GPSJam; Mapping a World of Cities; Mapping Wind Data With R

It’s #300DayMapChallenge month! You can follow along with my entries (NOTE: it is unlikely there will be one for each challenge day) over at a new subdomain I set up.

We’ll take a clue from the start of the challenge and talk a bit about maps today.

TL;DR

This is an AI-generated summary of today’s Drop.

1. The first section discusses the use of ADS-B Exchange data in generating maps on GPSJam. The maps represent likely GPS interference based on aircraft reports of their navigation system accuracy. The correlation between areas of low accuracy and known or suspected GPS jamming activity is highlighted, with the most common cause of degraded GPS accuracy believed to be military jamming in conflict zones.

2. The second section explores the historical and geographical reasons for the development and growth of cities. It introduces Mapping a World of Cities, a digital collaboration between ten map libraries and collections in the United States. The project uses maps to show how world cities have changed over time, reflecting shifts in ethnicity, economy, systems of power and oppression, and areas of grandeur and decay.

3. The third section features a tutorial by Milos Popovic on how to map wind data using R. The tutorial, available on Milos’s blog, is comprehensive and includes a corresponding GitHub repo. The tutorial guides users on how to download, reshape, and visualize wind data in the form of streamlines using R.

GPSJam

ADS-B Exchange is “the world’s largest community of unfiltered ADS-B/Mode S/MLAT feeders, providing enthusiasts, researchers, and journalists access to the globe’s most extensive flight data for unprecedented flight monitoring”.

The alphabet soup in that sentence needs some ‘splainin.

Automatic Dependent Surveillance–Broadcast (ADS-B), Mode S, and Multilateration (MLAT) are all technologies used in aviation for surveillance and communication.

ADS-B is a surveillance technology in which an aircraft determines its position via satellite navigation or other sensors and periodically broadcasts it. This enables the aircraft to be tracked without the need for an interrogation signal from the ground. The information can be received by air traffic control ground stations and other aircraft, providing situational awareness and allowing self-separation. ADS-B is automatic and dependent on data from the aircraft’s navigation system.

Mode S (Select) is a Secondary Surveillance Radar process that allows selective interrogation of aircraft according to a unique 24-bit address assigned to each aircraft. It is designed to avoid overinterrogation of the transponder and to allow automatic collision avoidance. Mode S transponders are compatible with Mode A and Mode C Secondary Surveillance Radar (SSR) systems. Upon interrogation, Mode S transponders transmit information about the aircraft to the SSR system, to Traffic Collision Avoidance System (TCAS) receivers on board aircraft, and to the ADS-B SSR system.

Multilateration (MLAT) is a technology that has been in use for many decades in both navigation and surveillance applications. It is based on the principle of determining the difference in distance to two stations located at known coordinates. MLAT can be used for both ground (aerodrome) and air traffic surveillance. In response to an interrogation signal from one of the MLAT sensors, the vehicle or aircraft transponder will transmit a reply that will be received and processed by all of the MLAT sites. The variance of Time Difference of Arrival (TDOA) at the various ground sites will allow accurate determination of the vehicle or aircraft position.

John Wiseman uses ADS-B Exchange data to generate maps on GPSJam of likely GPS interference, based on aircraft reports of their navigation system accuracy. The formula used for each hex takes into account the number of aircraft reporting good and bad accuracy.Essentially, the map is asserting that there’s a correlation between areas of low accuracy and known or suspected GPS jamming activity. The most common cause of degraded GPS accuracy is believed to be military jamming in conflict zones.

John uses hexagons to represent those different levels of accuracy. Green hexagons indicate good accuracy, yellow hexagons indicate low accuracy reported by 2-10% of aircraft, and red hexagons indicate low accuracy reported by more than 10% of aircraft.

The data on the map is limited to dates after February 14, 2022, and some other dates may have incomplete data.

The section header image is a GPSJam from October 30th.

Mapping a World of Cities

Cities pop up and grow for many reasons, and maps can help us understand why. Often, it’s because of geographical features like a source of fresh water or a good harbor. Occasionally, it’s due to historical events like a royal decree or a land claim. From the earliest cities, people living close together have shaped their surroundings to meet their needs, whether economic, cultural, religious, or military.

As nations, empires, and trade networks grew, cities became interconnected. They weren’t just linked to other cities, but also to the rural areas around them.

Maps are more than just pictures of a city at a certain point in time. They’re also tools for planning and debating the shape of a city. They show us how cities have changed over time, reflecting shifts in ethnicity and economy, systems of power and oppression, and areas of grandeur and decay. Furthermore, they capture the good times and the bad, growth and decline, creation and destruction.

People who live in cities often feel a strong sense of pride in their city. This shared sense of place is rooted in the city’s history. Maps help us understand this history, telling the stories of a city’s past and present, and maybe even its future.

Mapping a World of Cities is “a digital collaboration between ten map libraries and collections in the United States. Covering four centuries, these maps show how world cities changed alongside the changing art and science of cartography. Explore the maps and images, and click through to the host institutions’ pages for more collections”.

The section header is a woodblock plan of the Culhua-Mexica city of Tenochtitlán, as the Spanish saw it, attributed to the artist Albrecht Dürer, is the earliest known map of a city in the Americas.

It’s a super fun site to linger over. All the maps are gorgeous and informative.

Mapping Wind Data With R

Milos Popovic caught a glimpse of Windy’s maps and got bitten by a bug I know all too well: “How can I make this in R?”.

The post is comprehensive, and has a corresponding GitHub repo.

I’ll leave you to explore, since Milos did a fantastic job on the post, so there’s no need for me to blather more.

FIN

Time to go buy a metric ton of discounted Halloween candy! ☮️