Mining multiple spatial–temporal paths from social media data

Yao, Hong; Xiong, Muzhou; Zeng, Deze; Gong, Jianhua

doi:10.1016/j.future.2017.08.003

Cited by 13 publications

(3 citation statements)

References 20 publications

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“…The goal was to provide spatial and seasonal coverage to be used further for the investigation of coastal ecosystems. An example of the value of mobility data is discussed by Yao et al (2018) [11], where an individual mobility pattern framework that can generate multiple spatial-temporal paths from social media data was proposed. The work reflected the diverse patterns existing in individual trajectories.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Open-Social Data Behavior Concerning Gasoline Stealing: A Case Study of the Mexican Petroleum Crisis

Zagal-Flores,

Mata-Rivera,

Torres-Ruiz

et al. 2023

Preprint

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At the beginning of 2019, the petroleum crisis impacted many economies dependent on this industry. The Mexican government started programs to identify points and government officials involved in the gasoline stealing from PEMEX (Petróleos Mexicanos), the country’s leading government petroleum company. The programs consisted of supervising and monitoring the Mexican country network of gasoline ducts to detect points where gasoline was being stolen. Consequently, large urban regions faced a lack and shortage of gasoline. This situation generated several reactions in social media and many open data in news media. Although the government provided open data about stealing gasoline locations related to crimes, it did not analyze the collected data to identify patterns, insights, and the spatio-temporal characterization of this phenomenon. This paper presents a study to deal with the regional semantics described in the social media locations of gasoline stealing. Thus, a framework to discover the trends that emerge from social media and how it is correlated with the government’s open data is also presented—the proposed methodology used machine learning techniques based on linguistic and semantic technologies. The analysis was applied to a dataset of 24,317 geo-referenced tweets. The obtained results reflected the Mexican thinking opinion regarding discovered social topics, polarization maps, and regional insights. According to discovered trends, there were long fuel lines between 1.5 and 5 kilometers (on average) at fuel stations in different Mexican states.

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Section: Related Workmentioning

confidence: 99%

Analysis of Open-Social Data Behavior Concerning Gasoline Stealing: A Case Study of the Mexican Petroleum Crisis

Zagal-Flores,

Mata-Rivera,

Torres-Ruiz

et al. 2023

Preprint

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show abstract

“…To understand the spatio-temporal patterns of human mobility is one of the basic targets and fundamental to follow-up analysis. Spatial-temporal paths consistent for each individual are mined from mobile data, whilst relationships among the generated hotspots in temporal dimensions are discovered [10]. Large-scale collective urban mobility was analysed to elaborate people flows between areas, as well as the exceptional patterns that are associated with real-world events such as soccer matches [11].…”

Section: Discovering the Phenomenonmentioning

confidence: 99%

A Review of Human Mobility Research Based on Big Data and Its Implication for Smart City Development

Wang

Zhang

Chan

et al. 2020

IJGI

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Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.

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“…Chaniotakis et al (2016) have provided a comprehensive review of the directions that transportation-related Social Media research is positioned. In short, the directions that the literature takes are either the use of Social Media for modeling and forecasting purposes, including an aspect of the use of Social Media data for OD Estimation (Liao et al, 2021;Osorio-Arjona and García-Palomares, 2019), Attraction Models (Lee et al, 2019;Yang et al, 2018;Hu and Jin, 2018), activity modelling (Cui et al, 2018;Chaniotakis et al, 2017;Hasan and Ukkusuri, 2018;Lee et al, 2016), extraction of mobility-related and spatial characteristics (Ebrahimpour et al, 2020;Hu et al, 2020;Kim et al, 2018;Yao et al, 2018;Yang et al, 2019) transportation-related sentiment analysis (Rahman et al, 2021;Bakalos et al, 2020;Sari et al, 2019;Ali et al, 2018Ali et al, , 2019, prediction and event detection (Chaturvedi et al, 2021;Yao and Qian, 2021;Alomari et al, 2019Alomari et al, , 2021Zulfikar et al, 2019;Zhang et al, 2018;Xu et al, 2018;Pereira et al, 2015), and accessibility analysis with the complementary use of Twitter data (Kim and Lee, 2021;Qian et al, 2020;Moyano et al, 2018). On another perspective, social media have also been used mainly from transport providers, for the direct communication that their platform allow with the end users (National Academies of Sciences, Engineering, and Medicine, 2021).…”

Section: Introductionmentioning

confidence: 99%