Turning old maps into 3D digital models of lost neighborhoods

Imagine strapping on a virtual reality headset and "walking" through a long-gone neighborhood in your city -- seeing the streets and buildings as they appeared decades ago.

That's a very real possibility now that researchers have developed a method to create 3D digital models of historic neighborhoods using machine learning and historic Sanborn Fire Insurance maps.

But the digital models will be more than just a novelty -- they will give researchers a resource to conduct studies that would have been nearly impossible before, such as estimating the economic loss caused by the demolition of historic neighborhoods.

"The story here is we now have the ability to unlock the wealth of data that is embedded in these Sanborn fire atlases," said Harvey Miller, co-author of the study and professor of geography at The Ohio State University.

"It enables a whole new approach to urban historical research that we could never have imagined before machine learning. It is a game changer."

The study was published today (June 28, 2023) in the journal PLOS ONE.

This research begins with the Sanborn maps, which were created to allow fire insurance companies to assess their liability in about 12,000 cities and towns in the United States during the 19th and 20th centuries. In larger cities, they were often updated regularly, said Miller, who is director of Ohio State's Center for Urban and Regional Analysis (CURA).

The problem for researchers was that trying to manually collect usable data from these maps was tedious and time-consuming -- at least until the maps were digitized. Digital versions are now available from the Library of Congress.

Study co-author Yue Lin, a doctoral student in geography at Ohio State, developed machine learning tools that can extract details about individual buildings from the maps, including their locations and footprints, the number of floors, their construction materials and their primary use, such as dwelling or business.

"We are able to get a very good idea of what the buildings look like from data we get from the Sanborn maps," Lin said.

The researchers tested their machine learning technique on two adjacent neighborhoods on the near east side of Columbus, Ohio, that were largely destroyed in the 1960s to make way for the construction of I-70.

One of the neighborhoods, Hanford Village, was developed in 1946 to house returning Black veterans of World War II.

"The GI bill gave returning veterans funds to purchase homes, but they could only be used on new builds," said study co-author Gerika Logan, outreach coordinator of CURA. "So most of the homes were lost to the highway not long after they were built."

The other neighborhood in the study was Driving Park, which also housed a thriving Black community until I-70 split it in two.

The researchers used 13 Sanborn maps for the two neighborhoods produced in 1961, just before I-70 was built. Machine learning techniques were able to extract the data from the maps and create digital models.

Comparing data from the Sanford maps to today showed that a total of 380 buildings were demolished in the two neighborhoods for the highway, including 286 houses, 86 garages, five apartments and three stores.

Analysis of the results showed that the machine learning model was very accurate in recreating the information contained in the maps -- about 90% accurate for building footprints and construction materials.

"The accuracy was impressive. We can actually get a visual sense of what these neighborhoods looked like that wouldn't be possible in any other way," Miller said.

"We want to get to the point in this project where we can give people virtual reality headsets and let them walk down the street as it was in 1960 or 1940 or perhaps even 1881."

Using the machine learning techniques developed for this study, researchers could develop similar 3D models for nearly any of the 12,000 cities and towns that have Sanborn maps, Miller said.

This will allow researchers to re-create neighborhoods lost to natural disasters like floods, as well as urban renewal, depopulation and other types of change.

Because the Sanborn maps include information on businesses that occupied specific buildings, researchers could re-create digital neighborhoods to determine the economic impact of losing them to urban renewal or other factors. Another possibility would be to study how replacing homes with highways that absorbed the sun's heat affected the urban heat island effect.

"There's a lot of different types of research that can be done. This will be a tremendous resource for urban historians and a variety of other researchers," Miller said.

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Can children map read at the age of four?

Children start to develop the basic skills that underlie map reading from the age of four -- according to new research from the University of East Anglia.

A new study published today reveals that they become able to use a scale model to find things in the real world.

The study involved 175 two to five-year-olds and is the largest of its kind.

The team say that this new spatial ability potentially lays the foundations for maths and science skills.

Lead researcher Dr Martin Doherty, from UEA's School of Psychology, said: "We wanted to find out when children can use scale models or maps to learn things about the world.

"So we played a hiding game with 175 children aged between two and five years old. We showed them a sticker hidden in a model of a room, and then they had to look for another sticker in the 'same place' in another model of the room.

"The two and three-year-olds were not able to recognise that the spatial arrangements in the model rooms were the same. But from about four years old, they were able to use one model room as a guide to finding the object in the other.

"This means that children start to develop the basic skills that underly map reading from the age of four.

