Abstract
This article provides an overview of the specifications of web-based computing platforms for urban data analytics and computational urban planning practice. There are currently a variety of tools and platforms that can be used in urban computing practices, including scientific computing languages, interactive web languages, data sharing platforms and still many desktop computing environments, e.g., GIS software applications. We have reviewed a list of technologies considering their potential and applicability in urban planning and urban data analytics. This review is not only based on the technical factors such as capabilities of the programming languages but also the ease of developing and sharing complex data processing workflows. The arena of web-based computing platforms is currently under rapid development and is too volatile to be predictable; therefore, in this article we focus on the specification of the requirements and potentials from an urban planning point of view rather than speculating about the fate of computing platforms or programming languages. The article presents a list of promising computing technologies, a technical specification of the essential data models and operators for geo-spatial data processing, and mathematical models for an ideal urban computing platform.
Highlights
In this article we focus on the applications of urban computing in Smart Cities Planning practice (as proposed by (Batty et al, 2012))
We propose that using a dataflow programming platform, the user can interact with the platform knowing only a common programming language to edit the nodes and only a handful of UI manoeuvres to get started; without the problem of learning a sophisticated UI
Similar to Python, Jupyter notebooks can be used thanks to the IRkernel55. In response to this question: “What are the essential means for urban computing?”, we have provided an overview of specific data models and functionalities required in dealing with geo-spatial data processing, referred to as spatial computing in Figure 3 and Table 1, which we deem as the essential means for urban computing
Summary
In this article we focus on the applications of urban computing in Smart Cities Planning practice (as proposed by (Batty et al, 2012)). They suggest that there is a need for a paradigm-shift in urban planning, from focus on the built environment problems to social problems such as deprivation, and their relations to space, spatial distributions and spatial planning. Considering the complexity of cities, they imply that there is a need to develop “a new science of human [spatial] behaviour” This paradigm shift towards developing new [spatial] sciences of cities can be facilitated by the so-called urban computing practices, e.g., by facilitating access to large datasets on human spatial behaviour. This article seeks to illustrate what are the essential means of urban computing practice from a methodological point of view, i.e., computational requirements for 1) developing scientific knowledge in the form of validated analytic/simulation models using spatial data and spatial relations; and 2) informing planning actions using the insight gained from analytic/simulation models on effectiveness of actions
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