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A co-occurrence pattern is an interesting pattern in human mobility, which has essential values in business intelligence, social activities, and urban planning. However, due to the deluge and complexity of mobile big data, as well as the complicated intrinsic features of the co-occurrence pattern, mining and analyzing the co-occurrence pattern are computationally highly expensive. Therefore, in this paper, we propose a framework to mine co-occurrence event data from mobile data and to explore the urban co-occurrence pattern visually. Our framework contains two modules: data modeling, to obtain the co-occurrence event data effectively utilizing frequent itemsets mining algorithm based on traffic GPS records, and visualization, to explore the co-occurrence pattern in urban scenarios from global, regional, statistical, and location perspectives. Our visualization system has been demonstrated using case studies with …