Colorado Zip Codes with 90% or more Occupancy

I like zip code data. I know that my research mentors scoff at a break-up of the landscape that isn’t based on a system designed for geographic analysis (as SPLSS and other grids are designed for accuracy in spatial statistical techniques reliant on interpolation).

Why I like it is because it is a spatial pattern that is designed by density collections of people – geographic space defined by population as a tangible phenomena. Especially when contrasting it with demographic, market, and other human activity based datasets.

In studies where gathering personal information prohibits address based data, zip codes are the highest level of non-invasive location capture. Why the retailer inquires on your zip at point of sale.

So that’s what happens with all that purchasing data we provide by making non-cash purchases. Target Marketing based off data from Market Profiling.

ESRI Zip Tapestry

You have to admit that market profiling is really an interesting analysis. Especially when you see that it’s kind of true.

David Brooks writes in his book On Paradise Drive: How We Live Now (And Always Have) in the Future Tense – defining social classes by their purchases: on suburbia, the hilarious examination of the ‘Grill-buying Guy’: “Once inside the megastore, he adopts the stride American men fall into when in the presence of large amounts of lumber.”

Why are these obscure population dynamic descriptions accurate? Because they are based on zip codes – spatial data designed to reflect human patterns.

In this zip code example you can see the booming phenomena of population in Colorado. As a higher density urban area, constrained by urban growth boundaries, transportation infrastructure and quality of life in centralized areas, Denver has high rental and occupancy rates in its urban areas.

From this data you can see the patterning of urban areas across the landscape, with density being defined as rental occupancy over 90% distributed by zip code.

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Margaret Spyker

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