Shared Bicycle Scheme Datathon Brief

1. Shared bicycle scheme in China datathon By Bart, Chira, Wei, Haimei, Jiamei and Shuo 1

2. What is the shared bicycle scheme? • This is a service in which bicycles are made available for shared use to individuals on a very short term basis. The first 30mins – 1hr are usually free or very inexpensive compared to other modes of transport. • Hangzhou bike-sharing system was the first in China starting in 2008. There are now numerous cities across China that have one or more competing bike-sharing programs. • In Shenzhen there are several different brands to choose from with some minor differences. Most are accessible via a phone app. 2

8. What’s the problem? • Users are finding that the app’s location services are slow and unreliable • Users wants to know where the nearest type of bike is (newer and cheaper) • The market is cluttered and quite saturated – How can Mobike improve their services? – Do people speak more positively or negatively about Mobike? – Does providing ‘free rides’ have a positive impact on sentiment? 8

9. How should we use the data? The data we had • 20mil CN newspapers txt files from 2016 to present • Access to Baidu tie ba, Weibo, Zhihu, blogs and search engines • Github Mobike GPS data real-time What we did with it • Keyword search to narrow down data set to 15,000 articles • Manual searching for the term ‘free rides’ to pin point offers in the past year • Track real-time location of Mobikes 9

10. How should we use the data? 10

12. How should we use the data? 12 • Access to Baidu tie ba, Weibo, Zhihu, blogs and search engines

13. How should we use the data? • Manual searching for the term ‘free rides’ to pin point offers in the past year 13

15. How do our data representations help? Our analysis • Heat map of popular docking locations • Cluster map of no. of available bikes by location Solution • Helps users know the location of their nearest Mobike in real-time (faster than the current app) and how many are available • Helps Mobike understand where to place more bikes at particular times of day 15

16. How do our data representations help? Our analysis • Route maps of Mobikes Solution • Helps users know popular routes taken by other users – To avoid – To plan better cycling routes – To know if a bike is coming to a location near them soon 16

17. How do our data representations help? Our analysis • Sentiment analysis of Mobike Solution • Helps Mobike understand if users are favourable towards them – With more data we could begin to draw comparisons on other brands • Helps Mobike see if ‘free rides’ make users more favourable towards them • Helps Mobike see if ‘free rides’ are beneficial to their business model (does it increase usage etc.) 17

18. Method • First, we download the sentiment dictionary from the Web, which includes stop word list, negative word list, degree adverb list. • Second, we cut the sentence by a tool named jieba and remove the stop words. • Third, we record the sentiment word and its position. And according to this, we can compute the score of the sentence. • Finally, we sum all sentences of an article and use it as the final sentiment score.

19. Result The number of news per week • The news about shared bicycles becomes more and more. • From the left the news is almost zero, because the shared bicycles just start to use. • From the middle, there is a decrease about the shared bicycles. There is the time of Spring Festival and people have a long holiday, and mainly talk about transport season or relevant topic. 0 200 400 600 800 1000 1200 1400 1600 1800 2016/10/1 2016/11/1 2016/12/1 2017/1/1 2017/2/1 2017/3/1 the number of news

20. Result The sentiment score of news per week • The x axis is time, the y axis is sentiment score. • Almost all news are positive while only a little is negative. • As we all know, the content of news is always official, so the negative is few 0 200 400 600 800 1000 1200 1400 1600 2016/10/1 2016/11/1 2016/12/1 2017/1/1 2017/2/1 2017/3/1 positive vs negative positive negative

21. Result The sentiment score of news per week • The positive ones are usually too long and only contain few negative sentences. So when we sum all sentences, its score becomes bigger and positive. • There is a sharp increase because there are a few articles which talk about situation analyzation of shared bicycles and are very long. • We try to normalize the score but as Xin said it would lose some useful information. So we need other method to solve the problem in the future. 0 100 200 300 400 500 600 700 2016/10/1 2016/11/1 2016/12/1 2017/1/1 2017/2/1 2017/3/1 Score of positive

22. Result The sentiment score of news per week • Almost all news are positive while only a little is negative. • We analyze a few negative case and find that it is usually short and about destroying the bicycles. -16 -14 -12 -10 -8 -6 -4 -2 0 2016/10/1 2016/11/1 2016/12/1 2017/1/1 2017/2/1 2017/3/1 Score of negative

23. Further work • Start collecting GPS data to create long-term historical data and understand frequency of usage vs time of day vs day of the week. • Create a cluster map so users know the nearest location of either a type 1 or 2 Mobike 23

24. Limitations • How we were limited? – We only had GPS data for one brand – The newspaper data was very noisy and overly positive tones – Zhihu contact got back to us too late and we didn’t have enough time to evaluate data • What did we learn? – How to work in a group with cross-cultural and language differences – What data is worth manipulating to tell a story and which to ignore • What we would do differently? – Create a Web App to make the work accessible and more user friendly 24

Shared Bicycle Scheme Datathon Brief

Shared Bicycle Scheme Datathon Brief

Recommended

More Related Content

Similar to Shared Bicycle Scheme Datathon Brief

Similar to Shared Bicycle Scheme Datathon Brief(20)

Recently uploaded

Recently uploaded(20)

Shared Bicycle Scheme Datathon Brief

Editor's Notes