Your spatial data might be lying to you. Zip code is the most common piece of geo-data analysts and data scientists see, but it has many quirks that can derail your analysis and lead to false conclusions. We'll look at the zip code and learn exactly what it is - and what it isn't. To do this we'll take a look at how a piece of mail get from point A to point B and take very quick trip though the history of the U.S. postal system before looking at other data you can collect that may be more appropriate for spatial purposes than zip code. Then we'll turn our attention to other ways seemingly good spatial data can lie to you: trap streets and paper towns.
Peter Lenz is a geographer and data scientist with Dstillery, a data insights company that builds behavioral profiles from billions of observations of human behavior every day using machine learning. Specializing in geographic big data and data storytelling, Peter can dive anywhere into the data pipeline from writing code, training models, to discovering insights in the data. He built up skill set working in the urban planning world, first at a consulting firm and later with an agency of the Department of the Interior. A native of New York City, today he and his family live in the Hudson Valley.
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