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Beautiful Data

Lecturer: Dr. Bo Yuan

E-mail: yuanb@sz.tsinghua.edu.cn

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Exploring Millions of

Social Stereotypes

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• How old do they look?

• Do you think they look smart?

• How do we perceive age, gender, and attractiveness?

Data Analysis!

The FaceStat Judging Interface

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Preprocessing the Data

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Problematic data

Aggregate results from multiple people into a single description

Map from multiple-choice responses to one numerical value

Exploring the Data

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Initial scatterplot matrix of the face data

Exploring the Data

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Initial histogram of face age data

Exploring the Data

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Histogram of cleaned face age data

Age, Attractiveness, and Gender

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Scatterplot of attractiveness versus age, colored by gender

Age, Attractiveness, and Gender

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Smoothed scatterplots for attractiveness versus age, colored by gender

Age, Attractiveness, and Gender

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Three iterations of plotting attractiveness versus age versus gender:(a) ages averaged within buckets per age year, (b) 95% confidence interval for each bucket, plus loess curves, and (c) larger buckets where the data is sparser.

Age, Attractiveness, and Gender

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Pearson correlation matrix

Clustering

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Attractiveness versus age, colored by cluster, 2000 points.

Clustering

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Cluster centroids, tags, and exemplars

Clustering

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Cluster centroids, tags, and exemplars

Conclusion

Our data indicates some familiar stereotypes. Women are considered more attractive than men Age have a stronger attractiveness effect for women than men

Also some potential surprises. Babies are most attractive Conservatives look more intelligent

The point of this instance is not to come to any particular conclusion.

Instead, we want to show some examples of the rich set of significant patterns contained in large, messy data set of human judgments.

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Visualizing Urban Data

Crimespotting Project

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Home Page

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How to Get the Crime Data?

Collect further details on the crime reports

Determining the location of crime

Recognize the crime icon

Get an image from CrimeWatch server

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A Sample Image

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A sample image from CrimeWatch shows areas of the theft, narcotics, robbery, and other crimes.

A Sample Image

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The same sample image from CrimeWatch with programmatically recognized icons outlined.

A Sample Image

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The same sample image with the reddish parts made white to show the red boxing glove icon more clearly.

Geolocation

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A map of downtown Oakland showing three reference points for triangulation purposes.

The Spotlight Feature

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The type selector shows the total numbers of each report type in the selected time span

Conclusion

Crime is a serious issue for any urban resident, by visualizing the crime data can we effectively protect the citizens.

The project has been a productive success, resulting in what we believe is a data service maximally useful to local residents.

City and government information is being moved onto the Internet to match the expectations of a connected, wired citizenry.

For more information about Crimespotting: http://oakland.crimespotting.org/

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Beautiful Political Data

Data Help Obama Win

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Redistricting and Partisan Bias

Redistricting Redistricting is the process of drawing United States electoral

district boundaries, often in response to population changes determined by the results of the decennial census.

Partisan Bias Partisan bias is a measure of how much the electoral system

favors the Democrats or Republicans, after accounting for their vote share.  

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Redistricting and Partisan Bias

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Effect of redistricting on partisan bias

Time Series of Estimates

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Age and Voting

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Sure, young people voted heavily for Mr.Obama, but they voted heavily for John Kerry. ----Mark Penn, Political Consultant

Was he right?

Age and Voting

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Some graphs showing recent patterns of voting by ages

Localized Partisanship in Pennsylvania

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Geographic partisanship in Pennsylvania

Conclusion

Political data is increasingly accessible and is increasingly being plotted and shared in the media and on the web.

At the research level, articles in political science journals are starting to make use of graphical techniques for discovery and presentation of results.

Statistical visualization to become more important and more widespread in political analysis.

