We present a two-step strategy that addresses fundamental deficiencies in social media-based event detection and achieves effective local event by taking advantage of geo-located data from Twitter. While previous work has mainly relied on an analysis of tweet text to identify local events, we show how to reliably detect events using meta-data analysis of geo-tagged tweets. The first step of the method identifies several spatio-temporal clusters within the dataset across both space and time using metadata to form potential candidate events. In the second step, it ranks all the candidates by the amount of hashtag/entity inequality. We used crowdsourcing to evaluate the proposed approach on a data set that contains millions of geo-tagged tweets. The results show that our framework performs reasonably well in terms of precision and discovers local events faster.