Craig Ulmer

Where Do Plane Owners Live?

2016-04-29 planes code

In addition to being an interesting source of data for plane statistics, the FAA registration dataset also provides address information for each plane's owner. I was curious to see who owned airplanes in my town (not just the drones), so I wrote a simple script to extract addresses in my zipcode from the database and convert them to geospatial coordinates. Below is a plot of all the registered plane owners for Livermore. I've also outlined different neighborhoods in town and colored them by how expensive their houses are. Unsurprisingly, people that own planes tend to live in wealthier neighborhoods.


Livermore has a busy municipal airport on the north-west side of town, with an east-west landing strip. Planes typically approach the airport by flying west over the city, using the railroad and I580 as visual guides to locate the airport. People that live east of the airport often complain about the noise of descending planes, but the airport was there long before the houses (it was built in 1965). In general, Livermore house prices increase the farther south you get. The cheap houses (where I live, in the yellow) start at about $500k. Down in Ruby Hill they're all well over $1M.

For the above plot, I shaded different parts of town based on how expensive their houses are: the darker green the color, the more wealthy the neighborhood. The shading wasn't very scientific- I just boxed up regions by hand and then looked up what Zillow said houses were going for in the neighborhood. Sadly, I found that my yellow-ish neighborhood had zero plane owners, which was consistent with other poorer neighborhoods. I think it's interesting that most of the plane owners live south of the landing path. I'm not sure if that's because that's where the more expensive houses usually are, or if plane owners are smart enough to know not to live long the flight path.

East Bay Owners

In addition to Livermore, I pulled out data on the neighboring areas (basically all of Alameda and Contra Costa counties). Below is a snapshot of it, but you can explore the data yourself in pannable Google map of the data.


The fun part of this project for me was learning how to do two new things. First, I had to figure out how to translate street addresses into geospatial coordinates. I found that the GeoPy library does all the hard work for you by submitting your queries to different web services that do this kind of translation. I initially queried to frequently and got my IP address temporarily blocked, so I added a three second delay between queries to be polite. The translations aren't perfect (the FAA data is fairly dirty), but they were good enough to handle the majority of my requests. Second, I needed a better way to plot points. Previously I've used Mapnik and Pylab to render maps, but they're tedious to script up properly. I hadn't tried Google's Maps API before, but I guessed it'd be easy since so many people use it. I signed up for my first API key, modified a Javascript example they provided, and it magically did everything I needed. I feel kind of silly for not messing with it sooner.

The script I used for extracting the data is, which I've put in my flight classifier repo. GeoPy needs a newer version of Python than what my CentOS 6 desktop had, so I had to build/install that as well.


Drone Registrations

2016-04-10 planes code

While looking planes up in the FAA dataset for the previous post, I noticed some planes had zero seats, weighed under 55 pounds, and were electric powered. Drones! (or more officially, sUAS - Small Unmanned Aircraft System). I knew that the FAA was making people register their drones, but I was surprised to see them showing up with other aircraft in the FAA database. After a little reading I learned that there are actually two ways to register: (1) online through a simple, instantaneous web page or (2) by mail using the traditional paper form process. While the by-mail approach takes a few weeks, your drone gets an N number and is plugged into the database. I wrote some python scripts to pull out electric plane registration info and plot it.

3,500 drone registrations is tiny compared to the web registration numbers (more than 300k in the first month). Still, it seems like a lot to me, given that I don't see an obvious reason to go through the by-mail process. In any case, I started filtering the data to see which organizations were registering. It wasn't that difficult, since the FAA database provides a registration type that identifies whether the owner is an individual, a corporation, or a government entity.

Commercial Drones

I first filtered on commercial entities, of which there were 940 different companies. Below is the complete list of companies with 10 or more drones. There are a few interesting stories here. First, Intel topped the charts with 111 drones. They seemed to all be the same ArsTec Hummingbird model, which (surprise) uses an Intel Atom Z530. BNSF Railway is using the drones to inspect rail lines (why not just strap a camera to a train?). Liberty Mutual says they're using them to assess insurance claims (eg natural disasters). San Diego Gas and Electric will do inspections of their service areas. Some companies do general "aerolytics", like this Talon Aerolytics video shows. Lockheed Martin manufactures their own drones. In addition to the electric models, their Missles and Fire Control group has a few drones under 55 pounds that use turbo-ject engines. There are also some mysteries in this list. Ashfloyd LLC has little outward info for a company with so many drones, causing some people to wonder who they are.

