Exploring the effectiveness of location‐based social media in modeling user activity space: A case study of Weibo

Yuan, Yihong; Wang, Xujiao

doi:10.1111/tgis.12450

Cited by 16 publications

(19 citation statements)

References 62 publications

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“…We have embraced the dimension of human activity, which is estimated using mobile and social network data [ 26 , 27 , 28 ]. In particular, the measurement of human activity is conducted by calculating the inflow and outflow numbers and density of Weibo users [ 29 , 30 ]. Human activity is measured by the inflow, outflow and total flow (sum of inflow and outflow) numbers and the number of social network platform check-ins.…”

Section: Methodsmentioning

confidence: 99%

Effects of Urban Vibrancy on an Urban Eco-Environment: Case Study on Wuhan City

Zeng

Chang

et al. 2022

IJERPH

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In the context of rapid urbanisation and an emerging need for a healthy urban environment, revitalising urban spaces and its effects on the urban eco-environment in Chinese cities have attracted widespread attention. This study assessed urban vibrancy from the dimensions of density, accessibility, liveability, diversity, and human activity, with various indicators using an adjusted spatial TOPSIS (technique for order preference by similarity to an ideal solution) method. The study also explored the effects of urban vibrancy on the urban eco-environment by interpreting PM 2.5 and land surface temperature using “big” and “dynamic” data, such as those from mobile and social network data. Thereafter, spatial modelling was performed to investigate the influence of urban vibrancy on air pollution and temperature with inverted and extracted remote sensing data. This process identified spatial heterogeneity and spatial autocorrelation. The majority of the dimensions, such as density, accessibility, liveability, and diversity, are negatively correlated with PM 2.5, thereby indicating that the advancement of urban vibrancy in these dimensions potentially improves air quality. Conversely, improved accessibility increases the surface temperature in most of the districts, and large-scale infrastructure construction generally contributes to the increase. Diversity and human activity appear to have a cooling effect. In the future, applying spatial heterogeneity is advised to assess urban vibrancy and its effect on the urban eco-environment, to provide valuable references for spatial urban planning, improve public health and human wellbeing, and ensure sustainable urban development.

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Section: Methodsmentioning

confidence: 99%

Effects of Urban Vibrancy on an Urban Eco-Environment: Case Study on Wuhan City

Zeng

Chang

et al. 2022

IJERPH

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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%

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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“…This study also suggests that geotagged Twitter data inform us more about the digital status of places and less about users’ day‐to‐day travel patterns. Yuan and Wang (), on the other hand, tackle how data collection duration and sample size affect the reliability of using location‐based social media (LBSM) data to infer human activity patterns based on the measures of radius of gyration (ROG) and entropy. This study finds that both ROG and entropy increase with increasing amounts of LBSM data, but the proportion of increase drops and approaches zero eventually.…”

Section: Overview Of Articles In This Special Issuementioning

confidence: 99%