"Based on these findings we predict children can read simple maps from around the age of four. Extending our methods to maps would help resolve a controversial developmental question," he added.

The study resolves a debate about whether understanding models is a representational ability or a spatial one.

Previous research had claimed that understanding models showed an understanding of representation. But the UEA team found that it is about understanding spatial layout, and that complex concepts like representation were not involved.

"This tells us that map-reading may be cognitively simpler than previously thought," added Dr Doherty.

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Cosmic cartographers map nearby universe revealing the diversity of star-forming galaxies

A team of astronomers using the Atacama Large Millimeter/submillimeter Array (ALMA) has completed the first census of molecular clouds in the nearby Universe, revealing that contrary to previous scientific opinion, these stellar nurseries do not all look and act the same. In fact, they're as diverse as the people, homes, neighborhoods, and regions that make up our own world.

Stars are formed out of clouds of dust and gas called molecular clouds, or stellar nurseries. Each stellar nursery in the Universe can form thousands or even tens of thousands of new stars during its lifetime. Between 2013 and 2019, astronomers on the PHANGS -- Physics at High Angular Resolution in Nearby GalaxieS -- project conducted the first systematic survey of 100,000 stellar nurseries across 90 galaxies in the nearby Universe to get a better understanding of how they connect back to their parent galaxies.

"We used to think that all stellar nurseries across every galaxy must look more or less the same, but this survey has revealed that this is not the case, and stellar nurseries change from place to place," said Adam Leroy, Associate Professor of Astronomy at Ohio State University (OSU), and lead author of the paper presenting the PHANGS ALMA survey. "This is the first time that we have ever taken millimeter-wave images of many nearby galaxies that have the same sharpness and quality as optical pictures. And while optical pictures show us light from stars, these ground-breaking new images show us the molecular clouds that form those stars."

The scientists compared these changes to the way that people, houses, neighborhoods, and cities exhibit like-characteristics but change from region to region and country to country.

"To understand how stars form, we need to link the birth of a single star back to its place in the Universe. It's like linking a person to their home, neighborhood, city, and region. If a galaxy represents a city, then the neighborhood is the spiral arm, the house the star-forming unit, and nearby galaxies are neighboring cities in the region," said Eva Schinnerer, an astronomer at the Max Planck Institute for Astronomy (MPIA) and principal investigator for the PHANGS collaboration "These observations have taught us that the "neighborhood" has small but pronounced effects on where and how many stars are born."

To better understand star formation in different types of galaxies, the team observed similarities and differences in the molecular gas properties and star formation processes of galaxy disks, stellar bars, spiral arms, and galaxy centers. They confirmed that the location, or neighborhood, plays a critical role in star formation.

"By mapping different types of galaxies and the diverse range of environments that exist within galaxies, we are tracing the whole range of conditions under which star-forming clouds of gas live in the present-day Universe. This allows us to measure the impact that many different variables have on the way star formation happens," said Guillermo Blanc, an astronomer at the Carnegie Institution for Science, and a co-author on the paper.

"How stars form, and how their galaxy affects that process, are fundamental aspects of astrophysics," said Joseph Pesce, National Science Foundation's program officer for NRAO/ALMA. "The PHANGS project utilizes the exquisite observational power of the ALMA observatory and has provided remarkable insight into the story of star formation in a new and different way."

Annie Hughes, an astronomer at L'Institut de Recherche en Astrophysique et Planétologie (IRAP), added that this is the first time scientists have a snapshot of what star-forming clouds are really like across such a broad range of different galaxies. "We found that the properties of star-forming clouds depend on where they are located: clouds in the dense central regions of galaxies tend to be more massive, denser, and more turbulent than clouds that reside in the quiet outskirts of a galaxy. The lifecycle of clouds also depends on their environment. How fast a cloud forms stars and the process that ultimately destroys the cloud both seem to depend on where the cloud lives."

This is not the first time that stellar nurseries have been observed in other galaxies using ALMA, but nearly all previous studies focused on individual galaxies or part of one. Over a five-year period, PHANGS assembled a full view of the nearby population of galaxies. "The PHANGS project is a new form of cosmic cartography that allows us to see the diversity of galaxies in a new light, literally. We are finally seeing the diversity of star-forming gas across many galaxies and are able to understand how they are changing over time. It was impossible to make these detailed maps before ALMA," said Erik Rosolowsky, Associate Professor of Physics at the University of Alberta, and a co-author on the research. "This new atlas contains 90 of the best maps ever made that reveal where the next generation of stars is going to form."

