Journal: IEEE Transactions on Visualization and Computer Graphics (TVCG).

Conference: IEEE Visual Analytics Science and Technology (VAST) 2014.

Authors: Wei Zeng, Chi-Wing Fu, Stefan Arisona, Alexander Erath, Huamin Qu

Abstract: 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.

https://www.youtube.com/watch?v=Q95c8PpneT8

Title: Visualizing Mobility of Public Transportation System
Authors: Wei Zeng, Chi-Wing Fu, Stefan Arisona, Alexander Erath, Huamin Qu
Journal: IEEE TVCG
Publisher: IEEE
Volume: 20(12)
Year: 2014
Pages: 1833 - 1842
Link: https://dx.doi.org/10.1109/TVCG.2014.2346893

Journal: International Journal of Geographical Information Science

Authors: Chen Zhong, Stefan Arisona, Xianfeng Huang, Michael Batty, Gerhard Schmitt

Abstract: 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.

Title: Detecting the Dynamics of Urban Structure Through Spatial Network Analysis
Authors: Chen Zhong, Stefan Arisona, Xianfeng Huang, Michael Batty, Gerhard Schmitt
Journal: International Journal of Geographical Information Science
Publisher: Taylor & Francis
Year: 2014
DOI: 10.1080/13658816.2014.914521
Link: https://www.tandfonline.com/doi/abs/10.1080/13658816.2014.914521#.U8P6N1bsd8E

Conference: 19th International Conference on Computer-Aided Architectural Design Research in Asia CAADRIA 2014

Authors: Chen Zhong, Stefan Arisona, Gerhard Schmitt

Abstract: The advancement of sensor technologies makes it possible to collect large amounts of dynamic urban data. On the other hand, how to store, process, and analyze collected urban data to make them useful becomes a new challenge. To address this issue, this paper proposes a visual analytics framework, which is applied to transportation data to manage and extract information for urban studies. More specif- ically, the proposed framework has three components: (1) a geographic information system (GIS) based pipeline providing basic data processing functions; (2) a spatial network analysis that is integrated into the pipeline for extracting spatial structure of urban movement; (3) interactive operations allowing the user to explore and view the output data sets at different levels of details. Taking Singapore as a case study area, we use a sample data set from the automatic smart card fare collection system as an input to our prototype tool. The result shows the feasibility of proposed framework and analysis method. To summarize, our work shows the potential of geospatial based visual analytics tools in using ‘big’ data for urban analysis.

Award: Won best presentation award.
Title: A Visual Analytics Framework for Large Transportation Datasets
Authors: Chen Zhong, Stefan Arisona, Gerhard Schmitt
Proceedings: 19th International Conference on Computer- Aided Architectural Design Research in Asia CAADRIA 2014
Publisher: The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
Year: 2014
Pages: 223 - 232
Link: https://cumincad.scix.net/cgi-bin/works/Show?caadria2014_014

1 Introduction

The multi-projector-mapper (MPM) is an open-source software framework for 3D projection mapping using multiple projectors. It contains a basic rendering infrastructure, and interactive tools for projector calibration. For calibration, the method given in Oliver Bimber and Ramesh Raskar’s book Spatial Augmented Reality, Appendix A, is used.

The framework is the outcome of the “Projections of Reality” cluster at smartgeometry 2013, and is to be seen as a prototype that can be used for developing specialized projection mapping applications. Alternatively, the projector calibration method alone could also be used just to output the OpenGL projection and modelview matrices, which then can be used by other applications. In addition, the more generic code within the framework might as well serve as a starting point for those who want to dive into ‘pure’ Java / OpenGL coding (e.g. when coming from Processing).

Currently, at ETH Zurich’s Future Cities Laboratory we continue to work on the code. Among upcoming features will be the integration of the 3D scene analysis component, that was so far realised by a separate application. Your suggestions and feedback are welcome!

1 Source Code Repository @ GitHub

The framework is available as open-source (BSD licensed). Jump to GitHub to get the source code:

https://github.com/arisona/mpm

The repository contains an Eclipse project, including dependencies such as JOGL etc. Thus the code should run out of the box on Mac OS X, Windows and Linux.

