Big Data and Urban Informatics: Innovations and Challenges to Urban Planning and Knowledge Discovery

Part of the Springer Geography book series (SPRINGERGEOGR)


Big Data is the term being used to describe a wide spectrum of observational or “naturally-occurring” data generated through transactional, operational, planning and social activities that are not specifically designed for research. Due to the structure and access conditions associated with such data, their use for research and analysis becomes significantly complicated. New sources of Big Data are rapidly emerging as a result of technological, institutional, social, and business innovations. The objective of this background paper is to describe emerging sources of Big Data, their use in urban research, and the challenges that arise with their use. To a certain extent, Big Data in the urban context has become narrowly associated with sensor (e.g., Internet of Things) or socially generated (e.g., social media or citizen science) data. However, there are many other sources of observational data that are meaningful to different groups of urban researchers and user communities. Examples include privately held transactions data, confidential administrative micro-data, data from arts and humanities collections, and hybrid data consisting of synthetic or linked data.

The emerging area of Urban Informatics focuses on the exploration and understanding of urban systems by leveraging novel sources of data. The major potential of Urban Informatics research and applications is in four areas: (1) improved strategies for dynamic urban resource management, (2) theoretical insights and knowledge discovery of urban patterns and processes, (3) strategies for urban engagement and civic participation, and (4) innovations in urban management, and planning and policy analysis. Urban Informatics utilizes Big Data in innovative ways by retrofitting or repurposing existing urban models and simulations that are underpinned by a wide range of theoretical traditions, as well as through data-driven modeling approaches that are largely theory agnostic, although these divergent research approaches are starting to converge in some ways. The paper surveys the kinds of urban problems being considered by going from a data-poor environment to a data-rich world and the ways in which such enquiries have the potential to enhance our understanding, not only of urban systems and processes overall, but also contextual peculiarities and local experiences. The paper concludes by commenting on challenges that are likely to arise in varying degrees when using Big Data for Urban Informatics: technological, methodological, theoretical/epistemological, and the emerging political economy of Big Data.


Big Data Urban Informatics Knowledge discovery Dynamic resource management User generated content 


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Urban Studies and Urban Big Data CentreUniversity of GlasgowGlasgowUK
  2. 2.Department or Urban Planning and PolicyCollege or Urban Planning and Public Affairs, University of Illinois at ChicagoChicagoUSA

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