Urban forms at human-scale, i.e., urban environments that individuals can sense (e.g., sight, smell, and touch) in their daily lives, can provide unprecedented insights on a variety of applications, such as urban planning and environment auditing. The analysis of urban forms can help planners develop high-quality urban spaces through evidence-based design. However, such analysis is complex because of the involvement of spatial, multi-scale (i.e., city, region, and street), and multivariate (e.g., greenery and sky ratios) natures of urban forms. In addition, current methods either lack quantitative measurements or are limited to a small area. The primary contribution of this work is the design of StreetVizor, an interactive visual analytics system that helps planners leverage their domain knowledge in exploring human-scale urban forms based on street view images. Our system presents two-stage visual exploration: 1) an AOI Explorer for the visual comparison of spatial distributions and quantitative measurements in two areas-of-interest (AOIs) at city- and region-scales; 2) and a Street Explorer with a novel parallel coordinate plot for the exploration of the fine-grained details of the urban forms at the street-scale. We integrate visualization techniques with machine learning models to facilitate the detection of street view patterns. We illustrate the applicability of our approach with case studies on the real-world datasets of four cities, i.e., Hong Kong, Singapore, Greater London and New York City. Interviews with domain experts demonstrate the effectiveness of our system in facilitating various analytical tasks.

This paper presents a framework which allows urban planners to navigate and interact with large datasets fused with social feeds in real-time, enhanced by a virtual reality (VR) capability, which further promotes the knowledge discovery process and allows to interact with urban data in natural yet immersive way. A challenge in urban planning is making decisions based on datasets which are many times ambiguous, together with effective use of newly available yet unstructured sources of information like social media. Providing expert users with novel ways of representing knowledge can be beneficial for decision making. Game engines have evolved into capable testbeds for novel visualization and interaction techniques. We therefore explore the possibility of using a modern game engine as a platform for knowledge representation in urban planning and how it can be used to model ambiguity. We also investigate how urban planners can benefit from immersion when it comes to data exploration and knowledge discovery. We apply the concept of using primitives to publicly available transportation datasets and social feeds of New York city, we discuss a gesture-based VR extension of our framework and lastly, we conclude the paper with feedback from expert users in urban planning and with an outlook of future challenges.

This paper presents a method and case study to visualize the urban stock of materials and its availability for use in building future cities. Re-using material from existing buildings for new buildings can be seen as a source for construction materials in times of depleting natural resources. The authors explain the concept of "urban mining" and the challenges, such as "How much resources are available in a city? Today? In the near future?" We explore what data are needed to answer the questions, and then discuss how to best visualize the data in an effective and intuitive way. We apply the concept to an exemplary real-world district in Singapore that is in transformation. Then, we discuss features of a visual tool prototype and explain the thinking behind the design, e.g., how the spatial and temporal dimensions can be presented. Lastly, we conclude the paper with an outlook of future challenges. The paper presents a multi-disciplinary approach with researchers from computer science, architecture, graphic design and material science, and contributes to the discussion of how to visualize knowledge and plan sustainable future cities.

In transportation studies, one fundamental problem is to analyze the departures and arrivals at locations in order to predict the travel demands for urban planning and traffic management. These movements can relate to many factors, e.g., activity distributions and household demographics. This paper presents how we use visualization to explore the relationship between people movements and activity distributions that are characterized by the points of interest (POIs). To effectively model and visualize such relationship, we introduce POI-mobility signature, a compact visual representation with two main components. 1) A mobility component to present major people movements information across temporal dimension. 2) A POI component to present the activity context over an area of interest in spatial domain. To derive the signature, we study assorted analytical tasks after discussing with transportation researchers, consider essential design principles, and apply the representation to study a real-world dataset, which is the massive public transportation data in Singapore with over 30 million trajectories and crowd-sourcing POIs retrieved from Foursquare. Finally, we conduct three case studies and interview three transportation experts to verify the efficacy of our method.

Human’s daily movements exhibit high regularity in a space–time context that typically forms circadian rhythms. Understanding the rhythms for human daily movements is of high interest to a variety of parties from urban planners, transportation analysts, to business strategists. In this paper, we present an interactive visual analytics design for understanding and utilizing data collected from tracking human’s movements. The resulting system identifies and visually presents frequent human movement rhythms to support interactive exploration and analysis of the data over space and time. Case studies using real-world human movement data, including massive urban public transportation data in Singapore and the MIT reality mining dataset, and interviews with transportation researches were conducted to demonstrate the effectiveness and usefulness of our system.

