Spatial urban data system: a cloud-enabled big data infrastructure for social and economic urban analytics

Anejionu, O. C.D., Thakuriah, P. (V.), McHugh, A., Sun, Y. , Mcarthur, D. , Mason, P. and Walpole, R. (2019) Spatial urban data system: a cloud-enabled big data infrastructure for social and economic urban analytics. Future Generation Computer Systems, 98, pp. 456-473. (doi: 10.1016/j.future.2019.03.052)

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The Spatial Urban Data System (SUDS) is a spatial big data infrastructure to support UK-wide analytics of the social and economic aspects of cities and city-regions. It utilises data generated from traditional as well as new and emerging sources of urban data. The SUDS deploys geospatial technology, synthetic small area urban metrics, and cloud computing to enable urban analytics, and geovisualization with the goal of deriving actionable knowledge for better urban management and data-driven urban decision making. At the core of the system is a programme of urban indicators generated by using novel forms of data and urban modelling and simulation programme. SUDS differs from other similar systems by its emphasis on the generation and use of regularly updated spatially-activated urban area metrics from real or near-real time data sources, to enhance understanding of intra-city interactions and dynamics. By deploying public transport, labour market accessibility and housing advertisement data in the system, we were able to identify spatial variations of key urban services at intra-city levels as well as social and economically-marginalised output areas in major cities across the UK. This paper discusses the design and implementation of SUDS, the challenges and limitations encountered, and considerations made during its development. The innovative approach adopted in the design of SUDS will enable it to support research and analysis of urban areas, policy and city administration, business decision-making, private sector innovation, and public engagement. Having been tested with housing, transport and employment metrics, efforts are ongoing to integrate information from other sources such as IoT, and User Generated Content into the system to enable urban predictive analytics.

Item Type:Articles
Additional Information:We acknowledge the Economic and Social Research Council (ESRC) who funded the Urban Big Data Centre (UBDC) to undertake this project as part of the Big Data Phase 2 of the UK Research and Innovation’s.
Glasgow Author(s) Enlighten ID:Thakuriah, Professor Piyushimita and Sun, Mr Yeran and Mason, Dr Phil and Mcarthur, Dr David and Walpole, Mr Rod and McHugh, Dr Andrew and Anejionu, Dr Obinna
Authors: Anejionu, O. C.D., Thakuriah, P. (V.), McHugh, A., Sun, Y., Mcarthur, D., Mason, P., and Walpole, R.
College/School:College of Social Sciences > School of Social and Political Sciences
College of Social Sciences > School of Social and Political Sciences > Urban Studies
College of Social Sciences > School of Education > People, Place & Social Change
Journal Name:Future Generation Computer Systems
ISSN (Online):0167-739X
Published Online:01 April 2019
Copyright Holders:Copyright © 2019 Elsevier
First Published:First published in Future Generation Computer Systems 98:456-473
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
651921Urban Big Data Research CentrePiyushimita ThakuriahEconomic and Social Research Council (ESRC)ES/L011921/1SPS - URBAN STUDIES
3040420UBDC Centre TransitionNick BaileyEconomic and Social Research Council (ESRC)ES/S007105/1S&PS - Administration