A study of spatial machine learning for business behavior prediction in location based social networks

Abstract

Understanding business behaviors requires acquiring huge amounts of data from diverse field studies. Location Based Social Networks can provide such large amounts of data that can be used in urban analysis to understand business behaviors. Towards more insight for business behavior, a novel analytical prespective that exploits data collected from Location Based Social Networks is introduced to predict business turnouts. Prediction is implemented using machine learning techniques. Spatial regression models are investigated through a comparative study to model the dataset features relationships for business behavior prediction. Geographically Weighted Regression model is found to be the most appropriate in predicting business turnouts of objects provided by Location Based Social Networks. Moreover, a Partitioned Geographically Weighted Regression model is proposed to deal with the data heterogeneity nature pursuing more accurate predictions for the business turnouts. An experimental case study, using data about venues registered in Foursquare is conducted to assess the performance of the proposed methods. The experimental results confirm the best performance by the Geographically Weighted Regression compared to Durbin, Durbin Error, Spatial Lag, Spatial Error, and Spatial Lag X regression models presented in this study. Moreover, the proposed Partitioned Geographically Weighted Regression model experimental results showed better prediction accuracy compared to the classical Geographically Weighted Regression model.