Forecasting people's future location information using data mining methods

The increasing popularity of smartphones over the last few years and the popularity of applications such as FourSquare, which use location services provided by these phones, have enabled us to obtain detailed information about people's mobility habits. In this study, it was aimed to estimate locations where people could visit using past location shares on FourSquare. It is envisaged that the study will contribute to the study of criminal investigations, enterprise popularity and personality analysis. Within the scope of the study, people's tweets about their FourSquare check-ins were analyzed. The categories of places that were visited on the Shares were taken from Foursquare. The collected spatial data were analyzed using decision tree and gradient boosted trees learner techniques and the findings obtained by estimating the categories of visited places were examined.