Flow data processing paradigm and its application in smart city using a cluster analysis approach

Zou, Xiang; Cao, Jinghua; Sun, Wei; Guo, Qizhong; Wen, Tao

doi:10.1007/s10586-018-2839-y

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Lytras, Visvizi, and Sarirete investigated the clustering of smart city services [ 17 ]. Xiang et al [ 18 ] studied data stream processing applications in a smart city using a cluster analysis approach. Indonesian scientists have also conducted similar research.…”

Section: Literature Reviewmentioning

confidence: 99%

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Urdabayev,

Kireyeva,

Vasa

et al. 2024

PLoS ONE

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The potential for developing smart cities in Kazakhstan is evaluated using cluster analysis. Built on previous research focused on clustering the regions of Kazakhstan, this study applies the same method to the cities of the country. The analysis uses indicators related to human capital, infrastructure, education, information technology, production, and other factors to assess the potential of each city. The clustering is performed using Single Linkage, Complete Linkage, and Ward’s methods. The results show that Almaty and Astana are the cities with the highest potential for becoming smart cities. Aktobe is identified as a city with distinctive features that may help or hinder its development as a smart city. The remaining cities are clustered into two groups, with one group having the potential to catch up and maintain the trend of developing smart cities, while the other group is less suitable for starting smart city projects and may require more investment per capita. The study highlights the deep regional inequality affecting the potential to successfully develop and manage smart cities in Kazakhstan. The analysis also reveals some limitations and challenges in the data and variables used, including the lack of data for some variables and the difficulties in "translating" some factors and indicators into quantitative variables for clustering. The study concludes that future research should address these challenges and consider clustering inside certain regions to focus on their unique features. The study recommends launching pilot projects in small cities, with the most successful practices then scaled and implemented in the core smart cities and possibly Aktobe, if it manages to use its advantages to compensate for risks. Overall, this study provides insights into the potential of smart city development in Kazakhstan and can inform policymakers in their efforts to support smart city projects in the country.

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Section: Literature Reviewmentioning

confidence: 99%

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Urdabayev,

Kireyeva,

Vasa

et al. 2024

PLoS ONE

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“…Digitization in general provides a wide range of available data, which makes it possible to understand and link user behavior. Processing data from multiple communication systems and interconnected subsystems helps in the development of smart cities [6]. In the context of the design of a smart city, the interaction between mobile applications and transport itself is especially important.…”

Section: Introductionmentioning

confidence: 99%

The Classification of Application Users Supporting and Facilitating Travel Mobility Using Two-Step Cluster Analysis

et al. 2023

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There is a significant and supported trend toward the achievement of ensuring continuous door-to-door travel in the pan-European transport network. Many innovative programs are dedicated to this topic through assigned projects. This paper is based on the concrete partial results of the H2020 project Shift2Rail IP4 to support the deployment of mobility as a service (IP4MaaS). Attitudes towards travel for demonstration sites were assessed based on the outputs of a sample of respondents from two countries. Cooperation in working on the IP4MaaS project was also provided by a partner from Slovakia (UNIZA) and the Czech Republic (OLTIS). Mathematical statistical tools were used to evaluate the available data to find a connection with promoting mobility as a service. This paper aims to identify differences in travelers’ needs with a focus on using applications using two-step cluster analysis. The research resulted in the identification of differences in traffic behavior within MaaS activities when comparing different clusters reflecting preferences for using a website or mobile application.

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