Towards cloud based big data analytics for smart future cities

TLDR

Cities generate vast land‑use, environmental, socio‑economic, energy, and transport data, and an integrated big‑data approach can inform science, policy, planning, governance, and business decisions to enable smarter environments. The paper proposes a cloud‑based analytics service for smart‑city big‑data management and analysis. The authors implemented a Hadoop‑Spark prototype that analyzes Bristol Open data to identify correlations among urban environmental indicators and compared results across experiments. The prototype demonstrates effective analytics, revealing quality‑of‑life trends in crime, safety, economy, and employment over time.

Abstract

A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.