2 Usage

The framework allows an arbitrary number of projectors - as many as your computer allows. At smartgeometry, we were using an AMD HD 7870 Eyefinity 6 with 6 mini-displayport outputs, where four outputs were used for projection mapping and one as control output:

2.1 Configuration

The code allows opening an OpenGL window for every output (for projection mapped scenes, windows without decorations are used, and they can be placed accordingly at full screen on the virtual desktop):

public MPM() {
  ICalibrationModel model = new SampleCalibrationModel();
  scene = new Scene();
  scene.setModel(model);
  scene.addView(new View(scene, 0, 10, 512, 512, "View 0", 0, 0.0, View.ViewType.CONTROL_VIEW));
  scene.addView(new View(scene, 530, 0, 512, 512, "View 1", 1, 0.0, View.ViewType.PROJECTION_VIEW));
  scene.addView(new View(scene, 530, 530, 512, 512, "View 2", 2, 90.0, View.ViewType.PROJECTION_VIEW));
  ...
}

Above code opens three windows: one control view (which contains window decorations), and two projection views (without decorations). The coordinates and window sizes in this example are just samples and need to be adjusted for a concrete case (i.e. depending on virtual desktop configuration).

2.2 Launching the Application & Calibration

Once the application launches, all views show the default scene. The control view in addition shows the available key strokes. Pressing “2” switches to calibration mode. The views will now show the calibration model, with calibration points. Note that in calibration mode, all projection views will blank, unless their window is activated (i.e. by clicking into the window).

For calibration, 6 circular calibration points in 3D space need to be matched to the their physical counterparts. Thus, when using the default calibration model, which is a cube, a physical calibration rig corresponding to the cube needs to be used:

The individual points can now be matched by dragging them with the mouse. For fine tuning, use the cursor keys. As soon as the 6th point is selected, the scene automatically adjusts.

For first time setup, there is also a ‘fill mode’ which basically projects a white filled rectangle (with a cross hair in the middle) for each projector. This allows for easy rough adjustment of each project. Hit “3” to activate fill mode.

Once calibration is complete, press “S” to save the configuration, and “1” to return to navigation / rendering mode. On the next restart, press “L” to load the previous configuration. When in rendering mode, the actual model is shown, which by default is just a fake model, thus a piece of code that is application specific. The renderer includes shadow volume rendering (press “0” to toggle shadows), however the code is not optimised at this point.

Note that it is not necessary to use a cube as calibration rig - basically any 3D shape can be used for calibration, as long as you have a matching 3D and physical model. Simply replace the initialisation of your ICalibrationModel with an instance of your custom model.

The following YouTube video provides a short overview of the calibration and projection procedure:

https://youtu.be/bWULiD3BWzY

3 Code Internals and Additional Features

The code is written in Java using the JOGL OpenGL bindings for rendering, and the Apache Commons Math3 Library for the matrix decomposition. Most of the it is rather straightforward, as it is intentionally kept clean and modular. Rendering to multiple windows makes use of OpenGL shared contexts. Currently, we’re working on the transition towards OpenGL 3.2 and will replace the fixed pipeline code.

In addition, the code also contains a simple geometry server, basically listening via UDP or UDP/OSC for lists of coloured triangles, which are then fed into the renderer. Using this mechanism, at smartgeometry, we build a system consisting of multiple machines doing 3D scanning, geometry analysis and rendering, by sending geometry data between them using the network. Note that this is prototype code and will be replaced with a more systematic approach in future.

4 Credits & Further Information

Concept & projection setup: Eva Friedrich & Stefan Arisona. Partially also based on earlier discussions and work of Christian Schneider & Stefan Arisona.

Code: MPM was written by Stefan Arisona, with contributions by Eva Friedrich (early prototyping, and shadow volumes) and Simon Schubiger (OSC).

Support: This software was developed in part at ETH Zurich’s Future Cities Laboratory in Singapore.

A general overview of the work at smartgeometry'13 is available at the “Projections of Reality” page.

Authors: Wei Zeng, Chi-Wing Fu, Stefan Arisona, Huamin Qu

Abstract: Massive amount of movement data, such as daily trips made by millions of passengers in a city, are widely avail- able 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 re- vealing 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.

https://www.youtube.com/watch?v=_QWnA1k2ZrU

Title: Visualizing Interchange Patterns in Massive Movement Data
Authors: Wei Zeng, Chi-Wing Fu, Stefan Arisona, Huamin Qu
Journal: Computer Graphics Forum
Publisher: Wiley
Year: 2013
Volume: 32(3)
Pages: 271-280
DOI: 10.1111/cgf.12114
Link: https://onlinelibrary.wiley.com/doi/10.1111/cgf.12114/abstract