Macs' Journal

Links, video, papers and more of my research interests in data visualization.

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In this version I have added the following features: - Bus numbers - Color distinction between rides that the user can take nearby (blue) and possible connections (gray) - Ambient display location (blue circles) - current vehicles position of nearby rides - Highlighting transfer through colored dots (scale going from red to green where red means that the transfer time is tight, and green that the transfer time is about 15 minutes)  Other improvements to be added: - Get nearby ride and transfer ride not only within the same station but in the nearby area (radius to be defined) - Add side bar with precise time information of vehicles coming - Add the legend for the time color scale

Possible future ideas to be implemented: - Map distortion for improving map readability - Real-time isochrone map - Real-time near-by-vehicles landmarks

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WebGL animation alpha version

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Visualizing Mobility of Public Transportation System

Authors: Wei Zeng, Chi-Wing Fu, Stefan Mu ̈ller Arisona, Alexander Erath, Huamin Qu | paper

In this paper the authors presents their tool for exploring public transport systems data. Their goal is to build a tool that allows not only to explore network topology, but also other mobility related factors, such as riding time, transfer time, waiting time and round-the-clock patterns.

The tool is a combination of three visualization models: - isochrone map view: regions accessible from a certain location within a given duration - isotime flow map view: temporal information comparison and manipulation - OD-pair journey view: for detailed visual analysis between a specified origin-destination pair.

The definition of the problem is very clear and interesting, namely: - INPUTS = <origin A, time t0, duration T> - OUTPUTS = <set of destination reachable from A at t0 within T> - GOAL = Explore mobility related factors associated with the set of routes.

The paper presents also an interesting and extended review of the literature, divided in five categories. Moreover the authors stated very clearly the tasks that their tool supports, namely:

Given the input information A, t0 and T , extract and presentreachable destinations B. 

Present clear pathways/routes from A to B. 

Examine and compare the travel time and travel efficiencyof the routes from A to B. 

Present detailed path information from A to Bi, i.e., variousmobility-related factors, see below for details. 

Examine the mobility-related factors and their round-the-clock pattern, i.e., their temporal variations over a day. 

The paper presents also two case studies that explain how the tool can be use and the interviewed of two transportation researchers. One of the researchers commented that: 

"it is interesting to visualize transfer information, as they are strongly negatively perceived travel elements."

Reading the whole paper turned out that the target user for the visualization is the transportation researcher or urban planner. I think that audience of the visualization should have been stated earlier in the abstract and/or in the introduction to better contextualize the problem being solved.

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Project Goals and Hypothesis

Goals

Test different visual encodings to understand which one is better

Improving static public transport maps adding temporal information

Let the user to freely plan his journey without constraints in the transportation type

Build a visualization that shows a large amount of movements data at a fine grain level with no user interaction

Filter out unnecessary information, such as bus trips that the user cannot take

Find an aesthetic for the visualization design that fits the audience

Find and efficient system infrastructure to deal with the complexity of CTA data

Implement smooth animation client side, finding an efficient algorithm and a suitable technology (SVG, canvas or WebGL?)

Provide user with time estimation of transit vehicles

Highlight connections of bus trips with bus trips

Hypothesis and tasks for proving hypothesis

Dynamic maps make it easier for the user to find a feasible ride through the public transportation network compared to static maps. 

user would try to find path to destination both in the dynamic map and in the static map: which one is faster to use?

A lot of unnecessary information are avoided using a dynamic map which shows only feasible rides

The amount of visual clutter can be measured by means of statistical saliency methods ( how easy is to add a new object that can attract the user in a given environment? ).

User would find the public transport system easier to use and therefore they would use it more.

Interviewing people

The perception of the quality of the service will improve (think when you are waiting for a bus but you have no idea of when the bus is coming)

Interviewing people

A pleasant aesthetic for the visualization makes the wayfinding process more user friendly.

Interview people

The visualization can be real-time.

Proved by running the algorithm and determine if the visualization is smooth.

WebGL should perform better than the other technologies

Build the visualization with different technologies, test them and compare them.

Time estimations are necessary for the user in order to plan an effective journey.

User is able to see connections where he can catch other vehicles.

Test if the user can find a journey to a given destination where at least one connection have to be founded.

Visual encoding that employs animation should better convey the direction and speed of vehicles.

Example paper: Drawing Road Networks with Focus Regions

This paper presents a distortion technique for focus+context approaches. The authors claim that in a map showing a network of roads relevant to the user, distortion should preferably take place where the network is sparse. The hypothesis they made is that enlarging the focus region and minimizing the changes to local star (subgraph with a center node and its neighbors), the output would be a map where distortions happen in areas where the network is sparse. Their hypothesis is confirmed by  running the algorithm over ten cities. As an example the road network of Boston contains three dense neighborhoods and as shown in the image below distortion happens mostly in the areas where the network is sparse.

I think the result is convincing and the goal is achieved. One implicit assumption that the authors made is that the network distorted using their approach is more useful for the user. This should be proved with a user case study.

