In this paper, we propose a method for extracting the geometries of interest (GOIs) of a mobile object which we define as the geometries of the points of interest (POIs) which a mobile object frequently visits. Based on extracted GOIs the area of a long-term GPS trajectory is partitioned into a grid area with inhomogeneous shaped cells. This proposes a method consists of three phases: (i) extracting the geometry of stay regions based on the concepts of time-value and the time-weighted centroid, (ii) determining the geometry of destination regions based on the extracted stay regions using a geometry based hierarchical clustering, and (iii) partitioning the trajectory area based on the geometry of the destination regions and their visit frequency. The extracted GOIs can effectively represent the geometries of the POIs of the mobile object while guaranteeing the characteristics of a valid partitioning. The proposed method is tested using a field database covering the trajectory of a mobile object with the length of 3.5 years, and the achieved results are compared to the state-of-the-art. Our experimental results show that the proposed stay extraction can detect valid stay regions with only one track point while the other methods lose those stays. Moreover, analysis of the outcomes of the method using empirical observation shows that the quality of the extracted stay regions, destination regions and the GOIs extracted by our proposed method is considerably higher than those extracted by methods proposed in the state-of-the-art.
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