Even then you have to go through the US Census FTP website, download geographic files state by state, and join those files to census measures.įinally, most people know, without looking at a map that zip codes represent a smaller area than a city, but larger than say a neighborhood. Terms like Census Block Group or Tract are less familiar to those who don’t work with geospatial data on a regular basis, and they can be more difficult to find and harder to work with, especially if you aren’t familiar with terms like Shapefile, FTP, and ETL. You can see the complete methodology and code in this notebook. To do that we can us PostGIS to find those intersections as well as create different statistical measures from that data (min, max, and percentiles). Once we had both sets of boundaries, we want to look at all block groups that are either completely contained by a ZCTA boundary, or at least 50% contained by a ZCTA boundary. To perform this analysis for the entire United States, I used CARTO and it’s notebook extension CARTOframes to pull in census data for Census Block Groups and Census ZCTA areas, which are stored in CARTO. * similar where the difference is greater than 0 The most similar* zip code is in Chesapeake, WV
The most unequal zip code is 33139 in Miami Beach, FL Sticking with median household income, we decided to expand this analysis to the entire United States, to see which areas are the least and most in-equal when you look at ZIP Codes and the Census Block Groups that intersect with the ZCTA Boundaries. Median Income is one way to evaluate the range of values within a ZIP Code (keep in mind these are ZCTA boundaries) but we can likely see variance like this in population, employment, and other relevant metrics for data analysis. What we can see is that 12 month median household income in this single zip code (75206) ranges from $9,700 to $227,000 when we look at block groups that completely or partially fall within this single ZIP Code, which the Census lists as having a median household income of $63,392. Let’s look at an example of this in one specific area in Dallas. The second is that spatial data is provided at multiple scales, and many times those boundaries are overlapping or nested within another boundary. Their behavior is influenced much more by their neighbors, or areas such as a neighborhoods or high activity areas (such as central business districts). The first is that humans don’t behave based on administrative units such as zip codes, or even census units. The later represents two specific issues in using spatial data: spatial scale of observations and spatial scale support (you can learn more about this in this lecture from UChicago’s Luc Anselin, here).
#Us zip code boundaries how to
They are deciding how to use data tied to those zip codes to understand trends, run their businesses, and find new ways to reach you, using that same five-digit code.Įven though there are different place associations that probably mean more to you as an individual, such as a neighborhood, street, or the block you live on, the zip code is, in many organizations, the geographic unit of choice. However, lots of companies, marketers, and data analysts spend hours looking at zip codes. You likely found the answer you were looking for and didn’t stop to think further about that five-digit code you’d just typed out. The last time you used your zip code, you were most likely entering your address into a website to make a purchase, finding a store near your home or office, or filling out some other online form.