Antenna TV Channels by Zip Code, City & DMA. Local broadcasters transmit Free TV channels over-the-air in 210 major cities across the United States, these broadcasts are sent over the airwaves for free and are intended for customers who live within a designated market area otherwise known as a 'DMA'. Jan 08, 2006 L005 3-Digit ZIP Code Prefix Groups—SCF Sortation 12-8-05 8-4-05 4-14-05 L005 describes the service area by individual 3-digit ZIP Code prefix for mail destined to a sectional center facility (SCF). Subject to the standards for the rate claimed, pieces for the 3-digit ZIP Code prefixes shown in Column A must be combined and labeled to the corresponding SCF destination shown in. Zip Codes - ZipCodes Metadata Updated: August 19, 2017. Access & Use Information. Downloadable File Geodatabase ZIP 994 views ZipCodesgdb.zip Link is broken. Has a real estate brokerage license in multiple states. A list of our real estate licenses is available here. TREC: Information about brokerage services, Consumer protection notice California DRE #1522444. Additionally, the DMA program only processes U.S. Zip codes; thus, Canadian and other foreign travelers are excluded from this analysis. Nonresident data from 2010 is compared in many cases to the 2005 data from the nonresident study.
30 Dec 2015Here’s a common market research scenario: we need to find more about the people who live in the area where we’re launching a TV and/or radio campaign. Maybe we just want to know demographics. Eurocave comfort vieillitheque manual transfer. Or maybe we want to know what restaurants are nearby.
Dmas By Zip Code
Those TV/radio areas are called DMAs (Designated Market Areas) by Nielsen. In theory, people living inside one DMA are exposed to the same television and radio.
A useful first step to learning more about the people within a DMA of interest is identifying which postal/zip codes fall inside the DMA. Knowing which zip codes are inside a DMA will let us slice the right census data to learn more basic demographic information, for example.
So let’s pick a few DMAs and we’ll find the zip codes that fall inside them:
- Seattle (DMA 819)
- Phoenix (DMA 753)
- Houston (DMA 618)
- Atlanta (DMA 524)
Ver telenovela pedro el escamoso online gratis. Crucial to this approach is acquiring spatial data for both zip codes and Nielsen DMAs. To make the data easier to work with, I am going to convert all spatial data to GeoJSON.
Geo data about zip codes is available from census.gov as a shapefile. As I learned in exploring my options for this approach, no geographical dataset can completely represent zip codes, since they’re designed as lists of addresses where one post office delivers mail and those addresses don’t always end up comprising a simple polygon on a map (more discussion about this here)
ogr2ogr can convert the shapefile provided by the US Census into a GeoJSON file (here’s a short tutorial):
ogr2ogr -f GeoJSON -lco COORDINATE_PRECISION=2 zip_codes.geojson [US CENSUS SHAPEFILE FILENAME].shp
Data about DMAs, since they’re from Nielsen, are probably available from them. In fact, specific data about which zip codes fall into DMAs of interest is also available from Nielsen, for a fee. But let’s assume in this research scenario either that we don’t have any money to spend on that or we want the flexibility to explore and visualize the data ourselves, so we want a dataset that’s free and gives us the shapes and locations of each DMA. I was able to track down a Topojson file on Github of each Nielsen DMA in the lower 48 states.
For the TopoJSON, there’s an easy online converter here. But since the file is a flavor of JSON already, it would also be easy to load without changing the format.
The Nielsen DMA TopoJSON file also contains metadata for each DMA, which is easier to reference if extracted and saved as a CSV. The metadata can be extracted using the following few lines of code:
The next step is finding out which zip code polygons are contained in or overlap with our DMAs of interest. There are lots of ways to do this, but I’m going to use a Python library called Shapely to examine overlaps.
Our two GeoJSON files can be loaded as dictionaries of Shapely objects using the below code, which uses their identifying properties (the zip code and the DMA number) found in the structure of the JSON as dictionary keys. This requires the python library geojson.
Dma Zones By Zip Code
A shapely method called ‘intersects’ can be used to identify the zip code shapes that intersect (are completely or partially contained within) the shapes of our Nielsen DMAs of interest. So for each shapely object representing a DMA, we can simply iterate through the zip code shapes to find those that intersect with it:
That’s it. We now have a list of US zip codes that overlap with or are contained in each of our four target Nielsen DMAs (the dictionary dma_lists). From here, we can get the right data from American FactFinder or other data sources to learn more about the people that live in the area where a marketing campaign is being launched.