Collecting and Visualizing Real-Time Urban Data through City Dashboards

Dashboards which collect and display real-time streamed data from a variety of rudimentary sensors positioned in the built environment provide an immediate portal for decision-makers to get some sense of their city and environment. These devices are linked to previous renditions of control and management of real-time services in cities, particularly transport, in control-room like settings but they are more flexible and do not require massive investment in hardware. At one level they are simply screens linked to some sort of computational device whose displays are focused in web page like formats. Here we catalogue the experience of building such dashboards for large cities in Great Britain. In particular, we link these to the emergence of open data, particularly reflecting the experience of the London Datastore. We then show how such dashboards can be configured in many different ways: as data tables which give some sort of physical presence to such data delivery, to purpose-built dashboards for schools, and to various moveable displays that have artistic as well as informative merit. To an extent as real-time streamed data become less of a novelty, we expect these dashboards to merge into more generic portals but for the moment they represent one very public face of the smart city and its big data

A Comprehensive Review on Computer Vision Analysis of Aerial Data

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.Comment: 112 page

Driving vivid virtual environments from sensor networks

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 89-95).The rise of ubiquitous sensing enables the harvesting of massive amounts of data from the physical world. This data is often used to drive the behavior of devices, but when presented to users, it is most commonly visualized quantitatively, as graphs and charts. Another approach for the representation of sensor network data presents the data within a rich, virtual environment. This thesis introduces the concept of Resynthesizing Reality through the construction of Doppelmarsh, the virtual counterpart of a real marsh located in Plymouth Massachusetts, where the Responsive Environments Group has deployed and maintained a network of environmental sensors. By freely exploring such environments, users gain a vivid, multi-modal, and experiential perspective into large, multi-dimensional datasets. We present a variety of approaches to manifesting data in "avatar landscape", including landscapes generated off live video, tinting frames in correspondence with temperature, or representing sensor history in the appearance and behavior of animals. The concept of virtual lenses is also introduced, which makes it easy to dynamically switch sensor-to-reality mapping from within virtual environments. In this thesis, we describe the implementation and design of Doppelmarsh, present techniques to visualize sensor data within virtual environments, and discuss potential applications for Resynthesizing Reality.by Don Derek Haddad.S.M

Iconic architecture through the lens of Instagram: the case studies of the Guggenheim Museum, Bilbao and the Dongdaemun Design Plaza, Seoul

Architecture has played an enormous role in the branding of cities, initially through cultural institutions such as museums, which have become the preferred platform for the expression of iconic architecture to boost the image of a city’s modernity and economic prosperity, and to express its civic pride. In recent years the seemingly endless potential of social media has allowed the consumption of architecture to surpass the boundaries of space and time. The instant image sharing and dissemination of Instagrammably photogenic iconic architecture has made the notion of ‘iconicity’ more questionable than it might have been before the social media era. This research aims to explore the manner in which contemporary iconic architecture is represented in social media, with a specific focus on the manner in which such architectural imagery moulds ‘iconicity’ in architecture; in doing so, it investigates the ways in which city image is incorporated into the social imagery of architecture. Using the two case studies of Frank Ghery’s Guggenheim Museum in Bilbao and Zaha Hadid’s Dongdaemun Design Plaza and Park in Seoul, the thesis scrutinises user-generated photographic images and accompanying textual descriptions, which were downloaded from Instagram. The empirical work involves a two-part multi-method approach combining visual content analysis and discourse analysis, using an adaptation of Panofsky’s Iconology, which was borrowed from art history. A general picture of the representational practices of Instagram images was gained through content analysis; this is followed by qualitative readings of individual images using Panofsky’s iconographic-iconological method. The results demonstrate that there are key elements that convey architectural iconicity in Instagram images. These include: (a) the heightened aesthetics of image-taking through the maximisation of aesthetic value in the portrayal of a building; (b) verbal texts alongside an image, which deliver information on the building; and (c) geographic associations through geo-tagging and hashtagging, and textual components, such as a caption and comments. The findings further indicate that, given that a majority of images are depicted in relation to architectural context, this context, in other words, the place in which a building is situated, is essential for the reception and perception of iconicity in the building. The present study is cross-disciplinary in nature, which serves as an important contribution to academic research into place branding by bringing together architecture, city branding, and social media. This is the first time that the Panofsky model of iconology has been applied to the field of place branding

Expanding Data Imaginaries in Urban Planning:Foregrounding lived experience and community voices in studies of cities with participatory and digital visual methods

“Expanding Data Imaginaries in Urban Planning” synthesizes more than three years of industrial research conducted within Gehl and the Techno–Anthropology Lab at Aalborg University. Through practical experiments with social media images, digital photovoice, and participatory mapmaking, the project explores how visual materials created by citizens can be used within a digital and participatory methodology to reconfigure the empirical ground of data-driven urbanism. Drawing on a data feminist framework, the project uses visual research to elevate community voices and situate urban issues in lived experiences. As a Science and Technology Studies project, the PhD also utilizes its industrial position as an opportunity to study Gehl’s practices up close, unpacking collectively held narratives and visions that form a particular “data imaginary” and contribute to the production and perpetuation of the role of data in urban planning. The dissertation identifies seven epistemological commitments that shape the data imaginary at Gehl and act as discursive closures within their practice. To illustrate how planners might expand on these, the dissertation uses its own data experiments as speculative demonstrations of how to make alternative modes of knowing cities possible through participatory and digital visual methods

