Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data

Xin, Yanan; MacEachren, Alan M.

doi:10.1080/13658816.2020.1770259

Cited by 16 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social events of interests, which will be referred to simply as events in the rest of the paper, can be observed as the representation of the real-world happenings at a given location and time. These happenings can be classified based on the thematic (e.g., festival or sport events), temporal , spatial , and other learning features (e.g., user profiles and social links) [ 2 , 27 , 29 ]. Discovering and disseminating events from diverse online social networks and with a variety of modes (e.g., text, image) have been the focus in many research studies, such as politics [ 1 ], traffic analysis [ 5 ], and fashion analysis [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

2022

View full text Add to dashboard Cite

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the hybrid extraction and clustering of social events from big social data streams. We rely on a hybrid learning model, where supervised deep learning is used for feature extraction and topic classification, whereas unsupervised spatial clustering is employed to determine the event whereabouts. We present ‘Deep-Eware’, a scalable and efficient event-aware big data platform that integrates data stream and geospatial processing tools for the hybrid extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. The system integrates a semantic keyword generation tool using KeyBERT for dataset preparation. Event classification is performed using CNN and bidirectional LSTM, while hierarchical density-based spatial clustering was used for location-inference of events. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that this hybrid approach for spatio-temporal event extraction has a major advantage for real-time spatio-temporal event detection and tracking from social media. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

show abstract

Section: Related Workmentioning

confidence: 99%

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

2022

View full text Add to dashboard Cite

show abstract

“…For example, Yuan and Raubal (2016) explore activity patterns to understand the sociospatial relationships and correlation between demographic factors and the usage of urban space for different communities. Xin and MacEachren (2020) recently proposed a workflow to understand event-related travel behaviour. Many of these applications rely on a common step in their analysis: the identification of meaningful locations in mobility data.…”

Section: Existing Approaches To Detecting Meaningful Locationsmentioning

confidence: 99%

“…Ahas et al 2010, Cheng et al 2010, McGee et al 2013) as well as a means-to-an-end in empirical analyses of human mobility (e.g. Huang and Wong 2016, Siła-Nowicka et al 2016, Xin and MacEachren 2020.…”

Section: Introductionmentioning

confidence: 99%

Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility

Chen

Poorthuis

2021

International Journal of Geographical Information Science

View full text Add to dashboard Cite

Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, whichcompared to conventional datasetscan be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R software package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic 'recipes' more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency

show abstract

“…Additionally, Twitter provides access to geo-tagged tweets via appropriate Restful service APIs (Goswami, & Kumar, 2019;Mazoyer et al, 2020). Apart from many static attributes that can be gathered from tweets, by using appropriate filtration mechanisms we can focus on specific locations and areas of interest and demonstrate user activities capturing their moving patterns (Xin, & MacEachren, 2020).…”

Section: State Of the Artmentioning

confidence: 99%

Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification

Kokkinos

Nathanail

2020

Transport and Telecommunication Journal

View full text Add to dashboard Cite

Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. We provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.

show abstract

Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data

Cited by 16 publications

References 35 publications

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility

Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification

Contact Info

Product

Resources

About