Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data

Ullah, Hidayat; Wan, Wanggen; Haidery, Saqib Ali; Khan, Naimat Ullah; Ebrahimpour, Zeinab; Luo, Tianhang

doi:10.3390/ijgi8110506

Cited by 37 publications

(34 citation statements)

References 54 publications

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“…Shi et al [28] also used Weibo data for examining tourism crowds in Shanghai by analyzing the check-ins in order to identify the popularity of tourism venues and the associations between these venues, with the help of sentiment analysis from user opinions. Ullah et al [31] used Weibo data to analyze the spatiotemporal patterns in green spaces for urban studies. The check-in behavior, along with gender differences, based on data from Weibo from early 2016 was presented by Rizwan et al [32,33].…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Khan

Wan

2020

IJGI

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The aim of this study is to analyze and compare the patterns of behavior of tourists and residents from Location-Based Social Network (LBSN) data in Shanghai, China using various spatiotemporal analysis techniques at different venue categories. The paper presents the applications of location-based social network’s data by exploring the patterns in check-ins over a period of six months. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data is translated into the Geographical Information Systems (GIS) format, and compared with the help of temporal statistical analysis and kernel density estimation. The venue classification is done by using information regarding the nature of physical locations. The findings reveal that the spatial activities of tourists are more concentrated as compared to those of residents, particularly in downtown, while the residents also visited suburban areas and the temporal activities of tourists varied significantly while the residents’ activities showed relatively stable behavior. These results can be applied in destination management, urban planning, and smart city development.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Khan

Wan

2020

IJGI

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“…They found out that human mobility is influenced by economic and geographical constraints and sentiment analysis of text messages enriched understanding of their research. Ullah et al [27] also analyzed spatio-temporal data from LBSNs to show the impact of people in green spaces. Yan et al [28] investigated three different datasets, including Sina Weibo data, to analyse individual's decision-making regarding the places they tend to go and the influence of the economic aspects of crowds on hot spot destinations.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

Ebrahimpour

Wan

García

et al. 2020

IJGI

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Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

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“…Shanghai, China (lying between 30 • 40 -31 • 53 N and 120 • 52-122 • 12 E [31,32]) is located on the eastern edge of the Yangtze River Delta [33]. The total area of Shanghai is 8359 km 2 , and the gross domestic product (GDP) was 480 billion dollars (USD) in 2018 [34,35].…”

Section: Study Area and Datasetmentioning

confidence: 99%

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

et al. 2020

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In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.

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Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data

Cited by 37 publications

References 54 publications

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

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