A Comparative Study of Spatial and Temporal Preferences for Waterfronts in Wuhan based on Gender Differences in Check-In Behavior

Wu, Jing; Li, Jingwen; Ma, Yue

doi:10.3390/ijgi8090413

Cited by 18 publications

(18 citation statements)

References 50 publications

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“…Wu et al [44] investigated spatiotemporal analysis based on the time of day and the variation in check-in patterns among weekdays and weekends. Wu et al [45] also investigated check-in studies at 21 of Wuhan's most renowned lakes.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

Hou

Liu

Uddin

et al. 2021

Mobile Information Systems

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Mobile applications are really important nowadays due to providing the accurate check-in data for research. The primary goal of the study is to look into the impact of several forms of entertainment activities on the density dispersal of occupants in Shanghai, China, as well as prototypical check-in data from a location-based social network using a combination of temporal, spatial, and visualization techniques and categories of visitors’ check-ins. This article explores Weibo for big data assessment and its reliability in a variety of categories rather than physically obtained information by examining the link between time, frequency, place, class, and place of check-in based on geographic attributes and related implications. The data for this study came from Weibo, a popular Chinese microblog. It was preprocessed to extract the most important and associated results elements, then converted to geographical information systems format, appraised, and finally displayed using graphs, tables, and heat maps. For data significance, a linear regression model was used, and, for spatial analysis, kernel density estimation was utilized. As per results of hours-to-day usage patterns, enjoyment activities and frequency distribution are produced. Our findings are based on the check-in behaviour of users at amusement locations, the density of check-ins, rush periods for visiting amusement locations, and gender differences. Our data provide light on different elements of human behaviour patterns, the importance of entertainment venues, and their impact in Shanghai. So it can be used in pattern recognition, endorsement structures, and additional multimedia content for these collections.

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Section: Related Workmentioning

confidence: 99%

Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

Hou

Liu

Uddin

et al. 2021

Mobile Information Systems

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“…Wu et al [59] explored the spatio-temporal analysis based on hours of the day and difference between weekdays/weekend check-in patterns. Wu et al [60] also covered check-in analysis around 21 famous lakes in Wuhan. Muhammad et al [61] carried out check-in analysis on weekend and weekdays with gender for Guangzhou city.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

Khan

Wan

et al. 2020

IJGI

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The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.

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“…Although, a few studies have attempted to combine social data with urban spatial potential to study the relationship between waterfronts and human settlements [33]. Scholars have also noticed gender differences in check-in behavior [34,35]. Thus, this study aims to take full advantage of social media check-in data with the combination of POIs to explore the variation characteristics of several time series of waterfront vibrancy and people's preferences for urban waterfronts.…”

Section: Literature Reviewmentioning

confidence: 99%

Temporal Characteristics of Waterfronts in Wuhan City and People’s Behavioral Preferences Based on Social Media Data

Chen

2019

Sustainability

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The appeal and vibrancy of urban waterfronts are catalysts for urban progress and sustainable urban development. This study aims to thoroughly explore the temporal characteristics of waterfront vibrancy and explore people’s behavioral preferences for various types of waterfronts at various times. On the basis of social media data, this study uses the seasonal index analysis method to classify waterfronts. Then, the kernel density estimation was used to analyze the spatial structure of different types of waterfronts. Finally, temporally weighted regression was used to indicate people’s preferences for various types of waterfronts. In general, results show the different temporal characteristics of users in waterfronts at different times and their behavioral preferences for waterfronts as the reasons behind these preface characteristics. First, on weekdays, people tend to visit daily waterfronts close to residences, and people find it convenient to walk after 18:00 and engage in recreational activities dominated by consumption and exercise, which reach a peak at 22:00–24:00. Second, on weekends, people prefer the weekend waterfronts with complete entertainment facilities and cultural themes. The natural seasonal waterfronts with seasonal landscapes attract people in various seasons, such as spring and autumn, whereas the social seasonal waterfront may be more attractive during high seasons, especially in March and June, due to big water events or nearby colleges and universities. Therefore, the government should improve the facilities of various types of waterfronts to satisfy people’s preferences at different times and help in proposing targeted suggestions with reference to future city waterfront planning and space design, contributing to the waterfronts’ vitality improvement, urban features, and promotion of urban sustainable development.

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A Comparative Study of Spatial and Temporal Preferences for Waterfronts in Wuhan based on Gender Differences in Check-In Behavior

Cited by 18 publications

References 50 publications

Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

Temporal Characteristics of Waterfronts in Wuhan City and People’s Behavioral Preferences Based on Social Media Data

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