------  -------------------------------
 111    Intel Corp
  93    Precisionhawk Usa Inc
  43    Ashfloyd LLC
  40    Aerovironment Inc
  23    Rotor F/X LLC
  22    Lockheed Martin Corp
  18    San Diego Gas & Electric
  17    Unmanned Innovation Inc Dba
  16    Talon Aerolytics LLC
  15    Wintec Arrowmaker
  14    Flirtey Inc
  13    Trimble Navigation Ltd
  12    BNSF Railway UAS Program
  12    Precision Hawk Usa Inc
  12    Cape Productions Inc
  12    Microsoft Corp
  12    Aerodrome LLC
  11    Hazon Solutions LLC
  11    Liberty Mutual Insurance
  11    Unconventional Concepts Inc
  10    Aerocine Ventures Inc
  10    Amazon Logistics Inc

I was a little surprised Amazon didn't have more given Amazon Prime Air. They currently have 10 drones with tail fins, and have registered four different models they've developed. They've been adding to their inventory since last year, and appear to have more in the works if you check with the FAA. Here are the counts for the different models:

Model   Number Tailfins Currently Registered
MK9A    0
MK021A  2 Starting March 2015
MK23A   1 December 2015
MK24    7 Starting April 2015

Government Drones

Next, I selected on Government users, which yielded 310 organizations. They're not as exciting as people would things though- they're mostly state schools, NASA, fire departments, and law enforcement. I moved National Labs into their own category to include more schools in this list.

------  -------------------------------
  32    Kansas State University
  22    Oregon State University
  21    Nasa Langley Research Center
  16    University Of Colorado
  14    Nasa Ames Research Center
  12    Virginia Polytechnic Institute & State University
  12    Department Of Commerce
  11    University Of Maryland Uas Test Site
  11    Georgia Institute Of Technology
  11    Cochise Community College
  10    University Of Alaska Fairbanks
  10    University Of Michigan
   9    University Of North Dakota
   8    Department Of Energy
   8    Center For Disaster Risk Policy
   7    Mississippi State University
   7    Sinclair Community College
   7    Auburn University
   6    Ohio State University
   6    Utah State University
   4    Bureau Of Alcohol Tobacco Firearms & Explosives
   3    Alameda County Sheriffs Office

National Labs

I also pulled out national labs from the gov list. All of the drones I saw in this section were the same stuff consumers buy.

------  -------------------------------
  12    Sandia National Laboratories
   4    Battelle Pacific Northwest National Laboratory
   2    Los Alamos National Laboratory
   1    National Marine Mammal Laboratory
   1    Brookhaven National Laboratory
   1    Oak Ridge National Laboratory


MIT Lincoln Laboratory also popped up in the Aircraft Reference file (which defines airplane types), but does not show up as an owner of a registered plane in the master list. Searching for the drone's manufacturer model number in the master list turned up 9 hits, though all of them had their blank fields for the owner. There are many blanked owner fields in the dataset, so this may just be part of the registration process and not obfuscation.

The drone's name is Locust, which appears to be a micro-UAV developed by students in MIT's Beaverworks program, commissioned by LL and the USAF back in 2010. Some former students mentioned working on LOCUSTS/PERDIX in their LinkedIn pages, and that they'd designed micro-uavs that could be deployed at 30,000ft from a "cartridge mounted on a business jet". I don't know if it's releated or not, but the Office of Naval Research has a video of their LOCUST (low-cost uav swarm technology). Didn't these people watch Terminator?


The above plots and data were generated with and, which I've put in my flight-classifier repo.