For the team, the new atlas doesn't mean the end of the road. While the survey has answered questions about what and where, it has raised others. "This is the first time we have gotten a clear view of the population of stellar nurseries across the whole nearby Universe. In that sense, it's a big step towards understanding where we come from," said Leroy. "While we now know that stellar nurseries vary from place to place, we still do not know why or how these variations affect the stars and planets formed. These are questions that we hope to answer in the near future."

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Sharks use Earth's magnetic fields to guide them like a map

Sea turtles are known for relying on magnetic signatures to find their way across thousands of miles to the very beaches where they hatched. Now, researchers reporting in the journal Current Biology on May 6 have some of the first solid evidence that sharks also rely on magnetic fields for their long-distance forays across the sea.

"It had been unresolved how sharks managed to successfully navigate during migration to targeted locations," said Save Our Seas Foundation project leader Bryan Keller, also of Florida State University Coastal and Marine Laboratory. "This research supports the theory that they use the earth's magnetic field to help them find their way; it's nature's GPS."

Researchers had known that some species of sharks travel over long distances to reach very specific locations year after year. They also knew that sharks are sensitive to electromagnetic fields. As a result, scientists had long speculated that sharks were using magnetic fields to navigate. But the challenge was finding a way to test this in sharks.

"To be honest, I am surprised it worked," Keller said. "The reason this question has been withstanding for 50 years is because sharks are difficult to study."

Keller realized the needed studies would be easier to do in smaller sharks. They also needed a species known for returning each year to specific locations. He and his colleagues settled on bonnetheads (Sphyrna tiburo).

"The bonnethead returns to the same estuaries each year," Keller said. "This demonstrates that the sharks knows where 'home' is and can navigate back to it from a distant location."

The question then was whether bonnetheads managed those return trips by relying on a magnetic map. To find out, the researchers used magnetic displacement experiments to test 20 juvenile, wild-caught bonnetheads. In their studies, they exposed sharks to magnetic conditions representing locations hundreds of kilometers away from where the sharks were actually caught. Such studies allow for straightforward predictions about how the sharks should subsequently orient themselves if they were indeed relying on magnetic cues.

If sharks derive positional information from the geomagnetic field, the researchers predicted northward orientation in the southern magnetic field and southward orientation in the northern magnetic field, as the sharks attempted to compensate for their perceived displacement. They predicted no orientation preference when sharks were exposed to the magnetic field that matched their capture site. And, it turned out, the sharks acted as they'd predicted when exposed to fields within their natural range.

The researchers suggest that this ability to navigate based on magnetic fields may also contribute to the population structure of sharks. The findings in bonnetheads also likely help to explain impressive feats by other shark species. For instance, one great white shark was documented to migrate between South Africa and Australia, returning to the same exact location the following year.

"How cool is it that a shark can swim 20,000 kilometers round trip in a three-dimensional ocean and get back to the same site?" Keller asked. "It really is mind blowing. In a world where people use GPS to navigate almost everywhere, this ability is truly remarkable."

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Astronomers release new all-sky map of Milky Way's outer reaches

Astronomers using data from NASA and ESA (European Space Agency) telescopes have released a new all-sky map of the outermost region of our galaxy. [Editor's note: See Related Multimedia link below.] Known as the galactic halo, this area lies outside the swirling spiral arms that form the Milky Way's recognizable central disk and is sparsely populated with stars. Though the halo may appear mostly empty, it is also predicted to contain a massive reservoir of dark matter, a mysterious and invisible substance thought to make up the bulk of all the mass in the universe.

The data for the new map comes from ESA's Gaia mission and NASA's Near Earth Object Wide Field Infrared Survey Explorer, or NEOWISE, which operated from 2009 to 2013 under the moniker WISE. The study makes use of data collected by the spacecraft between 2009 and 2018.

The new map reveals how a small galaxy called the Large Magellanic Cloud (LMC) -- so named because it is the larger of two dwarf galaxies orbiting the Milky Way -- has sailed through the Milky Way's galactic halo like a ship through water, its gravity creating a wake in the stars behind it. The LMC is located about 160,000 light-years from Earth and is less than one-quarter the mass of the Milky Way.

Though the inner portions of the halo have been mapped with a high level of accuracy, this is the first map to provide a similar picture of the halo's outer regions, where the wake is found -- about 200,000 light-years to 325,000 light-years from the galactic center. Previous studies have hinted at the wake's existence, but the all-sky map confirms its presence and offers a detailed view of its shape, size, and location.