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Concept #1

Canvas implementation: http://hakim.se/experiments/html5/trail/03/

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Visualizing Public Transport Systems: State-of-the-Art and Future Challenges

I surveyed and summarized the state-of-the-art techniques for visualizing Public Transport Systems. Moreover some of the most exciting unsolved problems are also reported. link

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First step..

The first step of the project was to implement an overview map visualizing all the trips around the city. This map give a sense of how much the public transportation system is crowded.

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Focus+Context Metro Maps

Authors: Yu-Shuen Wang, Ming-Te Chi | paper

In this article the authors present a focus+context method to visualize complicated metro maps on a small display area, such as a smartphone. The map layout design is inspired by Mr. Beck's underground map, with the following guidelines: - straight lines - regularly spaced stations - maximal angle of incident edges - octilinear edge direction (either vertical, horizontal or 45°)

Authors defines the technique "deformation" because it differs from distortion in that is highly readable. The result is also compared to distortion techniques such as fish-eye lenses as shown in picture below.

The authors instead are performing a smart deformation which doesn't affect readability of elements that are out of focus. The process for building the schematic metro layout is interesting and it is shown by the image below:

The network layout of the subway system is taken as input in the first stage where a smooth deformation is applied. In the following stage all the edges are transform into an octilinear setting and finally in the last stage the map is labeled.

This work has two contribution: the main contribution is a focus+context technique which can effectively display metro maps on a small display, and the second contribution is a novel method for automatic layout of metro maps.

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Visualization of Spatio-temporal Data of Bus Trips

Authors: Hong Thi Nguyen, Chi Kim Thi Duong, Tha Thi Bui, and Phuoc Vinh Tran | paper

This article presents the implementation of visualization tools for the design of bus travels on temporal maps. It explains how to use the so call space-time cube to represent bus trips, namely buses moving on bus routes at given times. Authors claim that most of the tools already available in this context do not support passenger travel selection, hence all the decisions are taken by the tool. But as we know user needs are diversified, in fact some look for the path with the shortest time, other (i.e. tourists) look for long paths for sightseeing, and some others don't want to wait long time at connections between buses. Authors claim that their solution is suitable for all these user needs.

Basically the tool consists in an interactive 3D visualization that use cartesian chart to display geographic location through x and y axes and temporal information along z axis. For each bus route a normalized bus trip is build using a shape-based abstractions of the bus trips. The user then select the departure time to get information about the actual bus trip (question: does this allow for integration with real-time online data?).

In my opinion the premises of this work are good, especially the need to address with one tool all the possible user needs, but the tool developed is not as good as the premises are. First of all, the tool seems not easy to use, in fact there are several steps to take in order to obtain the information we want. Moreover I think that the visualization is not suitable for the type of users that this tool is addressing. In fact the visualization has a scientific style which in my opinion is not suitable for everyday users of a transportation systems.

Regarding the paper itself, it is not easy to understand the authors' solution; probably a showcase video would make it easier to comprehend what the tool is doing. More over the introduction is very repetitive without really adding more information; I think that it could be summarized in just a few sentences. Finally authors argues that their tool is suitable for many user needs, but no user study has been reported in the article to backup this fact.

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Visual Abstraction of Complex Motion Patterns

Authors: Halldòr Janetzko, Dominik Ja ̈ckle,Oliver Deussen Daniel A. Keim | paper

The problem of visualizing motion over time is difficult in complex networks since there is an high degree of over-plotting since the are many revisits of the same place.

The purpose of the paper is to present a technique developed by the authors for visualizing complex motion patterns. The described approach is based on the application of two visual abstraction techniques: geospatial and temporal. Authors claim that this method reduce visual clutter and help to better understand movement patterns. The main contributions are clearly stated and are: - user controlled density-based analysis of trajectories - geospatial aggregation and simplification for visualizing transition between regions of interest - temporal partition of trajectories according to movements changes

In the literature the main approaches to complex motion visualization are: - simplification - aggregation - distortion

Authors suggests that is difficult to visualize movement without using distortion techniques even though the approach they are proposing is not using any distortion technique.

The technique proposed by the authors can be briefly described as partitioning and aggregate trajectories and visual abstract trajectories when certain properties are full-filled.

Geospatial abstraction: partition and aggregate trajectories, visually abstract trajectories when certain properties are fulfilled. The purpose of visual abstraction is also to get rid of unimportant data. For example in the albatrosses showcase authors are filtering based on speed in order to get rid of foraging behaviors and highlight migration behaviors.

The visualization is performed both on map and using the small multiple technique. In the former technique clusters are shown and trajectory are simplified using the Douglas-Peucker algorithm. The latter technique is used to display temporal changes in the behaviors using a simplified graph that keeps topology coherency with the map. The nodes that are not contained in a given timespan are grayed out.

Authors claim that their method is applicable to every type of movement data. They have received important feedbacks by biology experts and most important being the integration of land use categories (e.g. forest, urban, etc.) that can help to understand the context upon which motion occurs; in the problem of visualizing vehicles movement in a complex transportation network this concept correspond to use useful landmarks that help the user to orientate.

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Some art piece with D3.js

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Interesting algorithm talk using visualization