81 Slow Trains,The Thoughtfulness of Chinese Speed 情系人民的公益”慢火车”

Country/area: China

Organisation: Jiangxi University of Finance and Economics XinHua net

Organisation size: Small

Publication date: 15/07/2021

Credit: Yubin Wang 王妤彬,Liguo Wang 王立国,Jing Huang黄晶,Shenwen Ye叶深文,Yuting Zhang张於庭


We are a team from Jiangxi University of Finance and Economics(JUFE) in China, and our members are all undergraduate students majoring in journalism and communication.  Followed the guidance of teacher team (Yubin Wang,Peng Nie, Shujun Luo), gathered in the Visualization for Data Journalism Studio of JUFE, we hope to present our in-depth and special thinking through data news and bring new thoughts to our readers. We are still growing and look forward to producing more mature works in the future.

Project description:

This work is bilingual in Chinese and English. By visually presenting data related to the 5633/4 train in Xide County, Sichuan Province, the work tries to illustrate that the slow-speed trains, as a crucial means of transportation serving millions of people in poverty-stricken areas, have the basic characteristics of slow speed, multiple stations, long dwell time, cheap ticket and quality service, which constitutes an important and distinctive picture of China’s poverty alleviation work.

Impact reached:

The short version of this work, titled “情系人民的‘公益慢火车’”, was first posted on the Xinhua Net’s National channel, receiving over 1 million hits in a single day and over 2.2 million hits in total, and was reposted by other media , China Economic Net etc.

Techniques/technologies used:

We have completed this work  based on the  Financial and Economics Data Journalism Visualization via Virtual Simulation-Experimental Based learning platform, combined with the open platforms called dychart and Map-lab
We choose appropriate charts for different types of data. dy chart provides us with a large number of original chart templates including rose diagram, water polo diagram, jade diagram and so on. Map-lab is a powerful tool for us to make maps, and the self-created platform is mainly used to layout and create a few charts. In addition, some charts were created and beautified by photoshop.

What was the hardest part of this project?

The most difficult part: The work involves a relatively wide variety of data including geographic location data, continuous data, discrete data and some unstructured data. What is most tough for us is independently excavating and organizing the complicated and diverse data, especially geographical position data of the trains and the information of poverty-stricken counties in different years.

The highlight of the work: This work captures the strong contrast between China’s rapid development and slow-speed train project for poverty alleviation. Through data analysis and visualization, readers can get a different perspective on China’s poverty alleviation achievements outside the mainstream media’s coverage. Reporting the news from diverse perspectives helps readers gain a more comprehensive understanding of China’s poverty alleviation efforts.

What can others learn from this project?

In this work, our self-createdFinancial and Economics Data Journalism Visualization via Virtual Simulation-Experimental Based learning platform serves as an aggregator, which may provide some inspiration for other journalists. There are many data visualization platforms with their own advantages on the market, and by building our own technology platform, we were able to integrate the strengths of them to better serve the data visualization work.

Project links: