to crowd events. In this paper, with leveraging the big data generated on
Baidu map, we propose a novel approach to early warning such potential crowd
disasters, which has profound public benefits. An insightful observation is
that, with the prevalence and convenience of mobile map service, users usually
search on the Baidu map to plan a routine. Therefore, aggregating users' query
data on Baidu map can obtain priori and indication information for estimating
future human population in a specific area ahead of time. Our careful analysis
and deep investigation on the Baidu map data on various events also
demonstrates a strong correlation pattern between the number of map query and
the number of positioning users in an area. Based on such observation, we
propose a decision method utilizing query data on Baidu map to invoke warnings
for potential crowd events about 1-3 hours in advance. Then we also construct a
machine learning model with heterogeneous data (such as query data and mobile
positioning data) to quantitatively measure the risk of the potential crowd
disasters. We evaluate the effectiveness of our methods on the data of Baidu
map.
