The use of bicycles in Helsinki

June 15, 2017
By

(This article was first published on Data science – Data, Economics and Rant, and kindly contributed to R-bloggers)

Every six months, the city of Helsinki publishes data on the number of cyclists in Helsinki per hour. Since 2014, a number of meters have been planted in different areas of Helsinki (Kaisaniemi, Munkkiniemi 1 and 2, Hesperian, Eteläesplanadi and Baana) to count the number of bikes. The latest data is freely available here.

I amalgamated this data set with data on the weather in Helsinki for each day to plot some graphics and create some analysis on the factors that determine the use of bicycles in Helsinki.

1. Data visualization

I found some very good models for the data visualization on Kaggle that I mostly use here.

fig1

The figure 1 shows the average number of bikes used in Helsinki per hour based on the day. Most of the cyclists seem to use the bike on weekdays to commute. We see two peaks in use, one at 8 in the morning and the second at 4pm.
It is noticeable that the motivation to ride a bike to go to work significantly decreases during the week. Therefore, there is approximatively 40% less bikes used on Friday at 8h than on Tuesday at the same time.
On Saturday and Sunday, people use bicycles in the afternoon but we don’t see the same level of use than on a weekday.

fig2

The figure 2 displays the number of bikes per day from 2014 to March 2017 and by temperature. We see a similar trend in the use of bikes in the different years. To no surprise, the maximum number of cyclists per day (more than 20 000 per day) is higher during the summer when the temperatures are high whereas when the temperature is lower than 0, the number of cyclists is usually lower (less than 10 000 a day).

fig3

Figure 3 shows the average number of bikes per day by month. The number of bicycles is significant in May 2016 compared to the other years. This might be partly explained by the introduction of a bicycle-sharing system in the city centre of Helsinki in spring 2016. Therefore, it might encourage people living in Helsinki but also tourists to use these bikes.

2. Insights on the factors that determine the use of bicycles

fig4

The correlation matrix shows the positive (blue) and negative (red) correlations between the number of bikes used (variable total4) and weather variables such as the temperature (variable temp), the morning dew (variable rosee), the humidity (humid), the atmospheric pressure (press), the visibility (visi), the wind (wind) and the accumulated precipitations (preci) on a day (preci).

The number of cyclists per day is highly and positively correlated with the temperature and the morning dew. In other words, when the temperature is high, people use more bicycles. On the other hand, humidity is highly and negatively correlated with the number of cyclist. The accumulated precipitations and the wind are not significantly correlated.

To leave a comment for the author, please follow the link and comment on their blog: Data science – Data, Economics and Rant.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)