Determining the Stopping Point on GPS Data Using Density Based Spatial Clustering of Application with Noise and Gaussian Mixture Model Cluster

Abstract

GPS data is an interesting thing to research. Various studies have been conducted to find information based on GPS data. In this paper, we propose a novel model for determining the stopping point on a GPS data for cases of human movement without using transportation modes. Further, this information can be used to determines human behavior such as fraud and favorite spot. The GPS data used in this research is the travel data of the SUSENAS survey officers at the time of updating the census block for 27 households. Density Based Spatial Clustering Of Application With Noise (DBSCAN) And Gaussian Mixture Model (GMM) Clustering model is used to create the model. The model made using a flowchart and applied to the GPS data that has been collected. The results of the developed model show that the stopping points generated using the DBSCAN cluster model are better than the stopping points generated using the GMM cluster model. Furthermore, the results of this study will be used to make model of surveyor fraud.