Human location tracking and analysis have always been important domains with a wide range of implementations in areas such as traffic prediction, security, disaster response, health monitoring, etc. With the availability and use of GPS-enabled devices, it has become easier to obtain location traces of an individual. There may be situations when the current location may be difficult to trace due to device failures or any unforeseen situation. This is one reason why geo tagged social networking or LBSN (location-based social networking) data research is gaining popularity. This kind of geo-tagged data when collected over time from a crowd can be analyzed for various mobility patterns of the population. This chapter focuses on how to predict the location of the people during mobility. A sample study to predict the geographical location and points of interest of a user is explained with the help of random forest classifier. Also, the chapter highlights the security and privacy concerns when LBSN is used for human mobility analysis.