This disturbance in the halo also provides astronomers with an opportunity to study something they can't observe directly: dark matter. While it doesn't emit, reflect, or absorb light, the gravitational influence of dark matter has been observed across the universe. It is thought to create a scaffolding on which galaxies are built, such that without it, galaxies would fly apart as they spin. Dark matter is estimated to be five times more common in the universe than all the matter that emits and/or interacts with light, from stars to planets to gas clouds.

Although there are multiple theories about the nature of dark matter, all of them indicate that it should be present in the Milky Way's halo. If that's the case, then as the LMC sails through this region, it should leave a wake in the dark matter as well. The wake observed in the new star map is thought to be the outline of this dark matter wake; the stars are like leaves on the surface of this invisible ocean, their position shifting with the dark matter.

The interaction between the dark matter and the Large Magellanic Cloud has big implications for our galaxy. As the LMC orbits the Milky Way, the dark matter's gravity drags on the LMC and slows it down. This will cause the dwarf galaxy's orbit to get smaller and smaller, until the galaxy finally collides with the Milky Way in about 2 billion years. These types of mergers might be a key driver in the growth of massive galaxies across the universe. In fact, astronomers think the Milky Way merged with another small galaxy about 10 billion years ago.

"This robbing of a smaller galaxy's energy is not only why the LMC is merging with the Milky Way, but also why all galaxy mergers happen," said Rohan Naidu, a doctoral student in astronomy at Harvard University and a co-author of the new paper. "The wake in our map is a really neat confirmation that our basic picture for how galaxies merge is on point!"

A Rare Opportunity

The authors of the paper also think the new map -- along with additional data and theoretical analyses -- may provide a test for different theories about the nature of dark matter, such as whether it consists of particles, like regular matter, and what the properties of those particles are.

"You can imagine that the wake behind a boat will be different if the boat is sailing through water or through honey," said Charlie Conroy, a professor at Harvard University and an astronomer at the Center for Astrophysics | Harvard & Smithsonian, who coauthored the study. "In this case, the properties of the wake are determined by which dark matter theory we apply."

Conroy led the team that mapped the positions of over 1,300 stars in the halo. The challenge arose in trying to measure the exact distance from Earth to a large portion of those stars: It's often impossible to figure out whether a star is faint and close by or bright and far away. The team used data from ESA's Gaia mission, which provides the location of many stars in the sky but cannot measure distances to the stars in the Milky Way's outer regions.

After identifying stars most likely located in the halo (because they were not obviously inside our galaxy or the LMC), the team looked for stars belonging to a class of giant stars with a specific light "signature" detectable by NEOWISE. Knowing the basic properties of the selected stars enabled the team to figure out their distance from Earth and create the new map. It charts a region starting about 200,000 light-years from the Milky Way's center, or about where the LMC's wake was predicted to begin, and extends about 125,000 light-years beyond that.

Conroy and his colleagues were inspired to hunt for LMC's wake after learning about a team of astrophysicists at the University of Arizona in Tucson that makes computer models predicting what dark matter in the galactic halo should look like. The two groups worked together on the new study.

One model by the Arizona team, included in the new study, predicted the general structure and specific location of the star wake revealed in the new map. Once the data had confirmed that the model was correct, the team could confirm what other investigations have also hinted at: that the LMC is likely on its first orbit around the Milky Way. If the smaller galaxy had already made multiple orbits, the shape and location of the wake would be significantly different from what has been observed. Astronomers think the LMC formed in the same environment as the Milky Way and another nearby galaxy, M31, and that it is close to completing a long first orbit around our galaxy (about 13 billion years). Its next orbit will be much shorter due to its interaction with the Milky Way.

"Confirming our theoretical prediction with observational data tells us that our understanding of the interaction between these two galaxies, including the dark matter, is on the right track," said University of Arizona doctoral student in astronomy Nicolás Garavito-Camargo, who led work on the model used in the paper.

The new map also provides astronomers with a rare opportunity to test the properties of the dark matter (the notional water or honey) in our own galaxy. In the new study, Garavito-Camargo and colleagues used a popular dark matter theory called cold dark matter that fits the observed star map relatively well. Now the University of Arizona team is running simulations that use different dark matter theories to see which one best matches the wake observed in the stars.

"It's a really special set of circumstances that came together to create this scenario that lets us test our dark matter theories," said Gurtina Besla, a co-author of the study and an associate professor at the University of Arizona. "But we can only realize that test with the combination of this new map and the dark matter simulations that we built."

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