The Expansion of Densely Inhabited Districts in a Megacity - Case of Tokyo

Understanding the patterns of human concentrations within megacities is of fundamental importance to our understanding of megacity dynamics, and for megacity management and policy making. This study presents an updated investigation of the historical expansion of densely inhabited districts (DIDs) in the world\u27s largest megacity, Tokyo. Long-term DID data (1960-2010) at 5- year intervals were analyzed in a geographic information systems framework. Results show that Tokyo completed rapid growth phase and is now in a maturity phase with minimal growth. Extension was the main form of expansion, although fragmented growth in the form of patches was also noted. The rate of DID expansion was strongly related to economic trends. However the direction and shape of expansion was influenced much by geographic and policy related factors. West and southern directions had earlier and greater expansion, likely related to the historical Tōkaidō corridor. Over 95% of all DIDs are located within 4km distance from a railway line. The coastline and distance from the CBD had some modifying influence. During the course of expansion, there was substantial decrease of population density in the inner wards. Future trends in Tokyo\u27s DIDs will be greatly influenced by aging demographic trends. This study therefore shows that megacity spatial expansion is a dynamic process influenced by various processes whose roles vary over time

The Autopoietic City – how to create a city that can create itself (and why it matters)

This major research paper (MRP) argues how a simple process that is self-sustaining can create a more balanced, thoughtful and livable framework for cities. How? By analyzing the history of cities, their challenges and the range of solutions proposed over time, we can uncover a set of simple rules that govern how individual cities work within their environmental and structural context. We can then use these rules to help communities create the spaces and places they want to live in. We can also see how breaking these rules can cause a cascade of solutions that invariably fail. The rules are mechanisms which create an internally-consistent system. This MRP aims to integrate these rules into a coherent framework for the system. We want to turn many little, individual transactions into a cohesive force that creates a livable, thriving city, without prescribing that people should act a certain way. We view humans realistically with all our flaws, not idealistically as perfectly rational beings. We view people as participants in their own vision of the city. By doing so, we achieve cities that are functional without having to wish them into being through top-down governance processes. With this approach, we enable cities that create themselves naturally, following simple patterns and relationships. This MRP tests this system of rules using three methods from the strategic foresight and innovation toolkit; using 1) systemic leverage points, 2) foresight scenario building and 3) strategic windtunnelling. We make the case that these rules are both necessary and sufficient to enact paradigm-shifting change. The first part of this MRP is gathering data from the past in the form of case studies, research, lived experience and statistics. The second part attempts to gather ‘data from the future’ in the form of scenarios, strategies and thought experiments. Together we will have an idea that balances reliability and validity. The outcome of this MRP will be a business case to support further investment of resources to prototype these rules in a variety of applications for those who want to make a lasting impact on the next century of human progress

CITY PROFILE: USING SMART DATA TO CREATE DIGITAL URBAN SPACES

In the process of modern urban development, cities face various challenges such as climate change, air pollution and poverty, which have negative effects on urban sustainable development and self-regulation. The construction of smart cities can effectively improve the capability of urban management and operation. In this paper, we aim to explore how to use the big data in urban physical, social and cyber spaces to construct smart cities. The concept of digital urban space is proposed to help achieve the construction of smart cities, and city profiling is accordingly presented as a construction method of digital urban spaces and city profile as a product. According to the goals of constructing digital urban spaces, we illustrate the conception and core implementation steps of city profiling, including urban facets modelling and urban facets profiling with smart data. With three application scenarios, we discuss how city profile can be used to meet the factual needs of management, operation and decision-making. City profile can model the cities with urban data and make them become organisms managed and operated by data, so that various information services related to the city can be provided to different users

Generative AI in the Construction Industry: A State-of-the-art Analysis

The construction industry is a vital sector of the global economy, but it faces many productivity challenges in various processes, such as design, planning, procurement, inspection, and maintenance. Generative artificial intelligence (AI), which can create novel and realistic data or content, such as text, image, video, or code, based on some input or prior knowledge, offers innovative and disruptive solutions to address these challenges. However, there is a gap in the literature on the current state, opportunities, and challenges of generative AI in the construction industry. This study aims to fill this gap by providing a state-of-the-art analysis of generative AI in construction, with three objectives: (1) to review and categorize the existing and emerging generative AI opportunities and challenges in the construction industry; (2) to propose a framework for construction firms to build customized generative AI solutions using their own data, comprising steps such as data collection, dataset curation, training custom large language model (LLM), model evaluation, and deployment; and (3) to demonstrate the framework via a case study of developing a generative model for querying contract documents. The results show that retrieval augmented generation (RAG) improves the baseline LLM by 5.2, 9.4, and 4.8% in terms of quality, relevance, and reproducibility. This study provides academics and construction professionals with a comprehensive analysis and practical framework to guide the adoption of generative AI techniques to enhance productivity, quality, safety, and sustainability across the construction industry.Comment: 74 pages, 11 figures, 20 table