Editing Seattle open data (section)

== Analyzing traffic on the Burke-Gilman trail ==
[[File:Bgt_bikes_and_peds_2019.png|thumb|right|250px|In this session we'll learn how to analyze and transform data about traffic on the Burke-Gilman trail over time, and create useful timeseries visualizations like this one!]]
We will spend the first part of the session today walking through the included notebook <tt>Burke-Gilman_commuter_traffic.ipynb</tt>. We will be reproducing this notebook section by section, coding as we go, until we culminate in exporting a CSV file that can be used to build the timeseries visualization above.

After that, you'll have time to explore next steps on your own, either tackling the "Challenge questions" below, exploring the capabilities of the SODA API, or asking your own research questions with any of the other datasets on data.seattle.gov!

=== Research questions we will answer in this session ===
# How many people used the Burke Gilman during commute hours in 2019?
# What were the busiest hours on the Burke Gilman in 2019?
# What are the busiest hours for bikes vs pedestrians?
# What are the busiest hours for bikes vs. peds AND northbound vs. southbound?

=== Challenge questions to apply what you've learned ===
''These are questions you now have the basic tools to answer using the BGT dataset (potentially in combination with other open datasets listed below):''
# What day of the week is busiest on the Burke Gilman?
# What day of the week is busiest for bikes? Is it the same as the busiest day for pedestrians?
# What month of the year is busiest? (aka do Seattlites really like to ride in the rain?)
# Has the Burke Gilman gotten busier over time? (the dataset we have goes back to 2014!)
# Do fewer people commute on the Burke Gilman when it's cold out? (hint: try combining this dataset with the dataset on road temperature over time!)
# Do more people commute into Seattle in the mornings by bike on the Burke Gilman, or on the the Mountain to Sound Trail?