Instead of using a world map as background image to locate the cities and countries, we inputted the latitude and longitude of each city and the central geo location for each country. The data then naturally formed us a global map. Cities and countries are represented by dots with size mapped to a totally volume of departure or arrival routes. We used two planes rather than one, as we believe two planets will better explain the dataset. The planet above (in a red tune: Red – Departure Country / Orange – Departure City) represents the departure cities/countries and the planet below (in a blue tune: Blue – Arrival Country / Green – Arrival City) represents the arriving cities/countries.

What this project provides you:
We provide a overview of the general information about this flight pattern database. This overview contents two modes: view by countries and view by cities. When certain mode is selected, corresponding lines will be drawn between the departure locations and the arriving locations. This visualization will provide the audience a general glance about which countries or cities are more activated and which are less. The more activated location will have clusters of lines and brighter color which attacks more attention. All the lines here share the same weight. In order to better presenting the volume, we mapped the volume to the number of lines and shift each lines’ position a little while generating the lines for the same country or city, this method creates interesting cluster effect instead of usual thick lines.

3 Dimension:
3D layout will provide much more variety than 2D and 1D formats. Audience would be able to observe the data in different angles which makes a project more interesting and attractive. Here, inspired by some architecture models, we brought in a sculpture concept, lines are not just information any more, they created a structure of history. By disable all the lines and only display the dots of cities or countries, the audience would be able to compare the volume of routes between departure and arrival cities.

Geek Part:
Algorithm we used for this visualization are fairly simple. One thing we find out about the country data set is, USA has a leading number of routes compare with all the other countries, around 12000. China places the second but the number of routes is only about half of United States’ size. Therefore, in order to scale the number down, we used logarithm number instead of the original number for dot size to scale down the “difference” between countries.

Filters to create Details-on-Demand:
It’s never a good visualization without providing details. Therefore, we hand over the control to the audience. The control panel at the top will give users the power to dig in to the data set and find customized information which they are most interested with.
Enable/Disable Country Dots: Display/Hide the dots represents countries.
Enable/Disable City Dots: Display/Hide the dots represents cities.
Enable/Disable Country Pattern: Display/Hide the lines that links the departure and arrival countries.
Enable/Disable City Pattern: Display/Hide the lines that links the departure and arrival cities.
Domestic Flight Pattern: Display/Hide domestic flights.
International Flight Pattern: Display/Hide international flights.
Cities/Countries: Choose to highlight cities’ patten or countries’ pattern, this need to be used with Departure and Arrival Pattern toggles.
Enable/Disable Departure Pattern: Display/Hide the flights that are departed from the highlighted cities/countries.
Enable/Disable Arrival Pattern: Display/Hide the flights that are arrived to the highlighted cities/countries.

We added key control for high light cities or countries. Both planets have been divided into 20 slices based on longitude and latitude. Press UP/DOWN arrow key will move along latitude and LEFT/RIGHT will move along longitude. Highlighted countries or cities’ detail will be displayed on the screen based on departure data and arriving data.
A lot of interested patterns could be generated from the control panel.

What we are interested as the designers:
The first thing we want to know when get this data set is of course, without a doubt, find out some “big events” that could cause huge population to arrive a city. Olympic Games is the first event that popped up our heads. Therefore, we did some queries to find out the “secret” about Olympic Games. The data set contents flight information from 1990 to April 2012. This covers five Summer Olympic Games: Barcelona(1990), Atlanta(1996), Sydney(2000), Athens(2004) and Beijing(2008). We visualized the flight pattern and population change in each of the Olympic years targeting only American flights to the Olympic host cities. However, as we care more about international flights, we left Atlanta out as it is inside the United States. Interesting patterns we find out, Barcelona and Athens both have a huge population increase during the Olympic time period. The number of passengers almost double for Barcelona. However, bigger city like Sydney and Beijing have a less obvious change, although the volume increased at a certain lever, but not as exciting as the former cities.

Add a Comment

Login or register to post comments


J.Jean's picture

stunning ! I am a fan of yours !

superwin's picture

good job guys!

rongbao1989's picture

sooooooooooooooooooooooo coooooooooooooooooooooool

howiepku's picture

one more time, one more chance~

howie_wong2's picture

haha, small teng niu ~~ i come again

yoyolong00's picture

so cool

luqing's picture

very good

db's picture


yvf's picture

So amazing!

Jason Bourn1273's picture

fantastic visual effect! the coolest video ever seen.

yitai's picture


Hao7771010111's picture

good work.

ucsbFanXu1019's picture

Cool project, I like it.

fengzjl's picture


stonecold_jason's picture


primerzju's picture

Nice work!

Yinghui's picture

good work!

rockwts's picture

In Chinese:贼好看!

pinkcyan's picture

nice color, wonderful work!

dyjian415's picture

Very beautiful

dyjian415's picture

Very beautiful

edg3's picture

pretty awesome! coooool!

XIAO's picture

good job!

yoosung's picture


aaronwxb's picture


howiewhy's picture

LOL small teng cow ~

Posted Nov 11, 2012
Views: 7493
Tags Flight Patterns, VisMarathon
Tools C++, Excel, Processing, Python
<iframe src="http://visualizing.org/embedded/44503" width="620" height="450" frameborder="0" scrolling="no" marginheight="0" marginwidth="0"></iframe>
Need help embedding?