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

Haidery, SA; Ullah, H; Khan, NU; Fatima, K; Rizvi, SS; Kwon, SJ

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

Haidery, SA Ullah, H Khan, NU Fatima, K Rizvi, SS Kwon, SJ

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ELECTRONICS, 2020, 9, (5)
Issue Date:: 2020-05-01

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Field	Value	Language
dc.contributor.author	Haidery, SA
dc.contributor.author	Ullah, H
dc.contributor.author	Khan, NU
dc.contributor.author	Fatima, K
dc.contributor.author	Rizvi, SS
dc.contributor.author	Kwon, SJ
dc.date.accessioned	2021-03-17T06:06:12Z
dc.date.available	2021-03-17T06:06:12Z
dc.date.issued	2020-05-01
dc.identifier.citation	ELECTRONICS, 2020, 9, (5)
dc.identifier.issn	2079-9292
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/147294
dc.description.abstract	<jats:p>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.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ELECTRONICS
dc.relation.isbasedon	10.3390/electronics9050837
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.title	Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-17T06:06:07Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	5

Abstract:

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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http://hdl.handle.net/10453/147294