With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users. Instead, tackling fairness must be inclusive and involve domain experts with specialized tools and workflows. Thus, domain-specific visualizations are needed for algorithmic fairness. Furthermore, while much work on AI fairness has focused on predictive decisions, less has been done for fair allocation and planning, which require human expertise and iterative design to integrate myriad constraints. We propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages explanations of causal attribution (Why), contrastive (Why Not) and counterfactual reasoning (What If, How To) to aid domain experts to assess and alleviate unfairness in allocation problems. We apply the framework to fair urban planning for designing cities that provide equal access to amenities and benefits for diverse resident types. Specifically, we propose an interactive visual tool, Intelligible Fair City Planner (IF-City), to help urban planners to perceive inequality across groups, identify and attribute sources of inequality, and mitigate inequality with automatic allocation simulations and constraint-satisfying recommendations (IF-Plan). We demonstrate and evaluate the usage and usefulness of IF-City on a real neighborhood in New York City, US, with practicing urban planners from multiple countries, and discuss generalizing our findings, application, and framework to other use cases and applications of fair allocation.

This research proposes a framework for signal processing and information fusion of spatial-temporal multi-sensor data pertaining to understanding patterns of humans physiological changes in an urban environment. The framework includes signal frequency unification, signal pairing, signal filtering, signal quantification, and data labeling. Furthermore, this paper contributes to human-environment interaction research, where a field study to understand the influence of environmental features such as varying sound level, illuminance, field-of-view, or environmental conditions on humans’ perception was proposed. In the study, participants of various demographic backgrounds walked through an urban environment in Zürich, Switzerland while wearing physiological and environmental sensors. Apart from signal processing, four machine learning techniques, classification, fuzzy rule-based inference, feature selection, and clustering, were applied to discover relevant patterns and relationship between the participants’ physiological responses and environmental conditions. The predictive models with high accuracies indicate that the change in the field-of-view corresponds to increased participant arousal. Among all features, the participants’ physiological responses were primarily affected by the change in environmental conditions and field-of-view.

Public spaces play an important role in the processes of formation, generation and change of urban identity. Under present day conditions, the identities of cities are rapidly deteriorating and vanishing. Therefore, the importance of urban design, which is a means of designing urban spaces and their physical and social aspects, is ever growing. This paper proposes a novel methodology by using Principle Component Analysis (PCA) and K-means clustering approach to find important features of the urban identity from public space. K. Lynch's work and Space Syntax theory are reconstructed and integrated with POI (Points of Interest) to quantify the quality of the public space. A case study of Zürich city is used to test of these redefinitions and features of urban identity. The results show that PCA and K-means clustering approach can identify the urban identity and explore important features. This strategy could help to improve planning and design processes and generation of new urban patterns with more appropriate features and qualities.

Urban spatial structure in large cities is becoming ever more complex as populations grow in size, engage in more travel, and have increasing amounts of disposable income that enable them to live more diverse lifestyles. These trends have prominent and visible effects on urban activity, and cities are becoming more polycentric in their structure as new clusters and hotspots emerge and coalesce in a wider sea of urban development. Here, we apply recent methods in network science and their generalization to spatial analysis to identify the spatial structure of city hubs, centers, and borders, which are essential elements in understanding urban interactions. We use a ‘big’ data set for Singapore from the automatic smart card fare collection system, which is available for sample periods in 2010, 2011, and 2012 to show how the changing roles and influences of local areas in the overall spatial structure of urban movement can be efficiently monitored from daily transportation.In essence, we first construct a weighted directed graph from these travel records. Each node in the graph denotes an urban area, edges denote the possibility of travel between any two areas, and the weight of edges denotes the volume of travel, which is the number of trips made. We then make use of (a) the graph properties to obtain an overall view of travel demand, (b) graph centralities for detecting urban centers and hubs, and (c) graph community structures for uncovering socioeconomic clusters defined as neighborhoods and their borders. Finally, results of this network analysis are projected back onto geographical space to reveal the spatial structure of urban movements. The revealed community structure shows a clear subdivision into different areas that separate the population’s activity space into smaller neighborhoods. The generated borders are different from existing administrative ones. By comparing the results from 3 years of data, we find that Singapore, even from such a short time series, is developing rapidly towards a polycentric urban form, where new subcenters and communities are emerging largely in line with the city’s master plan.To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.

There is an increased interest in the efficient creation of city models, be it virtual or as-built. We present a method for synthesizing complex, photo-realistic facade images, from a single example. After parsing the example image into its semantic components, a tiling for it is generated. Novel tilings can then be created, yielding facade textures with different dimensions or with occluded parts in painted. A genetic algorithm guides the novel facades as well as in painted parts to be consistent with the example, both in terms of their overall structure and their detailed textures. Promising results for multiple standard datasets - in particular for the different building styles they contain - demonstrate the potential of the method.

This paper focuses on a method for parameterization of urban space at the pedestrian scale. There is a growing demand for new design approaches that would help to slow down urban sprawls, move away from modernist urban planning models and improve the quality of urban centers overall [15]. One important aspect that contributes to the overall sustainability of contemporary cities is the quality of pedestrian space. To enhance urban environment at pedestrian scale new design strategies must be investigated. Contemporary urban centers must provide various options for public transport, contain fine-grained urban functions, mixed-use building types and good-quality public open spaces to facilitate their use by pedestrians. The aim of this paper is (a) to identify the initial set of design parameters of urban form at the pedestrian scale and (b) to demonstrate the exemplary implementation of these parameters within parametric software, e.g. Grasshopper for Rhinoceros [26] and Esri CityEngine [25] for analysis, optimization and evaluation of urban settings.

We comment on design teaching and communication in the domain of architectural education, particularly in the initial parts of the design process, where hand sketching is applied. We are aware of advantages that electronic sketch devices incorporate, therefore we investigate their implementation in diverse phases of architectural studio education. We are testing our presumptions on site, running an experimental design studio with students at two universities: ETH Zurich and CVUT Prague. The focus lies in configuration and further use of Value Lab in teaching and in an adequate sketch application development. The tool is called ColLab sketch and its multi-interfacial manner fits large touch-tables as well as small tablets.

This paper describes a method for creating spatial illusion projections in a simple yet efficient way. It uses the horizontal and vertical lens shift properties of a virtual camera in a 3D modelling software to produce a normalized image that can be subsequently mapped with a traditional mapping technique, as cornerpin keystone correction for instance, onto a real facade. Our calculations describe how to automatically derive horizontal and vertical shift values. The method was developed during the creation process of a series of projections created with the 3D modelling software “Blender”.