Discovering personally meaningful places

Zhou, Changqing; Frankowski, Dan; Ludford, Pamela; Shekhar, Shashi; Terveen, Loren

doi:10.1145/1247715.1247718

Cited by 168 publications

(26 citation statements)

References 28 publications

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“…Then, the DBSCAN clustering algorithm is applied to extract the activity center for each user. The term activity center refers to the mean center point of the geographical area where a user most frequently shows up [26]. We calculated visitation frequency, travel distance, and attractiveness for each retail agglomeration.…”

Section: Methodsmentioning

confidence: 99%

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Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Wang

Jiang

Liu

et al. 2016

IJGI

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Abstract:Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent.

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Section: Methodsmentioning

confidence: 99%

“…Previous research has applied the DBSCAN algorithm to detect density clusters and extract activity centers for individuals from geo-tagged social media data [26,31]. Based on this research, we used DBSCAN to obtain each individual user's frequently visited areas.…”

Section: Extracting Activity Centersmentioning

confidence: 99%

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Wang

Jiang

Liu

et al. 2016

IJGI

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“…In this section, two groups of experiments were designed to test the performance of the VSLC through a comparison to the three other algorithms: K-Medoids, DJ-Cluster [24], and CB-SMoT [25].…”

Section: Refueling Stop Events Extraction Experimental and Evaluationmentioning

confidence: 99%

POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station

Yang

2018

IJGI

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Abstract:Points of interest (POIs) such as stores, gas stations, and parking lots are particularly important for maps. Using gas station as a case study, this paper proposed a novel approach to enhance POI information using low-frequency vehicle trajectory data and social media data. First, the proposed method extracted spatial information of the gas station from sparse vehicle trace data in two steps. The first step proposed the velocity sequence linear clustering algorithm to extract refueling stop tracks from the individual trace line after modeling the vehicle refueling stop behavior using movement features. The second step used the Delaunay triangulation to extract the spatial information of gas stations from the collective refueling stop tracks. Second, attribute information and dimension sentiment semantic information of the gas station were extracted from social media data using the text mining method and tripartite graph model. Third, the gas station information was enhanced by fusing the extracted spatial data and semantic data using a matching method. Experiments were conducted using the 15-day vehicle trajectories of 12,000 taxis and social media data from the Dazhongdianping in Beijing, China, and the results showed that the proposed method could extract the spatial information, attribute information, and review information of gas stations simultaneously. Compared with ground truth data, the automatically enhanced gas station was proved to be of higher quality in terms of the correctness, completeness, and real-time.

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“…In the following, two groups of experiments were designed to compare the TDBC with the other four algorithms, K-Medoids, DJ-Cluster [13], CB-SMoT [19] and Time-Based Clustering [3]. The first experiment used Dataset 1, which has a large amount of data, to compare the performance of the algorithms.…”

Section: Stop Point Extraction Experimentsmentioning

confidence: 99%

A Two-Step Clustering Approach to Extract Locations from Individual GPS Trajectory Data

Zhang

Tian

et al. 2016

IJGI

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High-accuracy location identification is the basis of location awareness and location services. However, because of the influence of GPS signal loss, data drift and repeated access in the individual trajectory data, the efficiency and accuracy of existing algorithms have some deficiencies. Therefore, we propose a two-step clustering approach to extract individuals' locations according to their GPS trajectory data. Firstly, we defined three different types of stop points; secondly, we extracted these points from the trajectory data by using the spatio-temporal clustering algorithm based on time and distance. The experimental results show that the spatio-temporal clustering algorithm outperformed traditional extraction algorithms. It can avoid the problems caused by repeated access and can substantially reduce the effects of GPS signal loss and data drift. Finally, an improved clustering algorithm based on a fast search and identification of density peaks was applied to discover the trajectory locations. Compared to the existing algorithms, our method shows better performance and accuracy.

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Discovering personally meaningful places

Cited by 168 publications

References 28 publications

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station

A Two-Step Clustering Approach to Extract Locations from Individual GPS Trajectory Data

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