The Use of Historical Data in Rule-Based Modelling for Scenarios to Improve Resilience within the Building Stock

Digital documentation has become integral to the preservation, analysis and communication of historical sites. New platforms are now being developed that involve complex 3D models and allow the analysis of spatial data. These include procedural modelling, a technique that enables the rapid development of ‘dynamic’ 3D environments, and generation of simulations for entire cities, resulting in low cost, high resolution 3D city models. Though procedural modelling has been used in the context of archaeology to ‘recreate’ cities at specific historic time points, the use of historical data in the development of rule-based procedural models for current cities has been little explored. Here, we test the extent to which construction age data, historical building regulations and architectural knowledge can be used in the generation of procedural rules, and the level of detail and potential impact that these models may have. Rather than creating an accurate representation of the city, we instead seek to simulate the way in which urban areas are likely to behave under certain conditions, in order to test what-if? planning scenarios. This allows us to explore more flexible ways of digitally ‘creating’ cities, past and present, and to gain insights into underlying ‘rules’ that govern their physical form

Accelerating Urban Modelling Algorithms with Artificial Intelligence

In this paper, we demonstrate that developments in computer hardware to support the increasingly complex artificial intelligence workflows for Deep Learning networks can be adapted for urban modelling and visualisation. The hypothesis here is that by leveraging the current practice of AI as a Service (AIaaS), then this enables Urban Modelling as a Service (UMaaS) to be developed. The starting point for this paper is a 3D visualisation of the Queen Elizabeth Olympic Park, developed using a web-based spatial interaction modelling system which calculates population metrics on the fly, capable of showing the results of interventions by urban planners in real-time. We take the web application that powers the interactive visualisation and use Google’s TensorFlow AI library to accelerate the matrix operations required to run the spatial interaction model, making the web application fast enough to be used interactively

VR binoculars: An immersive visualization framework for IoT data streams

Every day we find ourselves moving through a blend of material spaces and immaterial networks. This invisible layer created from the millions of the data streams and network connections that take place around us tends to get denser with the recent development and deployment of the IoT devices in the urban space. In our work we aim to explore how the available technology of Mixed Reality spectrum can be applied to provide us an immerse view of the information that exist within the invisible layer of the “cyberspace” [1]. To this extend, we propose “VR Binoculars”, a digital visualization framework that operates in real time.The available Virtual (VR), Augmented (AR), and Mixed Reality (MR) technology is used as a medium to unveil the information that exist in our surrounding space. Specifically, we situate the user within an environment where the digital data visualizations and the physical space are matched together, providing to the user the ability to interact, orient themselves and navigate naturally.For us the proposed framework it is able to promote a better understanding of the IoT ecosystem, it can justify the use of sensors in the public space, and it can raise awareness about privacy and data sharing. Using different urban environments as test cases (indoor, outdoor) we present our methodology and our first results

Visualising spatial and social media

In this chapter, we begin by surveying the development of computer graphics as it has influenced the development of the spatial representation of social and economic data, charting the history of computer cartography and geographic information systems (GIS) which have broadened into a wide array of forms for scientific visualisation. With the advent of the World Wide Web and the widespread adoption of graphical user interfaces (GUIs) to most kinds of computer device, visualisation has become central to most sciences and to the dissemination of many kinds of data and information. We divide our treatment of this domain according to three themes. First we examine how the 2-­‐dimensional map has become key to many kinds of spatial representation, showing how this software has moved from the desktop to the web as well as how 2-­‐d has moved to 3-­‐d in terms of the visualisation of maps. Second, we explore how social data is being augmented by space-­‐time series generated in real time and show how such real-­‐time streaming of data presents problems and opportunities in which visualisation is key. We illustrate these new data for basic feeds from cities but then move to examine data from transit systems, social media, and data that is pulled from the crowd – crowdsourcing. Finally we note the development of visual analytics showing how 2d and 3d spatial representations are essential to interpreting the outputs and the workings of more complex models and simulations. We conclude with the notion that much of what we develop in this chapter for the space-time domain is generic to the future representation of all kinds of social data