Post-Superbowl Flight Data

2016-03-13 planes code

Earlier this year the local Livermore paper had some articles about how air traffic at our municipal airport was going to shoot up during the Superbowl, because there weren't going to be enough places for private jets to land in the Bay Area. I didn't think much of it at the time, since the paper tends to have delusions about how rich people will fly to Livermore and spend time here. However, after the Superbowl, my wife noticed on social media that several of her local friends were chatting about how there were a lot of jets taking off from the airport that night. I fired up dump1090 and let it grab for an hour before bed. After a bit of post-processing work, here's a timeline for all the flights I saw:

Post Processing

Dump1090 is a great program- in addition to displaying where planes are in a webpage, it produces easy-to-parse dump files that contain a good bit of plane information. I captured two types of traces from dump1090 after the Superbowl: the detailed runtime output with all the message info and the distilled, csv-formatted data from the netcat interface. The grabs went on for about an hour and yielded 30MB and 13MB of data, respectively. Looking at the data, I saw basically what I expected: there were a large number of private planes, but none of them were reporting position information. It drives me crazy that they clearly have ADS-B equipment but don't transmit position. Current regulations don't require it though, so nearly all private planes turn it off to prevent you from tracking their exact locations.

The dataset did leave me with a big pile of timestamped ADS-B IDs though, so I started looking for ways I could convert the IDs to something more interesting. I found that the FAA provides an extremely useful database you can download that contains full registration information for all US planes. The database is a collection of easily-parsed CSV files, and contains each plane's ADS-B hex code, tailfin, plane type, and owner information. The master DB files are currently close to 200MB uncompressed, but when I extracted just the ADS-B id and owner columns, it was only about 8MB (small enough for a quick lookup table use).

I used the FAA info to find the owners of the planes in my dataset, and then did some simple text processing to assign a classification to each plane to group similar owners together. Since I only had 98 planes to look at, I mostly did the classifications by hand. 36 of the planes were easy to classify because they were owned by commercial, passenger airline companies like Delta. Another 16 planes were owned by banks (fun fact: banks own more planes than any other type of company). Through some Google searches, I identified four private passenger carriers (e.g., Xojet) that took care of 7 more planes. I found 2 more planes owned by oil companies (Eaton and San Joaquin Refining) and 1 emergency helicopter (California Shock Trauma). I also found a plane owned by a gun store and another by a trucking company. There were 21 other planes in the FAA dataset that didn't turn much up in Google searches, that I marked as unclassified. That left me with 15 planes that weren't in the FAA dataset.

Foreign Planes

The FAA dataset only has info on US planes, so I figured the missing planes must all be foreign owned. I did some reading and learned that the hex IDs reported in ADS-B are from the International Civil Aviation Organization (ICAO), and that each country is assigned its own block of values in the address space. For example the US fits in A00000 to AFFFFF (which explains why I always see A's in my data), while Portugal is in 490000 to 497FFF. Annoyingly, I couldn't find an official table with all the country codes in it anywhere. I did find a website that had deduced the info and put it into a table. I grabbed it and did a lot of awking to put it into a lookup table my scripts could use. Here's where the 15 remaining planes were from, sorted by country:

C00738  22:29:12.396 22:50:48.658 Canada
C00964  22:30:02.529 22:40:50.856 Canada
C04852  22:57:57.379 23:16:09.175 Canada
C06E87  22:29:06.825 22:38:57.286 Canada
C08048  22:31:58.590 22:43:20.798 Canada
780A5B  23:16:51.247 23:21:39.138 China
780A70  22:29:07.154 22:29:44.770 China
780DA9  22:44:36.948 22:52:14.966 China
0D049E  23:22:46.770 23:25:08.781 Mexico
0D083B  22:55:46.443 23:02:04.836 Mexico
0C206B  22:44:08.180 22:50:31.290 Panama
52027A  23:10:56.513 23:19:52.647 (reserved, EUR/NAT)
899103  22:34:37.444 22:42:35.777 Taiwan
072233  22:29:13.247 22:32:35.813 unknown
A22E75  22:42:30.730 22:55:33.598 United States

The last plane there is a US plane, which should have been in the FAA database. FlightAware gave me the tail fin (N24JG), which the FAA told me had a December renewal rate. My guess is that the plane was just in-between renewals. In any case it was an interesting plane because it's owned by Jeff Gordon, Inc. Jeff Gordon is a race car driver, so I guess I did spot a celebrity. Neat.

Military/Surveillance Flights

The next unknown was 072233. I didn't find this registered anywhere, but Google searches turned the number up in lists where people monitor military plane activity. They reported this as 09-72233, which they say is a US Army UH-72A or EC45 helicopter (unarmed).