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Data Finds Data

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An example Corruption at the Roulette Wheel Past Posting

Data Finds Data

Data Finds Data

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What can data finds data system do for us? Guest Convenience

Customer service

On the way to “data finds data”:

Data Finds Data

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What can data finds data system do for us? Improved Child Safety Cross-compartment Exploitation

Data Finds Data

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What should we solve first? All examples benefit from just in-time discovery. However, we should solve the “enterprise discoverability” problem. Federated search

Do not have the indexes necessary to enable the efficient location of a record.

Requires recursive processing.

Federated search cannot support the “data finds data” mission, because it has no ability to deliver on enterprise discoverability at scale.

Directories are necessary!

Conclusion

Determine how new observations relate to what is known.

Differentiate one organization from another.

Likely become another building block from which next generations of advanced analytics will benefit.

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Exploring Your Life in Data

Exploring Your Life in Data

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Web: About sharing, broadcasting and distributing. About tracking, monitoring, analyzing his\her habits and behaviors.

Tools: PEIR & YFD

Difference: PEIR runs in the background and automatically upload data. YFD requires that users actively enter data.

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Some Examples

DietSense

Family Dynamics

Walkability

Thanks to built-in sensors.

All bring people involved in their communities with just their mobile phones.

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Visualization

• Traces are colored based on impact and exposure values.

• A different mapping scheme that make all trips on the map mono-color, using circles to encode impact and exposure.

• All traces are colored white, and the model values are visually represented with circles that varies in size at the end of each trip.

• Greater values are displayed as circles larger in area while lesser values are smaller in area.

Visualization

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• We grayscaled map tiles and inverted the color filters so that map items that were originally lightly colored turned dark and vice versa.

• To be more specific, the terrain was originally lightly colored, so now it is dark gray, and roads that were originally dark are now light gray.

• This darkened map lets lightly colored traces stand out, and because the map is grayscale, there is less clashing.

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Visualization

• PEIR provides histograms to show distributions of impact and exposure for selected trips.

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PEIR Interface

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Design of Interface in YFD

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Track of Feelings and Emotions

Conclusion

People who collect data about themselves are not necessarily after the actual data.

They are mostly interested in the resulting information and how they can use their own data to improve themselves.

We use the data visualization to teach and to draw interest.

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The Design of Sense.us

The Design of Sense.us

Data beautiful?

How to make it beautiful?

An example to demonstrate: sense.us

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Quartet ——An Example

Four data sets

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Same statistical properties

Quartet ——An Example

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Back to Sense.us

Consider The correlation between two numerical values

To visualize change over time

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scatterplot

line graph

Not always the case

Back to Sense.us

Effect of our choice was influenced by Martin’s Baby Name Voyager visualization, a stacked graph of baby name popularity that became surprisingly popular online.

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Stacked Graph

Job Voyager

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Job Voyager visualization:

Left: an overview showing the constitution of the labor force over 150 years;

Right: a filtered view showing the percentage of farmers.

Differentiate Individual Series

When we filtered the view to show only males or only females.

Enable perceptual discrimination by varying color saturation in an arbitrary fashion.

Rather than vary colors arbitrarily, do so in a meaningful, data-driven way.

Subsequently vary color saturation according to socio-economic index scores for each occupation.

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Stacked Graph

Birthplace Voyager

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Birthplace Voyager visualization:

Left: an overview showing the distribution of birthplaces over 150 years;

Right: a filtered view showing the total number of European immigrants.

U.S. Census State Map and Scatterplot

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Left: Interactive state map showing changes in each state’s population from 2000 to

2005;

Right: Scatterplot of U.S. states showing median household income (x-axis) versus

retail sales (y-axis); New Hampshire and

Delaware have the highest retail sales.

Population Pyramid

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Population pyramid visualization:

Left: a comparison of the total number of males and females in each age group in 2000;

Right: the distribution of school attendees in 2000 (an annotation highlights the

prevalence of adult education).

Conclusion

The combination of interactive visualization and social interpretation can help an audience more richly explore a data set.

The forms of analysis we observed in sense.us were exploratory in nature, the system had a clear educational benefit and users reported that using sense.us was both enjoyable and informative.

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