Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Mousavi, Seyed Morteza; Harwood, Aaron; Karunasekera, Shanika; Maghrebi, Mojtaba

doi:10.1007/s12652-016-0400-5

Cited by 14 publications

(33 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During this cluster formation, researchers use two thresholds: (1) a maximum distance threshold between any pair of points within the cluster, which is the maximum distance that a user can cover in a place and (2) a minimum time duration threshold, which is the minimum duration that the user needs to stay in the same cluster. Other researchers have also used similar spatio-temporal techniques that rely on both distance and time thresholds [27], [28].…”

Section: Related Workmentioning

confidence: 99%

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

Vhaduri

Poellabauer

2021

IEEE Trans. Big Data

View full text Add to dashboard Cite

Modern smartphones and wearables are able to continuously collect significant amounts of sensor data, where such data can be helpful to study a user's mobility or social interaction patterns, but also to deliver various services based on a user's presence at different places during certain times of the day. Therefore, it is important to accurately identify personal places of interest (POIs), such as a user's workplace or home. Such places are usually determined using segmentation of location traces, but frequent gaps in the data (i.e., missing location readings) can result in a large number of small and incomplete segments that should actually be grouped together into a single large segment. This paper presents a segmentation approach that utilizes a user's personal data obtained from multiple sensor sources and devices such as the battery recharge behavior (measured on smartphones), step counts, and sleep patterns (measured by wearables), to opportunistically fill gaps in the user's location traces. Using the data from a mobile crowd sensing study of more than 450 users over a 2-year period, we show that our approach is able to generate fewer, but more complete segments compared to the state of the art.

show abstract

Section: Related Workmentioning

confidence: 99%

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

Vhaduri

Poellabauer

2021

IEEE Trans. Big Data

View full text Add to dashboard Cite

show abstract

“…Multiple sources from the literature have studied the dynamic solution by considering different aspects of mobility characteristics [5][6][7][8][9][10]. Considering only the spatial characteristics, several classical clustering algorithms are introduced to extract stops from a trajectory.…”

Section: Related Workmentioning

confidence: 99%

An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories

Luo

Zheng

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based spatial clustering of applications with noise) is a classical density-based algorithm used to find the high-density areas in space, and different derivative methods of this algorithm have been proposed to find the stops in trajectories. However, most of these methods required a manually-set threshold, such as the speed threshold, for each feature variable. In our research, we first defined our new concept of move ability. Second, by introducing the theory of data fields and by taking our new concept of move ability into consideration, we constructed a new, comprehensive, hybrid feature-based, density measurement method which considers temporal and spatial properties. Finally, an improved DBSCAN algorithm was proposed using our new density measurement method. In the Experimental Section, the effectiveness and efficiency of our method is validated against real datasets. When comparing our algorithm with the classical density-based clustering algorithms, our experimental results show the efficiency of the proposed method.

show abstract

“…Figure 1 shows the GOI extracted using the proposed method in (Mousavi et al, 2016). As it is clearly evident, the GOIs (polygons depicted with blue color) have acceptable geometric similarity with the real world GOIs (depicted with red polygons).…”

Section: Introductionmentioning

confidence: 98%

“…They refer to the geometry of the POIs as the Geometries of Interest (GOIs). Based on the results reported in (Mousavi et al, 2016), the geometries of the extracted GOIs are significantly more similar to the real world GOIs compared to the base line methods (Ye et al, 2009;Hariharan and Toyama, 2004). Despite the fact, the quality of the extracted GOIs is required to be enhanced to resemble the real world GOIs more accurately.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing the quality of geometries of interest (GOIs) extracted from GPS trajectory data using spatio-temporal data aggregation and outlier detection

Mousavi

Harwood

Karunasekera

et al. 2016

J Ambient Intell Human Comput

Self Cite

View full text Add to dashboard Cite

One of the initial phases in the applications dealing with data processing on GPS trajectory data is to generate the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The sequence is constructed by labeling each of the GPS observations of the trajectory by the ID of their intersecting Geometries of Interest (GOIs). In this paper, we enhance the performance of the state-of-the-art scheme for constructing the GOIs of a mobile object by proposing a data aggregation and outlier detection method. Our experimental results using geometric similarity metrics show that our improved GOI construction method outperforms the baseline methods by constructing the GOIs remarkably more geometrically similar to the real world GOIs. The geometric similarity metrics are only applicable when we have access to the geometries of the real world GOIs (ground truth). To be able to analyse the performance of the GOI extraction methods in envi-

show abstract

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Cited by 14 publications

References 34 publications

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories

Enhancing the quality of geometries of interest (GOIs) extracted from GPS trajectory data using spatio-temporal data aggregation and outlier detection

Contact Info

Product

Resources

About