In the era of social networks, we have a huge amount of social geotagged data that reflect the real world. These data can be used to provide or to enhance situational and public safety awareness. It can be reached by the way of analysis and visualization of geotagged data that can help to better understand the situation around and to detect local geo-spatial threats. One of the challenges in the way of reaching this goal is providing valuable statistics and advanced methods for filtering data. Therefore, in the scope of this paper, we collect sufficient amount of public social geotagged data from Twitter, build different valuable statistics and analyze them. Also, we try to find valuable parameters and propose the useful filters based on these parameters that can filter data from invaluable data and, by this way, support analysis of geotagged data from the perspective of providing situational awareness.