Research and application of the global positioning system (GPS) clustering algorithm based on multilevel functions
Pages 357 - 368
Abstract
With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS trajectory data in lifelogs is typically categorized into continuous and discrete trajectories. Finding a point of interest (POI) from discrete trajectories is a challenging task to do and has caught little attention so far. This paper suggests an LP-DBSCAN model for mining personal trajectories from discrete GPS trajectory data. It makes use of the hierarchical structure information implied in GPS trajectory data and it is suggested a variable-levels, variable-parameters clustering method (LP-DBSCAN) based on the DBSCAN algorithm to increase the precision of finding POI information. Finally, the Liu lifelog dataset is subjected to a systematic evaluation. In terms of GPS data that are not evenly distributed geographically, the experimental results demonstrated that the proposed algorithm could more accurately identify POI information and address the adverse effects caused by the global parameters of the traditional DBSCAN algorithm.
References
[1]
Yen AZ, Fu MH, Ang WH, Chu TT, Tsai SH, Huang HH, Chen HH. Visual lifelog retrieval: humans and machines interpretation on first-person images. Multimed Tools Appl. 2023.
[2]
Shen YC, Guo B, Shen Y, Duan XL, Dong XQ, Zhang H, et al. Personal big data pricing method based on differential privacy. Comput Secur. 2022; 113: 102529.
[3]
Ribeiro R, Trifan A, Neves AJR. Lifelog retrieval from daily digital data: Narrative review. JMIR mHealth uHealth. 2022; 10(5): e30517.
[4]
Nestik TA, Zhuravlev AL. Big data analysis in psychology and social sciences: perspective directions of research. Psikhol Zh. 2019; 40(6): 5-17.
[5]
Jalal A, Batool M, Kim K. Sustainable wearable system: Human behavior modeling for life-logging activities using K-ary tree hashing classifier. Sustainability. 2020; 12(24): 10324.
[6]
Gupta R, Crane M, Gurrin C. Considerations on privacy in the era of digitally logged lives. Online Inform Rev. 2021; 45(2): 278-296.
[7]
Alam N, Graham Y. Memento: a prototype search engine for LSC 2021. Multimed Tools Appl. 2023.
[8]
Lee A, Ryu H. Comparison of the change in interpretative stances of lifelog photos versus manually captured photos over time. Online Inform Rev. 2020; 44(2): 521-541.
[9]
Sugawara J, Ochi D, Yamashita R, Yamauchi T, Saigusa D, Wagata M, et al. Maternity Log study: A longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy. BMJ Open. 2019; 9(2): e025939.
[10]
Ksibi A, Alluhaidan ASD, Salhi A, El-Rahman SA. Overview of lifelogging: Current challenges and advances. IEEE Access. 2021; 9: 62630-62641.
[11]
Liu G, Rehman MU, Wu Y. Personal trajectory analysis based on informative lifelogging. Multimed Tools Appl. 2021; 80(14): 22177-22191.
[12]
Lee KH, Urtnasan E, Hwang S, Lee HY, Lee JH, Koh SB, Youk H. Concept and proof of the lifelog bigdata platform for digital healthcare and precision medicine on the cloud. Yonsei Med J. 2022; 63: S84-S92.
[13]
Xu QL, Del Molino AG, Lin J, Fang F, Subbaraju V, Li LY, Lim JH. Lifelog image retrieval based on semantic relevance mapping. ACM T Multim Comput. 2021; 17(3): 92.
[14]
Seo J, Choi A, Sung M. Recommendation of indoor luminous environment for occupants using big data analysis based on machine learning. Build Environ. 2021; 198: 107835.
[15]
Bum J, Choo H, Whang JJ. Image-Based Lifelogging: User Emotion Perspective. CMC-Comput Mater Con. 2021; 67(2): 1963-1977.
[16]
Kim JS. Feature-first add-on for trajectory simplification in lifelog applications. Sensors. 2020; 20(7): 1852.
[17]
Yu L, Gui Z. Analysis of enterprise social media intelligence acquisition based on data crawler technology. Entrep Res J. 2021; 11(2): 3-23.
[18]
Nguyen MD, Shin WY. An improved density-based approach to spatio-textual clustering on social media. IEEE Access. 2019; 7: 27217-27230.
[19]
Tran C, Vu DD, Shin WY. An improved approach for estimating social POI boundaries with textual attributes on social media. Knowl-Based Syst. 2021; 213: 106710.
