Personal Resume Data Visualization

Team Member:  Siyang(Autumn) Jing

The Personal Resume Visualization is designed to tell my personal story in an interesting and clear way. This resume is used for job interviews, whose audience is interviewers. Because both the interviewer and I are designers, the goal is that the visualization map is beautiful and easy to understand.

Though refection of my personal experiences, I categorize my data to three layers, which are concluded as three key words, brad global view, professional achievements as well as reliable and efficient personality. The first layer, the outer one, talked about the first key word, global view. This associated with my education background, my practice, the activities the teaching experience and awards. The middle layer is my design projects throughout my academic career. The project are labeled with professional tags, which will make my work type very clear to the audience. The inner layer illustrated my special personalities, which will benefit my future career. All the three layers will tell the audience my personal story.

It is not only a personal data visualization for me, but also an opportunity to reflect on myself and make progress in the future.


Policy Tower

Team Members: Almaha Almalki, Lisa Woo, Jingxian Zhang, and Siyang(Autumn) Jing

We set up the scenario in a Mayor’s Summit, World Cities Summit or G20. The mayors of metropolitan areas all over the world will discuss the future solutions for the environmental problems, which including the air pollution as one of the most important topic. As the air pollution

Phenomena of Beijing draw the wide attention, the mayor of Beijing will show other policy makers how the policy will help control the air pollution problem.

Based on the Beijing pollution data from 2008 to 2016, we found the quality of the air had huge relationship with the holding of the big events such as Beijing Olympic Games, National Day March, APEC, etc. Behind the blue sky, there is strong policies to affect the air quality.

So we choose the year of 2014 and 2016 to see how different combination of policies could affect air pollution. The policies are categorized to long term policies and short term policies. Long term policies are renovation of heating systems, vehicle traffic restriction based on plate number, license-registration lottery, vehicle traffic restriction based on exhaust etc. Short term policies are vehicle traffic restriction based on plate number, close some factories in Beijing and its neighbor provinces temporarily , stop using X% of buses, and shut down construction sites temporarily etc.

The demo of the Policy Tower: In the tower, each cup represents an environmental policy. Different amount of blue colors, which represent strength of different policies, will be put in the cups. Yellow water (air pollution) will be poured from the top and go through all the cups. It will gradually turn green while drilling down — the more (and more strong the) policies, the more green the water will become. This is how policy tower works.

But we still need ask more question and do more work to complete this visualization. For example, we need more data for how much each policy contributes to the improvement of air pollution. Combining the cost of different policies to measure the feasibility of each of them, etc.

Link to Slides:

Architecture and Related Services Occupation Share

-what data is being shown

The data show the occupations by share in the architecture and related services.

-who you think the audience is

The audience are architects, people who have the related jobs, people who are interested in the architecture and related services, people who study the structure of the architecture related occupations, and people who want to start business related to architecture etc.

-what you think the goals of the data presentation are

The goal of this map is to show there are many related services of architecture, and what are the Percentage of those services in the industry chain. Secondly, the map creator also categories the occupations by management, business, science and art; Sales and office occupations; Service occupations; production, transportation and material moving occupations; natural resources, construction and maintenance occupations; Military specific occupations. In addition, the complicated map indicate the innate association of one occupation with another.

-whether you think it is effective or not and why

It is very effective in terms of showing the percentages of each occupation take in the whole industry. And the category is very clear. It is very helpful to understand the relationships and thus better refine this architecture related industry. It is also good that the audience could click on each occupation to see the population and the salaries. However, I find some of the information are too small to read. In addition, I could not understand the logic of how the map creator associate one occupation with another and why the order of the occupations are like these.

Siyang Jing’s Data Log

8:00_wake up and check Facebook, WeChat, news on the phone and chatting with my friends on line for a while.

8:30_turn on a Reading book App while doing the exercise. 4

9:00_checking the email while having breakfast

10:00_Checking out the book in the library

12:00_Watch the TV show while having lunch at home

14:00_Use the Harvard App to Check the M2 shuttle to go to MIT

15:00_From a discussion group by using google PPT, google Doc and other group chatting softwares.

18: 00_go to the Gym running while listening to music

19:00_Using an food making App to teach me prepare for the food for tomorrow

20:00_Chatting with my team through wechat and sometimes facetime with them

21:00_Doing homework and searching the materials on line

23:00_make a phone call to my mother or friends.

00:00_Check Facebook, WeChat and News online

Most often used: Wechat, google chrome, office 365