Stefan Müller Arisona

Inferring building functions from a probabilistic model using public transportation data

Cities are complex systems. They contain different functional areas originally defined by planning and then reshaped by actual needs and use by the inhabitants. Estimating the functions of urban space is of significant importance for detecting urban problems, evaluating planning strategies, and supporting policy making. In light of the potential of data mining and spatial analysis techniques for urban analysis, this paper proposes a method to infer urban functions at the building level using transportation data obtained from surveys and smart card systems. Specifically, we establish a two-step framework making use of the spatial relationships between trips, stops, and buildings. Firstly, information about the travel purposes for daily activities is deduced using passengers’ mobility patterns based on a probabilistic Bayesian model. Secondly, building functions are inferred by linking daily activities to the buildings surrounding the stops based on spatial statistics. We demonstrate the proposed method using large-scale public transportation data from two areas of Singapore. Our method is applied to identify building functions at building level. The result is verified with master plan, street view, and investigated data, and limitations are identified. Our work shows that the presented method is applicable in practice with a good accuracy. In a broader context, it shows the effectiveness of applying integrated techniques to combine multi-source data in order to make insights about social activities and complex urban space.

Revealing centrality in the spatial structure of cities from human activity patterns

Identifying changes in the spatial structure of cities is a prerequisite for the development and validation of adequate planning strategies. Nevertheless, current methods of measurement are becoming ever more challenged by the highly diverse and intertwined ways of how people actually make use of urban space. Here, we propose a new quantitative measure for the centrality of locations, taking into account not only the numbers of people attracted to different locations, but also the diversity of the activities they are engaged in. This ‘centrality index’ allows for the identification of functional urban centres and for a systematic tracking of their relative importance over time, thus contributing to our understanding of polycentricity. We demonstrate the proposed index using travel survey data in Singapore for different years between 1997 and 2012. It is shown that, on the one hand, the city-state has been developing rapidly towards a polycentric urban form that compares rather closely with the official urban development plan. On the other hand, however, the downtown core has strongly gained in its importance, and this can be partly attributed to the recent extension of the public transit system.

Visualizing the Relationship Between Human Mobility and Points of Interest

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.

StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views

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.

A visual analytics design for studying rhythm patterns from human daily movement data

Abstract 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.

Time-Space Diagram Revisited

Widely used in the design and analysis of transportation systems, time–space diagrams were developed in an era of data scarcity, when it was necessary to obtain data by means of driver logs, human observers, and aerial photographs. This paper shows how time–space diagrams remain relevant today, in an era of data abundance. An application efficiently encodes the trajectories of bus GPS data in a time–space cube and uses simple geometric methods to calculate and to visualize the headways and separation of buses on a bus route. These methods are discussed in detail. How they can be used as the basis of a software package that monitors performance measures for a variety of applications is explored.

Biennale 4D - Exploring the Archives of the Swiss Pavilion at the «Biennale di Venezia» Art Exhibition.

The Swiss pavilion at «Biennale di Venezia» offers a platform for national artists to expose their work. This well documented white cube displays the change of contemporary Swiss art from the early 50 s up to today. The project «Biennale 4D» pursues the goal to make the archives of these past art exhibitions more comprehensible by creating an interactive explorative environment through the use of innovative Virtual Reality technology. The project poses multiple challenges like visualization of historic content and its documentation, dealing with the heterogeneity and incompleteness of archives, interaction design and interaction mapping in VR space, integration of meta data as well as realizing a Virtual Reality experience for the public space with current VR technology.