[20]
Jiang SX, Guan W, He ZB, Yang L. Measuring taxi accessibility using grid-based method with trajectory data. Sustainability. 2018; 10(9): 3187.
[21]
Dong J, Chen B, He LN, Ai C, Zhang F, Guo DH, Qiu XG. A spatio-temporal flow model of urban dockless shared bikes based on points of interest clustering. ISPRS Int J Geo-Inf. 2019; 8(8): 345.
[22]
Chen C, Liao CW, Xie XF, Wang YS, Zhao JF. Trip2Vec: A deep embedding approach for clustering and profiling taxi trip purposes. Pers Ubiquit Comput. 2019; 23(1): 53-66.
[23]
Xing Y, Wang K, Lu JJ. Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China. J Transp Geogr. 2020; 87: 102787.
[24]
Xu ZZ, Cui G, Zhong M, Wang X. Anomalous urban mobility pattern detection based on GPS trajectories and POI data. ISPRS Int J Geo-Inf. 2019; 8(7): 308.
[25]
Chen HX, Tao F, Ma PL, Gao LN, Zhou T. Applicability evaluation of several spatial clustering methods in spatiotemporal data mining of floating car trajectory. ISPRS Int J Geo-Inf. 2021; 10(3): 161.
[26]
Cheng D, Yue G, Pei T, Wu MB. Clustering indoor positioning data using E-DBSCAN. ISPRS Int J Geo-Inf. 2021; 10(10): 669.
[27]
van Dijk J, Krygsman S. Analyzing travel behavior by using GPS-Based activity spaces and opportunity indicators. J Urban Technol. 2018; 25(2): 105-124.
[28]
Wang TF, Ren C, Luo Y, Tian J. NS-DBSCAN: A density-based clustering algorithm in network space. ISPRS Int J Geo-Inf. 2019; 8(5): 218.
[29]
Ponce-Lopez R, Ferreira J Jr. Identifying and characterizing popular non-work destinations by clustering cellphone and point-of-interest data. Cities. 2021; 113: 103158.
[30]
Bu JT, Yin J, Yu YF, Zhan Y. Identifying the daily activity spaces of older adults living in a high-density urban area: A study using the smartphone-based global positioning system trajectory in shanghai. Sustainability. 2021; 13(9): 5003.
[31]
Marakkalage SH, Lau BPL, Zhou YR, Liu R, Yuen C, Yow WQ, Chong KH. WiFi fingerprint clustering for urban mobility analysis. IEEE Access. 2021; 9: 69527-69538.
[32]
Yao ZX, Yang F, Guo YD, Jin PJ, Li Y. Trip end identification based on spatial-temporal clustering algorithm using smartphone positioning data. Expert Syst Appl. 2022; 197: 116734.
[33]
Liu G, Rehman MU, Wu Y. Toward storytelling from personal informative lifelogging. Multimed Tools Appl. 2021; 80(13): 19649-19673.
[34]
Yu L, Yu T, Wu YX, Wu GD. Rethinking the identification of urban centers from the perspective of function distribution: A framework based on point-of-interest data. Sustainability. 2020; 12(4): 1543.
[35]
Dong L, Li JZ, Xu YJ, Yang YT, Li XM, Zhang H. Study on the spatial classification of construction land types in Chinese cities: A case study in Zhejiang province. Land. 2021; 10(5): 523.
[36]
Gao Q, Molloy J, Axhausen KW. Trip purpose imputation using GPS trajectories with machine learning. ISPRS Int J Geo-Inf. 2021; 10(11): 775.
Recommendations
A new hybrid method based on partitioning-based DBSCAN and ant clustering
Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based ...
Points of interest recommendation from GPS trajectories
Recently, points of interest POIs recommendation has evolved into a hot research topic with real-world applications. In this paper, we propose a novel semantics-enhanced density-based clustering algorithm SEM-DTBJ-Cluster, to extract semantic POIs from ...
Detecting New Points of Interest Using Taxi GPS Data
IAIT '23: Proceedings of the 13th International Conference on Advances in Information TechnologyNowadays, online mapping services, such as navigation, rely significantly on points of interest (POIs) data to enhance their utility and efficiency. However, maintaining an up-to-date POI dataset is a challenging task. Recognizing this, we observed that ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© 2024 – IOS Press. All rights reserved.
Publisher
IOS Press
Netherlands
Publication History
Published: 14 March 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Nov 2024
Other Metrics
Citations
View Options
View options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in