The final plane was 52027A, which caught my eye because it falls into a NATO band of the ICAO numbers (I believe). I looked it up in the raw dump1090 data and found that it also used the callsign IRONS12, which sounds like a tough-guy military callsign. I was hoping it might be the F15 that intercepted four planes during the superbowl (and escorted them to Livermore), but I think it's actually a surveillance plane. I found references to an IRONS12 callsign being used by an RC-26B with serial 920372 in the Bay Area the week before the superbowl (and leaving after). The RC-26B appears to be an Air National Guard plane with sensors for filming and tracking, and serves to "bridge the gap between Department of Defense and civil authorities". Now that I think about it, a surveillance plane is a lot more interesting than the F15s that the news covered.

Flight Times

The only other analysis I did on this data was look at how long planes were in the air (or otherwise chirping their ADS-B info). Given my antenna configuration that night, most planes were only visible for about 10-15 minutes. The emergency helicopter though operated for more than 20 minutes. I'd been hoping to see some private planes with long running times (a sign that they were sitting at the airport waiting for their owners to show up), but that didn't happen.


I've put the data and the scripts used to do these plots on github. The country code lookup table I made for this work is also in the repo.


Webcam Timelapses

2016-02-07 webcam

For fun, I went through some of the images I captured from my webcam scraper project and converted them into timelapse videos. The videos are all pretty repetitive. However, the videos did help me spot some nice one-offs, such as the Vancouver lightning strike I reported on previously. Here are some Youtube clips of the more interesting ones.

Rising Tides

The tides can be interested to watch in timelapses. Check out the rise and fall of boats in these webcams from Alaska and Hilton Head:


I spent a summer in Metz, France during college, so it was nice to see pictures of the city and Paris come up in the screensaver. One of the Metz webcams tracked an interesting building with curvy architecture being built. The Eiffel Tower cam is fairly constant, but if you stop it around Bastille day you can see some fireworks.

Around Europe

There were a few other places I grabbed from around Europe. The problem I had with getting data from there was that with the timezone differences, it was often night there when my desktop was running, resulting in night images. Here are timelapses from Warsaw, the Vatican, and Switzerland.

California Mountains

California also has some good cameras out in the mountains. Here are two from Mount Wilson (at the Mt. Wilson Observatory near L.A.) and Mount Shasta.

The Bay Area

The bay area has a few good cameras, besides the normal traffic cams. Here are cameras from downtown, Sausalito, and Berkeley.


I only found a few webcams for Atlanta, but the skyline camera always looked good to me. Georgia State used to have a really good, user-controlled camera with a strong zoom that let you look around the downtown streets and buildings. It was interesting to watch how other people controlled the camera. I often thought that if I watched it long enough I'd witness some downtown crime.


I was surprised to find that there are multiple webcams in Antarctica, and that they are well maintained. I don't seem to have it anymore, but on mother's day, someone left a sign in front of the webcam that said "I love you mom". The below timelapse is a little boring, but midway through it you see sea lions and penguins (I think) come up on shore.


Finally, here's my favorite camera, the one pointed at the Burrard Bridge in Vancouver. If you look around 34 seconds into it, you'll see the lightning strike I mentioned in the previous post.

Webcam Picks

2016-01-30 webcam

I had a chance to looked through my webcam dataset a little more and pick out a few interesting moments. First, my favorite site was a long-running webcam in Vancouver called KatKam, which shows the Burrard Bridge and the English Bay. I've never been there before, but the colors of the water, sky, and bridge often looked pleasant. While flipping through its pictures, I noticed that one night the webcam caught a lightning strike in the distance:

Another favorite webcam for me is at the Mount Wilson Observatory, near LA. I visited the observatory one time when I lived in Pasadena and thought the massive 100-inch Hooker Telescope was really amazing. Their webcam captures a few different views of the surrounding area, including the mountains, the observatories, and LA. However, back in September of 2009 it monitored a major forest fire as it marched over the hills. It must have been terrifying to watch it move from crest to crest, getting closer every day:

The Eiffel Tower was also another good one. There were at least three different cameras pointed at it, so if a supervillain ever did make a ray gun to steal it, it would be well documented. The Eiffel Tower was also one of the few places in Europe that I continued grabbing after dark, as they often had interesting light displays on it and around it during the holidays. Bastille day was a good day to check it out- I even caught some fireworks around it in 2009: