DS4UX (Spring 2016)/Seattle traffic
Daily bicycle and pedestrian traffic patterns on the Burke-Gilman trail
In this project, we will explore data gathered from data.seattle.gov, an open data repository that contains a variety of interesting civic datasets. The dataset we are working with today is a count of traffic per hour on the Burke-Gilman trail at NE 70th street, broken down by bicycle and pedestrian traffic.
- Gain experience importing, parsing, and exporting large flat datafiles into Python
- Become more comfortable working with data stored in Python dictionaries — one of the most common (and powerful!) data structures for working with large research datasets.
- Practice reading and extending other people's code
- Start to think up ideas of what kind of analysis you might perform with data like this for your final project.
Code and data
This .zip archive contains the basic code for the project, and the datasets we'll be using. Save it to your desktop (or whatever directory you're using to store DS4UX code) and uncompress the folder.
Data files used in this project
You don't need to download these separately—they're already included in your .zip file.
- Burke-Gilman trail bike and pedestrian traffic counts — this is our primary dataset, which we'll be using for all in class exercises and coding challenges.
- Mountain-to-Sounds trail bike and pedestrian traffic counts — this is a 'bonus' dataset which you will need to complete the 'bonus' coding challenges.
Ideas for analysis
We'll go over these in class. The solutions are included in your
bgt-traffic.zip as the files
- How many people walked northbound on the BGT between 6 and 7 AM on August 28, 2014?
- How many people walked or rode northbound during April 1, 2015?
- Which hour during November 11, 2015 saw the most overall traffic?
The Week 4 coding challenges are based on this project. Please note that Challenges 1-3 are required.
- Other interesting datasets from Data.Seattle.Gov
- Other Socrata sites with open datasets you can download