Visualizing Data: Seattle Bike Routes
SEATTLE’S BIKING ECOLOGY VISUALIZED
Bicycling is a healthy and environmentally-friendly mode of transportation. With Seattle’s accelerated growth rate in its population and economy, the subsequent worsening of its single-vehicle traffic is in turn, inevitable. Thus, it is important to look at the viability and popularity of alternate forms of transportation to reduce traffic congestion. For this reason, we sought to look at a bike sharing dataset, and to visually explore variables that we believe may be relevant to stakeholder groups of varying levels. This may include the planners and builders in charge of the entire Seattle transportation environment to the cyclists themselves who ride through the city every day.
Our data is sourced from Pronto, Seattle’s public bicycle sharing service that operated from October 2014 until March 2017. It provides a convenient snapshot into how thousands of commuters and cyclists traveled around the city over two years. Its primary limitation is that the data was collected in the context of a now defunct cycle sharing company. Although Pronto is now defunct, the data was collected by the organization from their inventory of over 500 bikes and 54 bike stations throughout the Seattle metropolitan area from October 2014 until August 2016. However, since Pronto ceased its operations, other bike sharing services have since replaced the organization and continued to utilize Pronto’s dataset as a source of predictive modeling for their own operations.
Using the programming language and environment of R (see Data Processing section in the full report below) to preprocess the original dataset from Kaggle, we were able to create new variables that were integral to the development process of our final visualization. After processing the dataset, we moved onto the iterative design process in which we played around with the many subsequent variables from the dataset to create various data visualizations intended for different applications and target audiences. By using a both processes of traditional hand-drawn sketching and producing RStudio statistical graphics, we were afforded the ability to experiment and create many different visual compositions from the large dataset.
After playing with the juxtaposition of various visual compositions, we ultimately decided that a radial chart could best depict Seattle’s biking ecology in an effective way so that it can help our target audience make decisions concerning their everyday commutes and/or riding habits. A radial graph also has the possibility of showing the relational data, which is imperative when designing for our target audience. Throughout the design process, the visual principle of selectivity had to be prioritized as we recognized that our large dataset, comprised of over 50,000+ data points, would need to be visualized in a compact space.
After the initial prototype of the visualization was developed, we then conducted user testings and evaluations to increase our understanding and knowledge of how people interacted and perceived the visualization. Multiple user testing methodologies were taken in this evaluation process to test the visualization’s efficacy in its visual information encoding, as well as all its relational visual components. From the feedback and information collected from the user testing process, we recognized that some improvements and changes had to be made. Whether the change was focused on the improving the visual salience so “reading” the visualization would not be so straining, or rebalancing the qualitative information and the quantitative data being encoded so that the user would not need prior knowledge to understand the visual, we were ready to revise and improve the visualization to any extent necessary.
In the end, we chose to use a variation of the chord diagram (after some revisions and reconfigurations of the data model) as the final visualization for this design project. We recognize that although our users may all have varying motivations in why they are using our visualization, they will be using it to understand Seattle’s biking environment. By depicting multiple relevant variables to our users with this visualization, our target audiences can use this graph to understand a more holistic story from the data that can be both applicable to them on an individual level as well as a broad scale.
This chord diagram represents over 50 thousand bike sharing trips in the greater city of Seattle by users of the bike sharing service Pronto from 2014 to 2016. Each colored section on the circumference of the circle represents 12 different neighborhoods from the dataset. The size of each section represents the total number of trips to and from that neighborhood by bikers. Each chord within the circle represents the bike trips to and from different Seattle neighborhoods. Seattle cyclists and riders will be able to use this visualization to decode Seattle’s biking ecology to aid in their daily commute and cycling habits.
For a more details on this project, please use the following link to download a copy of the report:
The data used to generate this visualization was sourced originally from Proto’s Bike Sharing dataset, which was retrieved from Kaggle.com.