Understanding Social Influence in Activity Location Choice and Lifestyle Patterns Using Geolocation Data from Social Media

Hasan, Samiul; Ukkusuri, Satish V.; Zhan, Xianyuan

doi:10.3389/fict.2016.00010

Cited by 29 publications

(14 citation statements)

References 25 publications

(27 reference statements)

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“…Various studies based on LBSN datasets to observe human check-in behavior under domains like privacy [73,76,77], gender differences [78], geographic spaces [56], urban emotions [79], activity location choice, lifestyle patterns [6,[80][81][82], and operations and production management [83] have been conducted. Li and Chen [63] studied location sharing by the users in the real world, and presented data analysis results over user profiles, update activities, mobility characteristics, social graphs, and attribute correlations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The major and minor axes of the SDE are calculated according to Equation 5, and their proportional relations denote the degree of flattening the SDE. The rotating azimuth is calculated according to Equation (6), which reflects the main trend directions [160,161]. The standard deviations of the major and minor axes of the SDE are calculated according to Equation (7).…”

Section: Standard Deviational Ellipse (Sde) Analysismentioning

confidence: 99%

“…In recent years, social media has dramatically expanded in popularity around the world and became an integral part of the information ecosystem in both application and research perspectives due to its unprecedented reach to masses (i.e., users, consumers, businesses, governments, and nonprofit organizations) [1]. Historically, traditional data sources (i.e., survey, census) [2,3] are analyzed to observe human activity behavior [4,5], lifestyle patterns [6], and gender differences [7], but these data sources are considered to be more expensive both in the collection and the analysis. This, in turn, requires more processing time and results in data sparsity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Standard Deviational Ellipse (Sde) Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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“…However, past studies mostly focused on the optimization of urban traffic structure due to severe traffic congestions in the city and few intercity trips. Although LBS data has been widely used in urban traffic and has made several achievements [40][41][42][43], few studies exist on dominant trip distances in intercity transportation [44,45]. It's worthy to mention that existing studies mainly focus on dedicated transport corridor and describe how to get the travel learn from the previous research, trip information including trip distance and trip trajectory can be deduced based on LBS data.…”

Section: Of 17mentioning

confidence: 99%

Investigating Dominant Trip Distance for Intercity Passenger Transport Mode Using Large-Scale Location-Based Service Data

Xiang

et al. 2019

Sustainability

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Intercity transport systems have been plagued by low efficiency and overutilization for a long time, due to unhealthy competition among multi-transport modes. Hence, this study aims to estimate the dominant trip distance of intercity passenger transport modes to optimize the allocation of intercity passenger transport resources and improve the efficiency of intercity transport systems. Dominant trip distance was classified into two types: Absolute dominant trip distance and relative dominant trip distance; and their respective models were developed using passenger transport mode share functions and fitting curves. Particularly, the big data of intercity passenger transport mode share rate of more than 360 cities in China was obtained using a network crawler and each passenger transport mode share function and their curves were proposed. Furthermore, the dominant trip distances estimation models of intercity passenger transport were developed and solved. The results show that there are significant differences in dominant trip distance between the transport modes. For example, the absolute and relative dominant trip distances of highway are 8–119 km and 8–463 km, respectively, while those of airway are 1594–3000 km and 2477–3000 km, respectively.

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“…Originally, most studies have explored characteristic of individuals' spatial-temporal travel behaviour and its interaction with urban space (Ahas et al 2010;Licoppe et al 2008). In recent years, big data and social media data are employed by transport modellers for modelling transport related issues (Hasan and Ukkusuri 2015;Hasan et al 2016;Rashidi et al 2017). At the same time researchers are actively using dedicated apps to monitor and learn about dynamic spatiotemporal behaviours.…”

mentioning

confidence: 99%

ICT, activity space–time and mobility: new insights, new models, new methodologies

Ben-Elia

Zhu

2018

Transportation

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Over three decades have passed since research on the complex interrelationships between Information and Communication Technologies (ICT) and travel behaviour began. Since then much has changed. Broadly speaking researchers have been chasing a complex and bi-directional moving target with both the development of digital technologies (what we refer to as three Internet generations) and changes to human behaviours coinciding side by side. As both transportation technologies and ICT contribute to remote activities, the impact of ICT on individuals' activities and related travel behaviour have been discussed. It is not surprising because untangling this issue is the basis of understanding travel behaviour in information age. In the mid 80 s and early 90 s scholars were trying to disentangle how telecommunications (as they were then called) would likely influence activity and travel patterns and an initial set of hypotheses were established by Salomon and Mokhtarian (Mokhtarian 1990;Salomon 1986). According to their framework telecommunications could substitute, complement, modify travel (route, mode, timing or destination) or have no effect at all. This simple and direct framework fitted well to the rather simple information technology of the time. The 1st Internet generation can traced to the opening of the World Wide Web (www) initially as a unidirectional protocol for passing on information content from producers to the users on sedentary end devices (desktops). Research focused mainly on the substitution/complementary relationships between virtual and physical activities especially e-working (teleworking/telecommuting-Mokhtarian and Salomon 1994) and e-shopping and how they affect the demand for travel (Cao 2012;Zhai et al. 2017). Another strand of research involved the influence of information on modification of travel choices based on the abundance of travel planners available on the web

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Understanding Social Influence in Activity Location Choice and Lifestyle Patterns Using Geolocation Data from Social Media

Cited by 29 publications

References 25 publications

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

Investigating Dominant Trip Distance for Intercity Passenger Transport Mode Using Large-Scale Location-Based Service Data

ICT, activity space–time and mobility: new insights, new models, new methodologies

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