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.

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With the increasing role of ICT in enabling and supporting smart cities, the demand for big data analytics solutions is increasing. Various artificial intelligence, data mining, machine learning and statistical analysis-based solutions have been successfully applied in thematic domains like climate science, energy management, transport, air quality management and weather pattern analysis. In this paper, we present a systematic review of the literature on smart city big data analytics. We have searched a number of different repositories using specific keywords and followed a structured data mining methodology for selecting material for the review. We have also performed a technological and thematic analysis of the shortlisted literature, identified various data mining/machine learning techniques and presented the results. Based on this analysis we also present a classification model that studies four aspects of research in this domain. These include data models, computing models, security and privacy aspects and major market drivers in the smart cities domain. Moreover, we present a gap analysis and identify future directions for research. For the thematic analysis we identified the themes smart city governance, economy, environment, transport and energy. We present the major challenges in these themes, the major research work done in the field of data analytics to address these challenges and future research directions.

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The aim of this paper is to present effectiveness of participatory ICT tools for urban planning and supporting bottom up decision making in urban management and governance. Design/methodology/approach-This work begins with presenting state of the art literature on the existing participatory approaches and their contribution to urban planning and policy making process. Further, a case study-namely the urbanAPI project is selected to identify new visualization and simulation tools applied at different levels of urban scales. These tools are applied in four different European cities-Vienna, Bologna, Vitoria-Gasteiz and Ruse-with the objective to identify data needs for application development and commonalities in requirements of such participatory tools and their expected impact in policy and decision making processes. Findings-The case study presents three planning applications i) 3D Virtual Reality at neighbourhood scale, ii) Public Motion Explorer at city wide scale, and iii) Urban Growth Simulation at city-region scale. These applications are dependent on specialized city data to develop required features and often data is not available in appropriate quality and necessitate data harmonization and pre-processing. In addition, urbanAPI applications indicate both active and passive participation secured by applying these tools at different level of urban scale and hence facilitate evidence based urban planning decisions. The level of urban scale at which these tools are applied plays a major role in acquiring participation from expert users or general public. In addition, regular engagement with the city administrations indicates commonalities in user needs and application requirements resulting in the development of generic features in these ICT tools which can be applied to other cities. Research limitations/implications-Top down and bottom up urban planning and policy making needs vary from one city administration to another. This makes it difficult to capture and develop all required features in respective IT tools and often result in failure of software solutions. Therefore, the benefit of identifying commonalities among city needs and requirements for IT tools can result in development of common features and save huge investments in bespoke/ad hoc software development and maintenance costs. The urbanAPI application development follow a structured requirements development methodology where over 50% of commonalities are identified and as a result these tools can be applied at various stages of a policy making cycle/process. However, specialized data is required for the development of these applications which often does not exist in proper quality and format and hence necessitate data harmonization and pre-processing to ensure successful delivery of these tools. Originality/value-This paper presents new ICT enabled participatory urban planning tools at different urban scale to support the collaborative decision making and urban policy development. Various technologies are used for the development of these IT to...