In this paper, we investigate the problem of detecting real-time city-scale hyper-local events based on the analysis of social media and human mobility. Different from general events reported by news media, hyper-local events refer to both small-scale and large-scale events pertaining to a geographical location. Since small-scale events, e.g, a party in a pub, an exhibition in a local museum, are not often reported through mainstream media platforms, such as newspapers, TV news, web media or government report, it is challenging to obtain the resources related to such kind of events. Besides, those media platforms have a great latency in reporting the news of ongoing events, resulting in that the events we saw might take place a couple of days ago. Though people have tried to find clues of events from real-time social media streams (e.g., Instagram and Twitter), the scarcity of social posts with geo-tagged information leads to a very low quality of localized event detection. In this paper, in addition to the data from social media stream, we apply human mobility data which contain rich spatial-temporal information as another important resource to improve the performance of hyper-local event detection. Specifically, we use taxi data as it is expected that the occurrence of hyper-local events usually leads to the change in the surrounding traffic. As far as our knowledge, this is the first work which combines multiple social media data sources with human mobility information for the task of real-time hyper-local event detection. We propose a two-step framework which is composed of an anomaly filter and an event classifier. Through experiments on New York City data, we show that our proposed system can effectively detect both small-scale and large-scale local events. Furthermore, we verify that applying human mobility data can significantly enhance the performance of event detection and classification.