scholarly journals PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Helen Cristina de Mattos Senefonte ◽  
Myriam Regattieri Delgado ◽  
Ricardo Luders ◽  
Thiago H. Silva
Keyword(s):  
Social Media ◽  
Mobility Patterns

scholarly journals Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility

2019 ◽  
Vol 8 (6) ◽  
pp. 271 ◽  
Author(s):  
Yuanxuan Yang ◽  
Alison Heppenstall ◽  
Andy Turner ◽  
Alexis Comber
Keyword(s):  
Social Media ◽  
Land Use ◽  
Smart Card ◽  
Temporal Variations ◽  
Time Profile ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Metro Station ◽  
Trip Purpose ◽  
Rate Measure

This study describes the integration and analysis of travel smart card data (SCD) with points of interest (POIs) from social media for a case study in Shenzhen, China. SCD ticket price with tap-in and tap-out times was used to identify different groups of travellers. The study examines the temporal variations in mobility, identifies different groups of users and characterises their trip purpose and identifies sub-groups of users with different travel patterns. Different groups were identified based on their travel times and trip costs. The trip purpose associated with different groups was evaluated by constructing zones around metro station locations and identifying the POIs in each zone. Each POI was allocated to one of six land use types, and each zone was allocated a set of land use weights based on the number of POI check-ins for the POIs in that zone. Trip purpose was then inferred from trip time linked to the land use at the origin and destination zones using a novel “land use change rate” measure. A cluster analysis was used to identify sub-groups of users based on individual temporal travel patterns, which were used to generate a novel “boarding time profile”. The results show how different groups of users can be identified and the differences in trip times and trip purpose quantified between and within groups. Limitations of the study are discussed and a number of areas for further work identified, including linking to socioeconomic data and a deeper consideration of the timestamps of POI check-ins to support the inference of dynamic and multiple land uses at one location. The methods and metrics developed by this research use social media POI data to semantically contextualise information derived from the SCD and to overcome the drawbacks and limitations of traditional travel survey data. They are novel and generalizable to other studies. They quantify spatiotemporal mobility patterns for different groups of travellers and infer how their purposes of their journeys change through the day. In so doing, they support a more nuanced and detailed view of who, where, when and why people use city spaces.

scholarly journals Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

2021 ◽  
Vol 10 (5) ◽  
pp. 344
Author(s):  
Yuqin Jiang ◽  
Xiao Huang ◽  
Zhenlong Li
Keyword(s):  
New York ◽  
Social Media ◽  
Land Use ◽  
New York City ◽  
York City ◽  
Local Governments ◽  
Human Mobility ◽  
Federal State ◽  
Mobility Patterns ◽  
Land Use Types

The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.

Analyzing social media data to discover mobility patterns at EXPO 2015: Methodology and results

2016 ◽  
Author(s):  
Eugenio Cesario ◽  
Andrea Raffaele Iannazzo ◽  
Fabrizio Marozzo ◽  
Fabrizio Morello ◽  
Gianni Riotta ◽  
...  
Keyword(s):  
Social Media ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Media Data

scholarly journals Investigating the relation between social media activities and the mobility and activity types of users

2019 ◽  
Author(s):  
Konstantinos Gkiotsalitis
Keyword(s):  
Social Media ◽  
Real World ◽  
Travel Behavior ◽  
Probabilistic Models ◽  
Activity Patterns ◽  
Limited Attention ◽  
Mobility Patterns ◽  
The Real ◽  
Twitter Data ◽  
Activity Types

In the past years, there has been an emerging number of studies on estimating the passenger demand in urban environments based on social media and cellular data. However, the study of the travel behavior at the individual level and the relation between social media activity and the activity/mobility patterns of users has received limited attention. To rectify this, this study examines Twitter data for unveiling the relations between geo-tagged Tweets and Twitter user sentiments, and the respective activity types performed in the real-world. In this work we try to find common patterns between users' Twitter activity and their actual mobility/activity patterns with the aim to provide some generalizations that can help to understand and model the travel behavior of users. This is achieved with the development of educated rules and probabilistic models that can predict the mobility transfers of users between different activities based solely on social media data. The validity of our generalizations is validated with the use of 4-month Twitter data from London. Only active Twitter users have been selected to study in deep the relations between social media activities/sentiments and the activity types performed in the real-world. Although our generalizations are case study-specific, they demonstrate that it is possible to extract the activity and mobility behavior of users with the use of social media and offer a first step in this direction.

scholarly journals The effect of mobility restrictions on the SARS-CoV-2 diffusion during the first wave: what are the impacts in Sweden, USA, France and Colombia

2021 ◽  
Author(s):  
Telle Olivier ◽  
Samuel Benkimoun ◽  
Richard Paul
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Great Majority ◽  
Control Measures ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Usa ◽  
Administrative Units ◽  
Individual Scale ◽  
The Individual

ResuméCombined with sanitation and social distancing measures, control of human mobility has quickly been targeted as a major leverage to contain the spread of SARS-CoV-2 in a great majority of countries worldwide. The extent to which such measures were successful, however, is uncertain (Gibbs et al. 2020; Kraemer et al. 2020). Very few studies are quantifying the relation between mobility, lockdown strategies and the diffusion of the virus in different countries. Using the anonymised data collected by one of the major social media platforms (Facebook) combined with spatial and temporal Covid-19 data, the objective of this research is to understand how mobility patterns and SARS-CoV-2 diffusion during the first wave are connected in four different countries: the west coast of the USA, Colombia, Sweden and France. Our analyses suggest a relatively modest impact of lockdown on the spread of the virus at the national scale. Despite a varying impact of lockdown on mobility reduction in these countries (83% in France and Colombia, 55% in USA, 10% in Sweden), no country successfully implemented control measures to stem the spread of the virus. As observed in Hubei (Chinazzi et al. 2020), it is likely that the virus had already spread very widely prior to lockdown; the number of affected administrative units in all countries was already very high at the time of lockdown despite the low testing levels. The second conclusion is that the integration of mobility data considerably improved the epidemiological model (as revealed by the QAIC). If inter-individual contact is a fundamental element in the study of the spread of infectious diseases, it is also the case at the level of administrative units. However, this relational dimension is little understood beyond the individual scale mostly due to the lack of mobility data at this scale. Fortunately, these types of data are getting increasingly provided by social media or mobile operators, and they can be used to help administrations to observe changes in movement patterns and/or to better locate where to implement disease control measures such as vaccination (Pollina & Busvine 2020; Pullano et al. 2020; Romm et al. 2020).

Using social media to characterize urban mobility patterns: State-of-the-art survey and case-study

2017 ◽  
Vol 1 ◽  
pp. 56-69 ◽  
Author(s):  
Matteo Manca ◽  
Ludovico Boratto ◽  
Victor Morell Roman ◽  
Oriol Martori i Gallissà ◽  
Andreas Kaltenbrunner
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Urban Mobility ◽  
Mobility Patterns

scholarly journals Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

2018 ◽  
Vol 7 (12) ◽  
pp. 481
Author(s):  
Zhewei Liu ◽  
Xiaolin Zhou ◽  
Wenzhong Shi ◽  
Anshu Zhang
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Human Mobility ◽  
Ground Truth ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Geographical Patterns ◽  
Outlier Test ◽  
Social Events ◽  
Media Data

Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.

Sign in / Sign up

Export Citation Format

Share Document