Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

Ullah, Hidayat; Wan, Wanggen; Haidery, Saqib Ali; Khan, Naimat Ullah; Ebrahimpour, Zeinab; Muzahid, Abdullah

doi:10.1109/access.2020.2973177

Cited by 38 publications

(18 citation statements)

References 71 publications

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“…Kernel density estimation (KDE) has been frequently used to quantify the spatial distribution of park visitors across a study area [87,104]. KDE is a statistical approach used to estimate a smooth and continuous distribution from a limited set of observed points [105].…”

Section: Methods Used In Spatial Data Analysismentioning

confidence: 99%

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Cui

Malleson

Houlden

et al. 2021

IJGI

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Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis.

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Section: Methods Used In Spatial Data Analysismentioning

confidence: 99%

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Cui

Malleson

Houlden

et al. 2021

IJGI

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“…c) According to the candidate visitors' trajectories and excluding the park staff, we designated candidate visitors who stayed in the park for 0.5-5 h as park visitors. To ensure that the data were representative, we removed parks that attracted fewer than 100 visitors on each study date (Ullah et al, 2020), and visitor volume data were obtained for 86 parks. We further determined each park visitor's gender and age.…”

Section: Data Sourcesmentioning

confidence: 99%

“…In recent years, the use of big data, such as social media and mobile phone data, has gradually gained traction as a new method of studying park visitation patterns in view of the limitations of traditional methods and the rapid development of computer science-and internet-related techniques (Chen et al, 2018;Li et al, 2018;Lin et al, 2021;Song et al, 2020a). Many studies using big data focus on park accessibility (Guo et al, 2019a;Hamstead et al, 2018), the spatial and temporal distribution of urban park users (Chen et al, 2018;Liang and Zhang, 2021;Ullah et al, 2020), and the factors in uencing park usage (Fan et al, 2021;Lyu and Zhang, 2019), with these studies aiming to provide references for the planning and construction of urban parks. Although some previous studies based on questionnaires and interviews have revealed that park visit patterns are in uenced by the visitors' age and gender, unfortunately, such visitor data cannot be easily obtained (Song et al, 2020b;Ullah et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Using Mobile Phone Data to Understand the Demographic Characteristics and Behavioral Patterns of Park Visitors in a Megacity, Beijing, China

Zhao

Ma³

et al. 2021

Preprint

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BackgroundUrban parks are important places that allow urban residents to experience nature but are also associated with the risk of exposure to contaminated soil. Local researches on demographic characteristics and population behavior patterns are the basis of soil exposure assessment. The objectives were to determine park visitors’ demographic characteristics and behavioral patterns. MethodsA total of 86 urban parks in Beijing were selected, and mobile phone data were used to quantify the park visits, identify the demographic characteristics of the visitors, and determine the visitors’ stay times. A Kruskal-Wallis test and kernel density estimation were used for difference analysis and to describe the spatial aggregation of visitors, respectively. A random forest model was used to analyze the factors influencing the visitors’ stay times.ResultsVisitor numbers at comprehensive parks and theme parks decreased significantly in winter, whereas seasonal variations in visitor numbers at community parks and country parks were small. Almost half of the visitors to Beijing’s urban parks were foreign visitors, who, unlike local visitors, preferred to visit parks located in the city’s center. Parks were mostly used by males and visitors aged 31–45. Most visitors stayed in the park for 1–2 h, and the distance from a given visitor’s home to the park was the most important factor affecting stay time, contributing 80.65% of the difference in stay time.ConclusionsThis study provides a quantitative analysis of the patterns relating to urban park use by different groups of people. Changes in behavior patterns of different groups of people should be considered when assessing soil exposure.

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“…A lot of work has been carried out by using KDE. The previous studies in density estimation from Weibo data mostly used a single technique such as KDE to estimate density on the maps and applied this for a specific task such as gender analysis in green parks and also focused upon the green spaces [22,23], tourism [24,25], and point of interest recommendations [26], etc. Researchers used Weibo to explore the spatial characteristics of check-in data using a single technique; i.e., the spatial analysis of check-in data was carried out using point density in Wuhan [27], and in [28], the authors used KDE to observe gender-based check-in behavior using Weibo data.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

Self Cite

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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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Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

Cited by 38 publications

References 71 publications

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Using Mobile Phone Data to Understand the Demographic Characteristics and Behavioral Patterns of Park Visitors in a Megacity, Beijing, China

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

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