Abstract Origin‐destination (OD) pattern is a highly useful means for transportation research since it summarizes urban dynamics and human mobility. However, existing visual analytics are insufficient for certain OD analytical tasks needed in transport research. For example, transport researchers are interested in path‐related movements across congested roads, besides global patterns over the entire domain. Driven by this need, we proposewaypoints‐constrained OD visual analytics, a new approach for exploring path‐related OD patterns in an urban transportation network. First, we use hashing‐based query to support interactive filtering of trajectories through user‐specified waypoints. Second, we elaborate a set of design principles and rules, and derive a novel unified visual representation called thewaypoints‐constrained OD viewby carefully considering the OD flow presentation, the temporal variation, spatial layout and user interaction. Finally, we demonstrate the effectiveness of our interface with two case studies and expert interviews with five transportation experts.

Many contemporary research tasks require complex decision making and interdisciplinary collaboration among different groups of experts and stakeholders. Nowhere is this more apparent than in the planning and design of future cities - rapidly growing cities where the development and allocation of energy, housing, transportation and other scarce resources requires the active participation of architects, urban planners, government stakeholders and private citizens. In this paper, we show how recent advances in display technology can facilitate such collaboration and create new opportunities for participatory urban planning. We introduce the Value Lab Asia as a model for collaborative planning and value creation, and show how a multi-screen display management framework called the Tool Library integrates the laboratory's physical and software infrastructure with the planning process.

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.

Public transportation systems (PTSs) play an important role in modern cities, providing shared/massive transportation services that are essential for the general public. However, due to their increasing complexity, designing effective methods to visualize and explore PTS is highly challenging. Most existing techniques employ network visualization methods and focus on showing the network topology across stops while ignoring various mobility-related factors such as riding time, transfer time, waiting time, and round-the-clock patterns. This work aims to visualize and explore passenger mobility in a PTS with a family of analytical tasks based on inputs from transportation researchers. After exploring different design alternatives, we come up with an integrated solution with three visualization modules: isochrone map view for geographical information, isotime flow map view for effective temporal information comparison and manipulation, and OD-pair journey view for detailed visual analysis of mobility factors along routes between specific origin-destination pairs. The isotime flow map linearizes a flow map into a parallel isoline representation, maximizing the visualization of mobility information along the horizontal time axis while presenting clear and smooth pathways from origin to destinations. Moreover, we devise several interactive visual query methods for users to easily explore the dynamics of PTS mobility over space and time. Lastly, we also construct a PTS mobility model from millions of real passenger trajectories, and evaluate our visualization techniques with assorted case studies with the transportation researchers.

Abstract Massive amount of movement data, such as daily trips made by millions of passengers in a city, are widely available nowadays. They are a highly valuable means not only for unveiling human mobility patterns, but also for assisting transportation planning, in particular for metropolises around the world. In this paper, we focus on a novel aspect of visualizing and analyzing massive movement data, i.e., the interchange pattern, aiming at revealing passenger redistribution in a traffic network. We first formulate a new model of circos figure, namely the interchange circos diagram, to present interchange patterns at a junction node in a bundled fashion, and optimize the color assignments to respect the connections within and between junction nodes. Based on this, we develop a family of visual analysis techniques to help users interactively study interchange patterns in a spatiotemporal manner: 1) multi‐spatial scales: from network junctions such as train stations to people flow across and between larger spatial areas; and 2) temporal changes of patterns from different times of the day. Our techniques have been applied to real movement data consisting of hundred thousands of trips, and we present also two case studies on how transportation experts worked with our interface.

Visualization is a medium for communicating information to and between people with different background knowledge. The availability of digital tools and the progress of visualization technologies enhance the potential of visual representation. But along with it, issues emerge as how to choose appropriate forms of visualization to make results generally understandable. This paper presents our approach to convey information on research conducted at ETH Zurich's Future Cities Laboratory (FCL) to a broad target audience through visualization. We first carry out a requirements analysis for the visualization task. The results of the analysis are elaborated and implemented by means of a software prototype that employs an interactive Dymaxion Map to visualize the information given in our case.

Abstract In this paper we propose a real‐time rendering approach for procedural cities. Our first contribution is a new lightweight grammar representation that compactly encodes facade structures and allows fast per‐pixel access. We call this grammarF‐shade. Our second contribution is a prototype rendering system that renders an urban model from the compact representation directly on the GPU. Our suggested approach explores an interesting connection from procedural modeling to real‐time rendering. Evaluating procedural descriptions at render time uses less memory than the generation of intermediate geometry. This enables us to render large urban models directly from GPU memory.