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Impact of Spending on Traffic Congestion

We schedule our day around it. We design our cities around it. We give so much of our lives to it. To many Americans, traffic is all of the above and more. We hate traffic even as it seems to grow worse every passing year.

But how do we fare compared to Americans across the nation? Is my commute the worst in the nation? Is it getting better or worse? Where is the government improving it? These are all complicated questions that can be answered with freely public data.

Data on commute times is collected by the Census Bureau's American Community Survey each year, and we'll access this data via Data USA's convenient API. Spending data about transportation projects is provided by the Treasury Department on USASpending.gov.

The resulting mashup can enable us to see the extent to which government spending through formula grants and related programs from the Department of Transportation aligns with high-congestion regions.

The representatives at Deloitte and Datawheel have provided a Data Usability tutorial making use of Data USA's API to build a dataviz to explore these questions.

In this data visualization, you can compare against the best and worst commutes in the nation, East Stroudsurg, PA and Pierre, SD respectively. Are you beating the national average or is there government spending near your daily commute? We can examine all these in the widget below.

Chart showing average daily one-way commute times (in minutes) for Pierre, SD - Harrisonburg, VA - East Stroudsburg, PA Metro Area - and the National Average.

In this Commerce Data Usability Tutorial, the representatives at Deloitte and Datawheel walk you through all the steps of replicating the data-visualization which mashes up statistics on average commuting times with data on congestion relief projects funded by the Department of Transportation. You'll learn how to load the required software to build the mashup, how to use basic shell scripting commands to make a directory structure where your visualization app can live, how to pull the code for the app from the open-source code repository on GitHub, and how to run a simple web server.

Written by Star Ying, Commerce Data Service and Peter Viechnicki, Deloitte

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