DS4UX (Spring 2016)/Seattle traffic: Difference between revisions
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== Code and data == | == Code and data == | ||
<font size="+1">[http:// | <font size="+1">[http://jtmorgan.net/ds4ux/week4/bgt-traffic.zip Click here to download today's project]</font> | ||
This .zip archive contains the basic code for the project, and the datasets we'll be using. | 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 === | === Data files used in this project === | ||
*[https://data.seattle.gov/Transportation/MTS-Trail-west-of-I-90-Bridge/u38e-ybnc Mountain-to-Sounds trail bike and pedestrian traffic counts] | *[https://data.seattle.gov/Transportation/MTS-Trail-west-of-I-90-Bridge/u38e-ybnc Mountain-to-Sounds trail bike and pedestrian traffic counts] — this is our primary dataset, which we'll be using for all in class exercises and [[DS4UX_(Spring_2016)/Day_4_coding_challenge|coding challenges]]. | ||
*[https://data.seattle.gov/Transportation/Burke-Gilman-Trail-north-of-NE-70th-St-Bike-and-Pe/2z5v-ecg8 Burke-Gilman trail bike and pedestrian traffic counts] | *[https://data.seattle.gov/Transportation/Burke-Gilman-Trail-north-of-NE-70th-St-Bike-and-Pe/2z5v-ecg8 Burke-Gilman trail bike and pedestrian traffic counts] — this is a 'bonus' dataset which you will need to complete the 'bonus' coding challenges. | ||
== Ideas for analysis == | == Ideas for analysis == | ||
Revision as of 04:08, 18 April 2016
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.
Goals
- 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
Click here to download today's project
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
- Mountain-to-Sounds trail bike and pedestrian traffic counts — this is our primary dataset, which we'll be using for all in class exercises and coding challenges.
- Burke-Gilman 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 bike_and_ped_idea[1-3].py
- 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?
Coding challenges
The Week 4 coding challenges are based on this project. Please note that Challenges 1-3 are required.
Related resources
- Other interesting datasets from Data.Seattle.Gov
- Other Socrata sites with open datasets you can download
