Explore Hot Spots of City Based on DBSCAN Algorithm

Yu, Xiaoqing; Ding, Yupu; Wan, Wanggen; Thuillier, Etienne

doi:10.1109/icalip.2014.7009862

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Noulas, et al [62] Explored user participation and provided insight of the city by analyzing social media data from foursquare in Seoul city and specially observed venues. Yu, et al [63] Applied DBSCAN algorithm to observe Weibo locations in Shanghai and compared with k-means.…”

Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

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Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as "Weibo") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

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Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

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show abstract

“…Schlitt et al (see [17]) apply the DBSCAN algorithm to electron diffraction data; the spatial clusters formed are then used to identify unitcells, which are the simplest repeating units of a crystalline solid. Yu et al (see [22]) use the DBSCAN algorithm to spatially identify spots of human activity in cities (with the hopes of aiding urban planners).…”

Section: Introductionmentioning

confidence: 99%

A Novel Metric for Detecting Anomalous Ship Behavior Using a Variation of the DBSCAN Clustering Algorithm

Botts

2021

SN COMPUT. SCI.

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There is a growing need to quickly and accurately identify anomalous behavior in ships. This paper applies a variation of the Density Based Spatial Clustering Among Noise (DBSCAN) algorithm to identify such anomalous behavior given a ship's Automatic Identification System (AIS) data. This variation of the DBSCAN algorithm has been previously introduced in the literature, and in this study, we elucidate and explore the mathematical details of this algorithm, we introduce a novel anomaly metric which is more statistically informative than the one previously suggested, and we study the asymptotic properties of this metric.

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An efficient clustering algorithm for trajectory points in Digital Movies Mobile Playing Systems

Zhang

Sun

et al. 2016

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

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Explore Hot Spots of City Based on DBSCAN Algorithm

Cited by 4 publications

References 7 publications

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

A Novel Metric for Detecting Anomalous Ship Behavior Using a Variation of the DBSCAN Clustering Algorithm

An efficient clustering algorithm for trajectory points in Digital Movies Mobile Playing Systems

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