Project description

Based on analysis and visualization of a massive amount of geographical and location-based behavioural data, this project aimed to shed a light on the lack of urban planning and social resources in the outskirt area of China’s capital, Beijing. With human element stories, the project also aimed to illustrate how migrant workers struggled and thrived there before they were forced to leave in the wave of gentrification. ~In late 2017, a deadly blaze in a Southern Beijing suburb killed 19 people living in a densely packed rental house. The heart-breaking incident was followed by a displacement of the underprivileged in the city in the dry and freezing winter. Most of the displacement occurred in-between two trunk ring roads of the city, namely the 5th Ring Road and the 6th Ring Road. The ring-shaped area surrounded by the two roads accommodated many migrant workers who could not afford to live elsewhere in the city. ~Before the displacement, the reporting team happened to have been working with a geo-data service company on a data journalism project to depict the urban planning issues on the city’s fringe. ~The project consists of 3 sections. (#1.) Following a series of zooming-in slides illustrating the history and development of the suburb, there is a group of 12 detailed maps exhibiting an extremely uneven distribution of residents, jobs, medical and education resources and etc. in the area to discuss the disparity and the problems therefore caused, e.g. traffic jam. (#2.) An interactive scattered plot was presented to increase user engagement – Users can choose two sets of living quality data out of 6 options to plot the graph. In most cases, points cluttered at the low-graded area on the resulting plot implying the suburb is a less suitable place to live. (#3.) Interviews with urbanist and sociologist were also included to further discuss the problems and solutions. Human interest stories on how the residents endured the bad living condition in order to thrive were also presented to the audience. A song composed by one of the interviewee was embedded at the end of the article.

What makes this project innovative?

(#1.) Exploring new sources of data, big data: Although official data was not available, the team managed to work with geo-data scientists to work on alternative forms of data. Besides urban statistics, the analysis also included data from city sensors, e.g. air quality sensor, and masked location-based mobile signal big data. (#2.) Finer granularity of details: instead of comparing different administrative divisions of Beijing which is a normal practice in local media, this project compares the 800 villages (much smaller than an admin. div.) located on the city’s fringe. (#3.) Mobile adaptation: the interactive scattered plot was replaced by a draggable radar diagram for better user experience on mobile device. (#4.) Pre-polling: Survey was set up to collect residents’ opinions and reach for potential interviewees. (#5.) Users have choices: the interactive plot provide the users with choices of different living standard factors and a chance to rethink the meaning of city life.

What was the impact of your project? How did you measure it?

(#1.) This project was disseminated widely via social media sharing on messenger app, WeChat, in China. (#2.) On the timeline bulletin board of WeChat users, audience and their followers were surprised to look into the place they lived, which is often overlooked in media, with the aid of data-viz. (#3.) Active discussion on city planning and living experience was observed among social media peers. The project was also discussed in a number of WeChat group chats on community work and city planning. (#4.) Most importantly, we believe that, with more understanding on how we live and work in a city with the aid of data, we the citizens are more empowered to rethink their privileges and responsibilities.

Source and methodology

(#1.) Data sources: masked mobile signal data with location, city sensor data, user review data from location-based crowdsource reviewing app, statistics from urban planning bureau, the city’s masterplan, research papers, opinion survey, interviews and field study. (#2.) Methodology: The project was carried out by data journalism team with Caixin in collaboration with a geo-data service company, GeoHey. GeoHey was responsible for the extraction of insightful data from its big data warehouse and the reporting team worked on further analysis of the data on QGIS. The reporting team also did interviews with experts to interpret the results.

Technologies Used

Technologies used: QGIS for analysis and prototyping of map visualizations. Adobe Illustrator and ai2html for creating graphs. HTML, CSS, JavaScript and D3.js for data visualization and web-page construction. Adobe After Affect and Audacity for video and sound editing.

Project members

Liu Jiaxin, Hu Fenhai, Leng Bin, Huang Chen, Geng Mingzhong


Project owner administration

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