Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics
Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics
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DOI：
10.1016/j.future.2019.03.052
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发表时间：
2019-09-01
影响因子：
7.5
通讯作者：
Walpole, Rod
中科院分区：
文献类型：
--
作者：
Anejionu, Obinna C. D.;Thakuriah, Piyushimita (Vonu);Walpole, Rod
空间城市数据系统（SUDS）是一个空间大数据基础设施，支持英国范围内的城市和城市地区的社会和经济方面的分析。它利用传统以及新兴的城市数据来源产生的数据。SUDS部署了地理空间技术、综合小区域城市指标和云计算，以实现城市分析和地理可视化，目标是获得可操作的知识，以更好地进行城市管理和数据驱动的城市决策。该系统的核心是通过使用新形式的数据和城市模型及模拟方案产生的城市指标方案。SUDS与其他类似系统的不同之处在于，它强调从实时或近实时数据源生成和使用定期更新的空间激活城市区域指标，以加强对城市内部相互作用和动态的理解。通过在系统中部署公共交通、劳动力市场可达性和住房广告数据，我们能够确定城市内关键城市服务的空间变化，以及英国主要城市的社会和经济边缘化产出区域。本文讨论了SUDS的设计和实现，遇到的挑战和限制，以及在开发过程中需要考虑的问题。在设计中采用的创新方法将使其能够支持对城市地区、政策和城市管理、商业决策、私营部门创新和公众参与的研究和分析。经过住房、交通和就业指标的测试，目前正在努力将其他来源的信息（如物联网和用户生成内容）整合到系统中，以实现城市预测分析。(C) 2019 Elsevier B.V.版权所有
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. (C) 2019 Elsevier B.V. All rights reserved.