urban science
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Urban Science ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 3
Author(s):  
Aenne A. Brielmann ◽  
Nir H. Buras ◽  
Nikos A. Salingaros ◽  
Richard P. Taylor

This article reviews current research in visual urban perception. The temporal sequence of the first few milliseconds of visual stimulus processing sheds light on the historically ambiguous topic of aesthetic experience. Automatic fractal processing triggers initial attraction/avoidance evaluations of an environment’s salubriousness, and its potentially positive or negative impacts upon an individual. As repeated cycles of visual perception occur, the attractiveness of urban form affects the user experience much more than had been previously suspected. These perceptual mechanisms promote walkability and intuitive navigation, and so they support the urban and civic interactions for which we establish communities and cities in the first place. Therefore, the use of multiple fractals needs to reintegrate with biophilic and traditional architecture in urban design for their proven positive effects on health and well-being. Such benefits include striking reductions in observers’ stress and mental fatigue. Due to their costs to individual well-being, urban performance, environmental quality, and climatic adaptation, this paper recommends that nontraditional styles should be hereafter applied judiciously to the built environment.


2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhuangyuan Fan ◽  
Becky P.Y. Loo

AbstractOngoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.


2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Junyi Zhang ◽  
Tao Feng ◽  
Jing Kang ◽  
Shuangjin Li ◽  
Rui Liu ◽  
...  

AbstractThe COVID-19 pandemic has caused various impacts on people’s lives, while changes in people’s lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people’s lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people’s lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about “what should be computed?” in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.


2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Andrew Karvonen ◽  
Vladimir Cvetkovic ◽  
Pawel Herman ◽  
Karl Johansson ◽  
Hedvig Kjellström ◽  
...  

AbstractDigitalisation is an increasingly important driver of urban development. The ‘New Urban Science’ is one particular approach to urban digitalisation that promises new ways of knowing and managing cities more effectively. Proponents of the New Urban Science emphasise urban data analytics and modelling as a means to develop novel insights on how cities function. However, there are multiple opportunities to broaden and deepen these practices through collaborations between the natural and social sciences as well as with public authorities, private companies, and civil society. In this article, we summarise the history and critiques of urban science and then call for a New Urban Science that embraces interdisciplinary and transdisciplinary approaches to scientific knowledge production and application. We argue that such an expanded version of the New Urban Science can be used to develop urban transformative capacity and achieve ecologically resilient, economically prosperous, and socially robust cities of the twenty-first century.


2021 ◽  
pp. 1-26
Author(s):  
Markus Rittenbruch ◽  
Marcus Foth ◽  
Peta Mitchell ◽  
Rajjan Chitrakar ◽  
Bryce Christensen ◽  
...  

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
James Patterson ◽  
Niko Soininen ◽  
Marcus Collier ◽  
Christopher M. Raymond

AbstractWhile innovative approaches to urban transformations are increasingly proposed, scholars often overlook challenges faced by endogenous actors (e.g. urban planners) tasked with taking action within non-ideal, real-world settings. Here we argue that an ‘inside’ view of transformations (focused on judgment in practice) is needed to complement existing ‘outside’ views (focused on assessment), where the feasibility of action becomes a central concern. This recasts urban transformations in a discretised perspective. It suggests a view of transformation pathways as both directed and stochastic, and emergent from an unfolding series of ‘fuzzy action moments’. Principles for bridging urban science and planning are derived.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ariel Salgado ◽  
Weixin Li ◽  
Fahad Alhasoun ◽  
Inés Caridi ◽  
Marta Gonzalez

AbstractWe present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.


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