Title: Design and implementation of big data analysis and visualisation platform for the smart city

Authors: Kai Sun; Naidi Liu; Xinghua Sun; Yuxin Zhang

Addresses: Shanghai Urban Construction Vocational College, Shanghai 201415, China ' School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, Hebei, China ' School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, Hebei, China ' School of Information Science and Technology, Hebei Agricultural University, Baoding 071000, Hebei, China

Abstract: Through the construction of smart cities, the modernisation of urban governance systems and governance capacities can be improved. However, the constructions of smart cities face the challenges of data failure, lack of relevance, information fragmentation, fundamental data without accepting new data and innovative concepts. Business big data are extracted, and converted from different departments, and then these structured, semi-structured, and non-structured data are extracted and transformed to load in data warehouse by ETL through the data sharing and exchange platforms. Then joint databases and element searches were used to create multi-department data business views to support specific applications in smart cities. This research realises the smart city big data visual analysis system. The system architecture includes data access layer, data management layer, data analysis layer and release management layer. The system mainly includes four modules: people's livelihood service, citizen big data, urban operation and big data map. This system helps break data barriers, connect data islands, and digitise many municipal businesses, so as to perform data analysis and data visualisation, and provide support for refined governance decision making. The system realises the access, integration, transformation, visualisation and interactive decision analysis of various data of urban life.

Keywords: smart city; big data; urban governance; data visualisation.

DOI: 10.1504/IJITM.2023.131842

International Journal of Information Technology and Management, 2023 Vol.22 No.3/4, pp.373 - 385

Received: 29 Oct 2021
Received in revised form: 24 Mar 2022
Accepted: 30 Mar 2022
Published online: 04 Jul 2023 *

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